<figure><img src="image_371.png" /><figcaption>Figure 1: Examples of defects in 3D printed parts</figcaption></figure>

At the moment the only possible solutions to qualify printed parts often involves the implementation of post-processing quality control systems either destructive or non-destructive, that are always very expensive. Otherwise, we can opt for visual inspection alone, which is more cost-effective but lacks a quantitative assessment of the defect and cannot penetrate the core of the part. As examples of non-destructive methods, laser scanning systems that can be utilized to perform detailed dimensional inspections and identify deviations from the intended geometry, X-ray and CT scanning methods to detect internal defects and assess the structural integrity of printed parts. Destructive testing methods includes tensile, compression, fatigue...

As a consequence, considerable research has been undertaken to grasp the critical parameters impacting the quality of printed parts. Simultaneously, efforts have been directed towards identifying defects in real-time or during post-processing through the application of image processing algorithms or the utilization of deep learning models. Various configurations have been tested, employing optical or thermal cameras positioned alongside printers to achieve a comprehensive perspective of parts, demonstrating positive outcomes in terms of quality control.

Single camera approach like camera coupled with computer vision techniques [2-5] or deep learning models [3,6-8] and fixed on the side view, can detect various macro extrusion AM defects (Blobs, Under-extrusion...), but this solution have its limitation since the printer head could obscure the view which make it difficult to always view the material as it is being deposited from the nozzle.

Methods that uses algorithms for change detection or Siamese network [9] to calculate the similarity or the difference between a reference image and the real time image, can give high accurate results in addition to localize the defect. However, techniques related to similarity score, need one correct printed reference at least. This may be especially limiting for custom parts.

Techniques like multi-camera 3D structured light scanning and digital image correlation offer the potential to compare 3D reconstructions of printed parts with digital models to identify dimensional inaccuracies. It offers high accuracy measurement but it is applied either on finished parts or layer by layer so it requires pausing the printing [10,11].

Within the context of this paper, we present a comprehensive monitoring system that emphasizes the critical role of thermal measurement in the 3D printing process. Our approach extends beyond thermal monitoring by incorporating a sophisticated deep learning model, specifically trained to detect defects in real-time during the printing process. The goal of this monitoring system is to generate a digital shadow of the printed components, facilitating the inspection during the post-processing phase. Furthermore, the system can be integrated seamlessly in close-loop mode, facilitating continuous adjustments and optimizations during the printing process, thereby enhancing the overall quality and reliability of the final printed components.

This integrated approach positions our monitoring system as a robust solution for advancing the precision and quality assurance aspects of 3D printing technology.
# Experimental set-up and methods

## *Thermal monitoring solution*

Our patented thermal monitoring system [12] comprises two thermal cameras securely attached to the printing tool, enabling a complete 360° view around the nozzle as shown in the Figure 2. The captured frames from these cameras are then transmitted to a processing unit, where they are seamlessly stitched together to create a cohesive and unique output image that encapsulates the entire thermal landscape.

Additionally, the system measures and records the temperature only from the pixels located on the filament being deposited, storing this information in a file that includes a spatial point cloud of the printed part, with time-temperature evolution for each point.

The system has also the capability to measure the temperature of the area where we intend to deposit the new material which is important to ensure a good adhesion quality of the part.

<figure><img src="image_366.png" /><figcaption>Figure 2: Schematic representation of the thermal monitoring system</figcaption></figure>

## *System configuration*

The innovative thermal monitoring system [12], engineered by COGIT COMPOSITES, is a sophisticated assembly featuring two high-performance thermal cameras. Operating seamlessly at an impressive 80 frames per second (FPS), these cameras are strategically mounted at a carefully determined angle around the printing tool. This strategic placement ensures a comprehensive coverage of thermal data throughout the printing process, these cameras generate a final stitched image with a resolution of approximately 528×370 pixels and ≈0.2 mm resolution in the printing plane.

The camera resolution is adapted to the filament width. In this study, the printed filaments measure 0.7 mm in width, allowing for at least 3 pixels per filament. This resolution is necessary to ensure the selection of the pixel at the centre of the filament. In the case of smaller filaments, a higher camera resolution is required. Similarly, for the printing of wider filaments, such resolution is not necessary.

To ensure the proper functioning of the thermal monitoring system, a series of calibration steps must be undertaken, encompassing the estimation of internal camera parameters (distortion coefficients and focal length....).

A Hand-Eye calibration also is performed to estimate the transformation between the cameras and the machine reference. To streamline this calibration process, a sophisticated procedure has
# Experimental set-up and methods

## *Thermal monitoring solution*

Our patented thermal monitoring system [12] comprises two thermal cameras securely attached to the printing tool, enabling a complete 360° view around the nozzle as shown in the Figure 2. The captured frames from these cameras are then transmitted to a processing unit, where they are seamlessly stitched together to create a cohesive and unique output image that encapsulates the entire thermal landscape.

Additionally, the system measures and records the temperature only from the pixels located on the filament being deposited, storing this information in a file that includes a spatial point cloud of the printed part, with time-temperature evolution for each point.

The system has also the capability to measure the temperature of the area where we intend to deposit the new material which is important to ensure a good adhesion quality of the part.

<figure><img src="image_372.png" /><figcaption>Figure 2: Schematic representation of the thermal monitoring system</figcaption></figure>

## *System configuration*

The innovative thermal monitoring system [12], engineered by COGIT COMPOSITES, is a sophisticated assembly featuring two high-performance thermal cameras. Operating seamlessly at an impressive 80 frames per second (FPS), these cameras are strategically mounted at a carefully determined angle around the printing tool. This strategic placement ensures a comprehensive coverage of thermal data throughout the printing process, these cameras generate a final stitched image with a resolution of approximately 528×370 pixels and ≈0.2 mm resolution in the printing plane.

The camera resolution is adapted to the filament width. In this study, the printed filaments measure 0.7 mm in width, allowing for at least 3 pixels per filament. This resolution is necessary to ensure the selection of the pixel at the centre of the filament. In the case of smaller filaments, a higher camera resolution is required. Similarly, for the printing of wider filaments, such resolution is not necessary.

To ensure the proper functioning of the thermal monitoring system, a series of calibration steps must be undertaken, encompassing the estimation of internal camera parameters (distortion coefficients and focal length....).

A Hand-Eye calibration also is performed to estimate the transformation between the cameras and the machine reference. To streamline this calibration process, a sophisticated procedure has
been designed to serve the specific requirements of the system and overcoming the constraint of the Cartesian machine, also a specific calibration object was adapted for thermal vision.

To initiate the monitoring system, it is necessary to previously transmit the G-code of the part needs to be printed to the processing unit. In addition to the two thermal cameras, the real-time position of the tool must be provided to the system. Whenever the printing starts, the system starts scanning the thermal evolution of the filament being deposited and generating the 5D data, as illustrated in the diagram below Figure 3.

<figure><img src="image_367.png" /><figcaption>Figure 3 : Thermal monitoring system functional diagram</figcaption></figure>

### Data output

As previously described, the system is capable of monitoring the real-time temperature before and after filament deposition, as shown in Figure 4.a. The red dots represent pixels measured following filament deposition, while the white dots represent pixels measured for the subsequent filament deposition. Those measured points are then represented in the Figure 4.b in another shape for easy understanding where the temperature is function of the distance from the extruder. In this new representation, the red curve corresponds to the red dots and the blue curve corresponds to the white dots.

Additionally, all collected data is systematically recorded in an HDF5 file, organized into various datasets.

* Dataset “Time”: It’s a 1D array of values corresponding to every instant $t$ of the collected data.

* Dataset “Position and Temperature”: It’s a 2D array, every row contains a list of $[X, Y, Z, T°C]$ points measured at the instant $t$.

* Dataset “Real Time Position”: It’s a 1D array of tool positions, every row contains the position collected at the instant $t$.

* Dataset “Dataset Length”: It’s a 1D array of values, every row indicates the number of points that has been measured at the instant $t$ (Note: this dataset is added for convenience because there are empty cells in the dataset “Position and Temperature”, due to variance in the number of points measured at every instant $t$).

The Figure 4.c provides a clear representation of the file structure.
been designed to serve the specific requirements of the system and overcoming the constraint of
the Cartesian machine, also a specific calibration object was adapted for thermal vision.

To initiate the monitoring system, it is necessary to previously transmit the G-code of the part needs to be printed to the processing unit. In addition to the two thermal cameras, the real-time position of the tool must be provided to the system. Whenever the printing starts, the system starts scanning the thermal evolution of the filament being deposited and generating the 5D data, as illustrated in the diagram below Figure 3.

<figure><img src="image_373.png" /><figcaption>Figure 3 : Thermal monitoring system functional diagram</figcaption></figure>

Data output

As previously described, the system is capable of monitoring the real-time temperature before and after filament deposition, as shown in Figure 4.a. The red dots represent pixels measured following filament deposition, while the white dots represent pixels measured for the subsequent filament deposition. Those measured points are then represented in the Figure 4.b in another shape for easy understanding where the temperature is function of the distance from the extruder. In this new representation, the red curve corresponds to the red dots and the blue curve corresponds to the white dots.

Additionally, all collected data is systematically recorded in an HDF5 file, organized into
various datasets.

* Dataset “Time”: It’s a 1D array of values corresponding to every instant *t* of the collected data.

* Dataset “Position and Temperature”: It’s a 2D array, every row contains a list of [X, Y, Z, T°C] points measured at the instant *t*.

* Dataset “Real Time Position”: It’s a 1D array of tool positions, every row contain the position collected at the instant *t*.

* Dataset “Dataset Length”: It’s a 1D array of values, every row indicates the number of points that has been measured at the instant *t* (Note: this dataset is added for convenience because there are empty cells in the dataset “Position and Temperature”, due to variance in the number of points measured at every instant *t*).

The Figure 4.c provides a clear representation of the file structure.
Step 1 : Thermal images capturing and stitching

<figure><img src="image_368.png" /></figure>

Step 2 : Points of interest measurement

<figure><img src="image_369.png" /></figure>

Step 3 : Storing 5D data in HDF5 file

<figure><img src="image_370.png" /><figcaption>Figure 4 : Thermal measurement, points after deposition (red dots) and before deposition (white dots) (a). Temperature representation of the measured points in function of the distance to extruder (b). Stored HDF5 file structure (c)</figcaption></figure>

For the purpose of visually exploring the thermal history at different time, a dedicated script has been developed to replay the content stored within the generated HDF5 file. This representation allows obtaining a digital shadow that can be used to detect defects or validate the thermal history of the parts. The provided Figure 5 serves as an example of the output digital shadow that is presented at an instant t.
Step 1 : Thermal images capturing and stitching

<figure><img src="image_374.png" /></figure>

Step 2 : Points of interest measurement

<figure><img src="image_375.png" /></figure>

Step 3 : Storing 5D data in HDF5 file

<figure><img src="image_376.png" /><figcaption>Figure 4 : Thermal measurement, points after deposition (red dots) and before deposition (white dots) (a). Temperature representation of the measured points in function of the distance to extruder (b). Stored HDF5 file structure (c)</figcaption></figure>

For the purpose of visually exploring the thermal history at different time, a dedicated script has been developed to replay the content stored within the generated HDF5 file. This representation allows obtaining a digital shadow that can be used to detect defects or validate the thermal history of the parts. The provided Figure 5 serves as an example of the output digital shadow that is presented at an instant t.
<figure><img src="image_371.png" /><figcaption>Figure 5: Sample representation of digital shadow</figcaption></figure>

## Defect detection

As mentioned before, the capability of the monitoring system is expanded through the development of a deep learning model with the ability to detect defects in real-time. It is assumed that distinguishable features in thermal images captured during 3D printing can be utilized to identify various types of defects. This assumption is based on the understanding that patterns associated with each defect in thermal imaging may be recognized due to heat distribution, speed, flow rate, and movement during the printing process.

As a result, an object detection model can achieve real-time and accurate detection and localization of defects, enabling the implementation of subsequent correction system.

## Data collection - Training

The dataset comprises thermal images captured from videos recorded during the printing process. To introduce defects, intentional modifications to printing parameters were made, either in real-time or by generating a modified G-Code using a python script.

Subsequently, these videos have been processed, and images were extracted at a rate of 10 frames per second to reduce redundancy. Before training, data augmentation techniques were applied to enhance the dataset. Existing methods from Albumentations [13] have been employed and two additional techniques have been developed: Custom Mosaic and Custom Cut Mix as shown in Figure 6.

Custom Mosaic involves generating an image by combining four distinct parts from different random images, each containing defects. This approach allows the reduction of batch size effectively.

In the context of Custom Cut Mix, the scenario presented a challenge with the original Cut Mix augmentation technique, given that the majority of objects were centred in each image, occupying various areas and sizes. The risk of cutting an empty portion from one image and pasting it onto an object, potentially obscuring it, was high. The implemented variation ensures that when cutting a random object from one image and pasting it onto another, the intersection between objects does not exceed 0.75, thereby preserving the integrity of the original object.
<figure><img src="image_377.png" /><figcaption>Figure 5: Sample representation of digital shadow</figcaption></figure>

## Defect detection

As mentioned before, the capability of the monitoring system is expanded through the development of a deep learning model with the ability to detect defects in real-time. It is assumed that distinguishable features in thermal images captured during 3D printing can be utilized to identify various types of defects. This assumption is based on the understanding that patterns associated with each defect in thermal imaging may be recognized due to heat distribution, speed, flow rate, and movement during the printing process.

As a result, an object detection model can achieve real-time and accurate detection and localization of defects, enabling the implementation of subsequent correction system.

## Data collection - Training

The dataset comprises thermal images captured from videos recorded during the printing process. To introduce defects, intentional modifications to printing parameters were made, either in real-time or by generating a modified G-Code using a python script.

Subsequently, these videos have been processed, and images were extracted at a rate of 10 frames per second to reduce redundancy. Before training, data augmentation techniques were applied to enhance the dataset. Existing methods from Albumentations [13] have been employed and two additional techniques have been developed: Custom Mosaic and Custom Cut Mix as shown in Figure 6.

Custom Mosaic involves generating an image by combining four distinct parts from different random images, each containing defects. This approach allows the reduction of batch size effectively.

In the context of Custom Cut Mix, the scenario presented a challenge with the original Cut Mix augmentation technique, given that the majority of objects were centred in each image, occupying various areas and sizes. The risk of cutting an empty portion from one image and pasting it onto an object, potentially obscuring it, was high. The implemented variation ensures that when cutting a random object from one image and pasting it onto another, the intersection between objects does not exceed 0.75, thereby preserving the integrity of the original object.
<figure><img src="image_372.png" /><figcaption>Figure 6: Output of modified Cut Mix and Mosaic methods</figcaption></figure>

The images have been categorized into seven distinct classes (No printing, Non-stick filament, Stringing, Gaps, Inconsistent extrusion, Blobs, and Hot spot). Examples of each defect in thermal images can be found in Figure 7.a (The different classes have been chosen after doing tests and analyses on the thermal images in order to avoid model confusion) and the total number of images per class is presented in Figure 7.b.

<figure><img src="image_373.png" /><figcaption>Figure 7: Example of defects in thermal images (a), Number of images per class used for training (b)</figcaption></figure>

For training, an NVIDIA RTX A2000 GPU was used. A subset of 10% of the images (images with no defects) were retained to reduce the False Positives (FP), then dataset was divided into 70% for training, 20% for validation and 10% for testing.

## Validation

Following extensive testing, the model hyper parameters have been fine-tuned and optimal features for annotation have been identified.

The ultimate model configuration yields a mAP50 of 0.91 and mAP50-95 of 0.72, as shown in the Figure 8. Additionally, the model achieved an inference speed of 5.1 ms per image on the validation dataset.

The confusion matrix shown in Figure 9 indicates favourable results across all classes, with only a slight confusion observed between hot spots and blobs. This confusion is expected, given the similar appearance of these defects in thermal images.
<figure><img src="image_378.png" /><figcaption>Figure 6: Output of modified Cut Mix and Mosaic methods</figcaption></figure>

The images have been categorized into seven distinct classes (No printing, Non-stick filament, Stringing, Gaps, Inconsistent extrusion, Blobs, and Hot spot). Examples of each defect in thermal images can be found in Figure 7.a (The different classes have been chosen after doing tests and analyses on the thermal images in order to avoid model confusion) and the total number of images per class is presented in Figure 7.b.

<figure><img src="image_379.png" /><figcaption>Figure 7: Example of defects in thermal images (a), Number of images per class used for training (b)</figcaption></figure>

For training, an NVIDIA RTX A2000 GPU was used. A subset of 10% of the images (images with no defects) were retained to reduce the False Positives (FP), then dataset was divided into 70% for training, 20% for validation and 10% for testing.

## Validation

Following extensive testing, the model hyper parameters have been fine-tuned and optimal features for annotation have been identified.

The ultimate model configuration yields a mAP50 of 0.91 and mAP50-95 of 0.72, as shown in the Figure 8. Additionally, the model achieved an inference speed of 5.1 ms per image on the validation dataset.

The confusion matrix shown in Figure 9 indicates favourable results across all classes, with only a slight confusion observed between hot spots and blobs. This confusion is expected, given the similar appearance of these defects in thermal images.
<figure><img src="image_374.png" /><figcaption>Figure 8: Model evaluation curves</figcaption></figure>

<figure><img src="image_375.png" /><figcaption>Figure 9: Model confusion matrix</figcaption></figure>

## Results and discussion

Despite the small number of images used for training, the final model successfully identified over 90% of the defects on test videos as shown in Figure 10. Also the usage of the mosaic method resulted in a notable reduction in the required number of epochs, halving the training time while maintaining high accuracy. Furthermore, the use of MixUp augmentation with rotation and flipping increased the mAP50 by 3.2% while increased the mAP50-95 by 1.7%.

Although thermal imaging has proven effective in detecting seven types of defects, there are still some cases where certain defects remain undetected. This highlights the importance of incorporating additional information, such as speed, flow rate, temperature evolution over time of the printed filament... One approach to address this is using multi-modal learning.

An alternative approach is to combine optical imaging and thermal imaging, expanding the range of accessible features. This fusion not only increases detection precision but also facilitates the identification of additional defects.
<figure><img src="image_380.png" /><figcaption>Figure 8: Model evaluation curves</figcaption></figure>

<figure><img src="image_381.png" /><figcaption>Figure 9: Model confusion matrix</figcaption></figure>

## Results and discussion

Despite the small number of images used for training, the final model successfully identified over 90% of the defects on test videos as shown in Figure 10. Also the usage of the mosaic method resulted in a notable reduction in the required number of epochs, halving the training time while maintaining high accuracy. Furthermore, the use of MixUp augmentation with rotation and flipping increased the mAP50 by 3.2% while increased the mAP50-95 by 1.7%.

Although thermal imaging has proven effective in detecting seven types of defects, there are still some cases where certain defects remain undetected. This highlights the importance of incorporating additional information, such as speed, flow rate, temperature evolution over time of the printed filament... One approach to address this is using multi-modal learning.

An alternative approach is to combine optical imaging and thermal imaging, expanding the range of accessible features. This fusion not only increases detection precision but also facilitates the identification of additional defects.
<figure><img src="image_376.png" /><figcaption>Figure 10: Examples of detected defects using the final model</figcaption></figure>

## Conclusions

In conclusion, we showed an in-situ thermal monitoring system capable of supervising the thermal evolution of the printed filament that also generates a digital shadow of the printed part.

Additionally, a deep learning model trained to detect defects achieved high accuracy in detecting and localizing 7 types of defects. Future work for improving the system involves detecting other types of defects by adding additional information for multi-modal learning and also using the system in close loop-mode in order to apply correction to the printing.

## References

[1] Print Quality Guide n.d. https://www.simplify3d.com/resources/print-quality-troubleshooting/

[2] Baumann F, Roller D. Vision based error detection for 3D printing processes. MATEC Web Conf 2016;59:06003. https://doi.org/10.1051/matecconf/20165906003

[3] Brion DAJ, Pattinson SW. Generalisable 3D printing error detection and correction via multi-head neural networks. Nat Commun 2022;13:4654. https://doi.org/10.1038/s41467-022-31985-y

[4] Tian X, Li Y, Ma D, Han J, Xia L. Strand width uniformly control for silicone extrusion additive manufacturing based on image processing. Int J Adv Manuf Technol 2022;119:3077-90. https://doi.org/10.1007/s00170-021-08370-y

[5] Huang T, Wang S, Yang S, Dai W. Statistical process monitoring in a specified period for the image data of fused deposition modeling parts with consistent layers. J Intell Manuf 2021;32:2181-96. https://doi.org/10.1007/s10845-020-01628-4

[6] Jin Z, Zhang Z, Gu GX. Automated Real-Time Detection and Prediction of Interlayer Imperfections in Additive Manufacturing Processes Using Artificial Intelligence. Adv Intell Syst 2020;2:1900130. https://doi.org/10.1002/aisy.201900130

[7] Zhang Z, Fidan I, Allen M. Detection of Material Extrusion In-Process Failures via Deep Learning. Inventions 2020;5:25. https://doi.org/10.3390/inventions5030025

[8] Jin Z, Zhang Z, Gu GX. Autonomous in-situ correction of fused deposition modeling printers using computer vision and deep learning. Manuf Lett 2019;22:11-5. https://doi.org/10.1016/j.mfglet.2019.09.005

[9] Niu Y, Chadwick E, Ma AWK, Yang Q. Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing 2022. https://doi.org/10.48550/arXiv.2212.08583

[10] Charalampous P, Kostavelis I, Kopsacheilis C, Tzovaras D. Vision-based real-time monitoring of extrusion additive manufacturing processes for automatic manufacturing error detection. Int J Adv Manuf Technol 2021;115:3859-72. https://doi.org/10.1007/s00170-021-07419-2
<figure><img src="image_382.png" /><figcaption>Figure 10: Examples of detected defects using the final model</figcaption></figure>

## Conclusions

In conclusion, we showed an in-situ thermal monitoring system capable of supervising the thermal evolution of the printed filament that also generates a digital shadow of the printed part.

Additionally, a deep learning model trained to detect defects achieved high accuracy in detecting and localizing 7 types of defects. Future work for improving the system involves detecting other types of defects by adding additional information for multi-modal learning and also using the system in close loop-mode in order to apply correction to the printing.

## References

[1] Print Quality Guide n.d. https://www.simplify3d.com/resources/print-quality-troubleshooting/

[2] Baumann F, Roller D. Vision based error detection for 3D printing processes. MATEC Web Conf 2016;59:06003. https://doi.org/10.1051/matecconf/20165906003

[3] Brion DAJ, Pattinson SW. Generalisable 3D printing error detection and correction via multi-head neural networks. Nat Commun 2022;13:4654. https://doi.org/10.1038/s41467-022-31985-y

[4] Tian X, Li Y, Ma D, Han J, Xia L. Strand width uniformly control for silicone extrusion additive manufacturing based on image processing. Int J Adv Manuf Technol 2022;119:3077-90. https://doi.org/10.1007/s00170-021-08370-y

[5] Huang T, Wang S, Yang S, Dai W. Statistical process monitoring in a specified period for the image data of fused deposition modeling parts with consistent layers. J Intell Manuf 2021;32:2181-96. https://doi.org/10.1007/s10845-020-01628-4

[6] Jin Z, Zhang Z, Gu GX. Automated Real-Time Detection and Prediction of Interlayer Imperfections in Additive Manufacturing Processes Using Artificial Intelligence. Adv Intell Syst 2020;2:1900130. https://doi.org/10.1002/aisy.201900130

[7] Zhang Z, Fidan I, Allen M. Detection of Material Extrusion In-Process Failures via Deep Learning. Inventions 2020;5:25. https://doi.org/10.3390/inventions5030025

[8] Jin Z, Zhang Z, Gu GX. Autonomous in-situ correction of fused deposition modeling printers using computer vision and deep learning. Manuf Lett 2019;22:11-5. https://doi.org/10.1016/j.mfglet.2019.09.005

[9] Niu Y, Chadwick E, Ma AWK, Yang Q. Semi-Siamese Network for Robust Change Detection Across Different Domains with Applications to 3D Printing 2022. https://doi.org/10.48550/arXiv.2212.08583

[10] Charalampous P, Kostavelis I, Kopsacheilis C, Tzovaras D. Vision-based real-time monitoring of extrusion additive manufacturing processes for automatic manufacturing error detection. Int J Adv Manuf Technol 2021;115:3859-72. https://doi.org/10.1007/s00170-021-07419-2
[11] Holzmond O, Li X. In situ real time defect detection of 3D printed parts. Addit Manuf 2017;17:135-42. https://doi.org/10.1016/j.addma.2017.08.003

[12] ROUA C. Fused filament additive manufacturing installation comprising an extrusion nozzle and a device for measuring and thermally controlling the method. WO2023017218, n.d.

[13] Albumentations Documentation 2023. https://albumentations.ai/docs/ (accessed May 25, 2023).
[11] Holzmond O, Li X. In situ real time defect detection of 3D printed parts. Addit Manuf 2017;17:135-42. https://doi.org/10.1016/j.addma.2017.08.003

[12] ROUA C. Fused filament additive manufacturing installation comprising an extrusion nozzle and a device for measuring and thermally controlling the method. WO2023017218, n.d.

[13] Albumentations Documentation 2023. https://albumentations.ai/docs/ (accessed May 25, 2023).
# Experimental and computational analysis of 3D printed 2D lattices

MUSSINI Andrea¹, CARTER Luke², VILLAPUN Victor², CAO Emily², COX Sophie² and GINESTRA Paola¹,ᵃ*

¹Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38 25123 Brescia, Italy

²School of Chemical Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, B15 2TT, United Kingdom

ᵃpaola.ginestra@unibs.it

**Keywords:** Lattice Structures, Metamaterials, Mechanical Simulations

**Abstract.** Additive Manufacturing can offer different solutions in terms of bespoke implants and deformable structures tailorable by the geometry design. The prototyping phase consists in a preliminary evaluation of the mechanical performances to find a relation with the geometrical parameters. For these reasons, numerical tools that can relate directly the geometrical features, especially for lattice or cellular structures, to the mechanical properties, are often studied to speed up the design and production process. In this study, the predictability of the deformation of a structure composed by honeycomb unit cells was studied performing compression simulations. Successively, the structure was 3D printed using fused filament fabrication and tested using a positioning stage, the relative displacements of 36 specific points of the structure were extracted using contactless measurement techniques and compared with the simulations. The results demonstrated a good predictability of the model in relation to the deformation of the structure with a stable relationship between the geometry selected and the final mechanical properties.

**Introduction**

Additive Manufacturing (AM) is a process used to produce highly complex parts that would otherwise require expensive processing. Because of the ability to produce complex geometric parts starting from medical imaging data and the possibility to use biocompatible materials, AM is rapidly becoming one of the most studied techniques for orthopaedic applications [1] especially cranial plates [2]. The main advantage of AM is the possibility to tune the structure depending on the patient specific needs, geometry and pathology [3].

Metamaterials are being extensively studied due to their capacity to defy the limitations of traditional materials, offering tailored and exceptional mechanical properties. These engineered materials are designed at a smaller scale, granting the ability to exhibit characteristics not commonly found in natural substances. By manipulating their structures and compositions, metamaterials can display properties like negative Poisson's ratios, negative mass density, or properties that can be finely tuned. Their response to stress and strain is unconventional, providing opportunities for applications in vibration damping, impact resistance, and even cloaking of mechanical waves. Moreover, these materials can be incredibly lightweight yet possess remarkable mechanical strength, offering valuable solutions in weight-sensitive industries like aerospace and automotive sectors. Metamaterials can achieve extreme mechanical properties, surpassing those found in conventional materials, and their structural flexibility and adaptability allow for tailoring responses to varying conditions. These unique mechanical characteristics open doors to novel applications, from next-generation mechanical components to advanced robotics and innovative medical devices, where custom-designed mechanical behavior is crucial.

<figure><img src="image_377.png" /></figure>

Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Published under license by Materials Research Forum LLC.
# Experimental and computational analysis of 3D printed 2D lattices

MUSSINI Andrea¹, CARTER Luke², VILLAPUN Victor², CAO Emily², COX Sophie² and GINESTRA Paola¹,ᵃ*

¹Department of Mechanical and Industrial Engineering, University of Brescia, Via Branze, 38 25123 Brescia, Italy

²School of Chemical Engineering, College of Engineering and Physical Sciences, University of Birmingham, Edgbaston, B15 2TT, United Kingdom

ᵃpaola.ginestra@unibs.it

**Keywords:** Lattice Structures, Metamaterials, Mechanical Simulations

**Abstract.** Additive Manufacturing can offer different solutions in terms of bespoke implants and deformable structures tailorable by the geometry design. The prototyping phase consists in a preliminary evaluation of the mechanical performances to find a relation with the geometrical parameters. For these reasons, numerical tools that can relate directly the geometrical features, especially for lattice or cellular structures, to the mechanical properties, are often studied to speed up the design and production process. In this study, the predictability of the deformation of a structure composed by honeycomb unit cells was studied performing compression simulations. Successively, the structure was 3D printed using fused filament fabrication and tested using a positioning stage, the relative displacements of 36 specific points of the structure were extracted using contactless measurement techniques and compared with the simulations. The results demonstrated a good predictability of the model in relation to the deformation of the structure with a stable relationship between the geometry selected and the final mechanical properties.

**Introduction**

Additive Manufacturing (AM) is a process used to produce highly complex parts that would otherwise require expensive processing. Because of the ability to produce complex geometric parts starting from medical imaging data and the possibility to use biocompatible materials, AM is rapidly becoming one of the most studied techniques for orthopaedic applications [1] especially cranial plates [2]. The main advantage of AM is the possibility to tune the structure depending on the patient specific needs, geometry and pathology [3].

Metamaterials are being extensively studied due to their capacity to defy the limitations of traditional materials, offering tailored and exceptional mechanical properties. These engineered materials are designed at a smaller scale, granting the ability to exhibit characteristics not commonly found in natural substances. By manipulating their structures and compositions, metamaterials can display properties like negative Poisson's ratios, negative mass density, or properties that can be finely tuned. Their response to stress and strain is unconventional, providing opportunities for applications in vibration damping, impact resistance, and even cloaking of mechanical waves. Moreover, these materials can be incredibly lightweight yet possess remarkable mechanical strength, offering valuable solutions in weight-sensitive industries like aerospace and automotive sectors. Metamaterials can achieve extreme mechanical properties, surpassing those found in conventional materials, and their structural flexibility and adaptability allow for tailoring responses to varying conditions. These unique mechanical characteristics open doors to novel applications, from next-generation mechanical components to advanced robotics and innovative medical devices, where custom-designed mechanical behavior is crucial.

<figure><img src="image_383.png" /></figure>
In the biomedical context, metamaterials have also played an important role in the development of structures with advanced functionalities [4,5], these materials are able to obtain specific mechanical (Young modulus, Poisson's ratio) and biological properties with a smart design of the small-scale architecture of the system. This smart use of the design at the unit cell level can also be used to improve implant fixation [6]. Numerical modelling of mechanical tests involves computational methods to simulate and analyse how materials respond to compression forces. It's a vital aspect of understanding material behaviour and predicting mechanical properties without solely relying on physical testing.

Finite Element Analysis (FEA) is a primary technique used in this modelling. It involves breaking down complex structures or materials into smaller, manageable elements to simulate their response to compression. This method predicts stress, strain, and deformation. Defining accurate boundary conditions is essential for simulation accuracy. This includes specifying the type of compression load applied to the material, whether it's uniaxial, confined, or any other form of loading. One key aspect is the validation and calibration of models against real experimental data [7].

This ensures that the simulations accurately represent the behaviour of the material under compression. Various software tools are employed for creating models, applying loads, defining material properties, and analysing results. These tools offer a platform for conducting detailed simulations and each tool is different in terms of computational costs and accuracy of the model. While numerical modelling offers cost and time-saving benefits, there are challenges. Accurately characterizing materials and selecting appropriate models, validating simulations against real-world data, and managing computational costs for complex models are key among these challenges.

In this paper, a 2D lattice honeycomb structure was designed in Solidworks (version 2021, ©2002-2023 Dassault Systèmes) varying general starting parameters to accommodate the chosen manufacturing process. Compression simulations were performed using Solidworks and the data regarding the displacement in the XY field of 36 specific points were extracted. The structure was then 3D printed and tested. The compression of the structure was recorded using a camera at 24 FPS the deformation of thirty six specific points in the structure was tracked. The tracked displacement of the points was compared with the results obtained in the simulations and the Poisson's ratio of the structure was calculated.

<figure><img src="image_378.png" /><figcaption>Figure 1. Tested structure and parameters definition.</figcaption></figure>

The predictability of the deformation of the lattice sample in Fig 1, produced through fused filament fabrication (FFF) of polylactic Acid (PLA), has been analysed using a contactless data extraction method to obtain the relative displacement of specific points of the structure.
In the biomedical context, metamaterials have also played an important role in the development of structures with advanced functionalities [4,5], these materials are able to obtain specific mechanical (Young modulus, Poisson's ratio) and biological properties with a smart design of the small-scale architecture of the system. This smart use of the design at the unit cell level can also be used to improve implant fixation [6]. Numerical modelling of mechanical tests involves computational methods to simulate and analyse how materials respond to compression forces. It's a vital aspect of understanding material behaviour and predicting mechanical properties without solely relying on physical testing.

Finite Element Analysis (FEA) is a primary technique used in this modelling. It involves breaking down complex structures or materials into smaller, manageable elements to simulate their response to compression. This method predicts stress, strain, and deformation. Defining accurate boundary conditions is essential for simulation accuracy. This includes specifying the type of compression load applied to the material, whether it's uniaxial, confined, or any other form of loading. One key aspect is the validation and calibration of models against real experimental data [7].

This ensures that the simulations accurately represent the behaviour of the material under compression. Various software tools are employed for creating models, applying loads, defining material properties, and analysing results. These tools offer a platform for conducting detailed simulations and each tool is different in terms of computational costs and accuracy of the model. While numerical modelling offers cost and time-saving benefits, there are challenges. Accurately characterizing materials and selecting appropriate models, validating simulations against real-world data, and managing computational costs for complex models are key among these challenges.

In this paper, a 2D lattice honeycomb structure was designed in Solidworks (version 2021, ©2002-2023 Dassault Systèmes) varying general starting parameters to accommodate the chosen manufacturing process. Compression simulations were performed using Solidworks and the data regarding the displacement in the XY field of 36 specific points were extracted. The structure was then 3D printed and tested. The compression of the structure was recorded using a camera at 24 FPS the deformation of thirty six specific points in the structure was tracked. The tracked displacement of the points was compared with the results obtained in the simulations and the Poisson's ratio of the structure was calculated.

<figure><img src="image_384.png" /><figcaption>Figure 1. Tested structure and parameters definition.</figcaption></figure>

The predictability of the deformation of the lattice sample in Fig 1, produced through fused filament fabrication (FFF) of polylactic Acid (PLA), has been analysed using a contactless data extraction method to obtain the relative displacement of specific points of the structure.
## Materials and Methods

In order to be able to study the deformation of the honeycomb net structure, and assess its predictability, a compression simulation was first conducted (Fig. 2 a and b). Then, three replicas of the structure were 3D printed and physically tested, to validate the computational simulations with the acquired displacement of the trigger points (Fig. 2 c and d). Two different type of validation tests were made, one to test the behaviour of the structure under multiple consecutive compressions and assess the repeatability of the experiment, and the second to test if any differences were present between two identical nets to assess the building process reliability and repeatability.

For the experimental tests, the extremities of the structure were fully locked for two reasons: the first is that even though the calculation of the Poisson's ratio usually requires free edges, we were not able to obtain a compression system that allowed for free and frictionless movement of the edges in the Y direction (Fig. 2 c), the second reason was that these locked edges tests are more similar to the envisioned use of the device, where the structure would be ideally attached to the patient's skull. Moreover, two different angle $α$, defined as $arctan(v1/v0)$, were tested to define a parametrical system.

Simulation of the compression tests. The starting parameters of the base unit cell were: $v0 = v2 = v3 = 30$ mm; $v1 = 3.75$ mm; $v4 = 15$ mm with a wall thickness of 0.6 mm and a height of 4 mm (Fig. 1 and Fig. 2a). These parameters were chosen as they could reliably be manufactured to enable a direct comparison between simulations and experiments. Simulations were conducted in Solidworks (version 2021, ©2002 - 2023 Dassault Systèmes) where custom material properties were inserted based on the PLA filament datasheet (Prusa) and literature [8]. The Von Mises-plasticity model was employed. The left side of the structure was then fully constrained and a displacement of -6mm was imposed on the right side in the X direction (Fig. 2b) as a boundary condition. The XY displacements of 36 points positioned at the structural nodes were extracted and subsequently compared to the experimental results.

<figure><img src="image_379.png" /><figcaption>Figure 2. Flow chart of the work performed, with some deepenings on the position of constraints in the simulation, the experimental setup and data extraction and some other structures.</figcaption></figure>

Manufacturing of the structure via FFF. The structures were produced using a FFF modelling system (MK3, Prusa© Prusa Research a.s.) equipped with a 0.4mm nozzle. Each structure was designed in Solidworks (version 2021), exported in .stl files and imported in the slicing software PrusaSlicer (version 2.4.2) to generate the Gcode, the filament used was the Prusament PLA Prusa
## Materials and Methods

In order to be able to study the deformation of the honeycomb net structure, and assess its predictability, a compression simulation was first conducted (Fig. 2 a and b). Then, three replicas of the structure were 3D printed and physically tested, to validate the computational simulations with the acquired displacement of the trigger points (Fig. 2 c and d). Two different type of validation tests were made, one to test the behaviour of the structure under multiple consecutive compressions and assess the repeatability of the experiment, and the second to test if any differences were present between two identical nets to assess the building process reliability and repeatability.

For the experimental tests, the extremities of the structure were fully locked for two reasons: the first is that even though the calculation of the Poisson's ratio usually requires free edges, we were not able to obtain a compression system that allowed for free and frictionless movement of the edges in the Y direction (Fig. 2 c), the second reason was that these locked edges tests are more similar to the envisioned use of the device, where the structure would be ideally attached to the patient's skull. Moreover, two different angle $α$, defined as $arctan(v1/v0)$, were tested to define a parametrical system.

Simulation of the compression tests. The starting parameters of the base unit cell were: $v0 = v2 = v3 = 30$ mm; $v1 = 3.75$ mm; $v4 = 15$ mm with a wall thickness of 0.6 mm and a height of 4 mm (Fig. 1 and Fig. 2a). These parameters were chosen as they could reliably be manufactured to enable a direct comparison between simulations and experiments. Simulations were conducted in Solidworks (version 2021, ©2002 - 2023 Dassault Systèmes) where custom material properties were inserted based on the PLA filament datasheet (Prusa) and literature [8]. The Von Mises-plasticity model was employed. The left side of the structure was then fully constrained and a displacement of -6mm was imposed on the right side in the X direction (Fig. 2b) as a boundary condition. The XY displacements of 36 points positioned at the structural nodes were extracted and subsequently compared to the experimental results.

<figure><img src="image_385.png" /><figcaption>Figure 2. Flow chart of the work performed, with some deepenings on the position of constrains in the simulation, the experimental setup and data extraction and some other structures.</figcaption></figure>

Manufacturing of the structure via FFF. The structures were produced using a FFF modelling system (MK3, Prusa© Prusa Research a.s.) equipped with a 0.4mm nozzle. Each structure was designed in Solidworks (version 2021), exported in .stl files and imported in the slicing software PrusaSlicer (version 2.4.2) to generate the Gcode, the filament used was the Prusament PLA Prusa
Galaxy Black filament. An infill of 100% was chosen, the nozzle temperature was 125° and the bed temperature was 60°.

Experimental compression tests. To allow the comparison with the simulations results, the 36 points at the structural nodes (average diameter 2.5 mm) were painted on the structure using white paint (Fig. 2c), and compression tests were performed using a bespoke Velmex XSlide positioning stage powered by an Arcus DMX-A2-DRV-17 engine. The locking mechanism allowed the study of the deformation of the structure by providing two planes that were at the same height in relation to the ground, on top of which the edges were located to assure the stability of the structure during the test. The locking mechanism was also 3D printed (Fig. 2c). The locking mechanism was attached to the positioning stage and the structure was firmly locked in place using pins that did not allow movement in the Y direction, while the locking mechanism itself prevented the free movement in the XZ axis. A compression of 6mm was imposed (resolution of 1 mm/10.000 steps), with an acceleration of 0,03 mm/s2 until a speed of 1 mm/s was reached. The movement of the structure, and the subsequent displacement of the points, were recorded using a HD Philips camera at 24 frames per second held above the positioning stage by a tripod. The recording started 1s before the start of the compression and was stopped 1s after the compression ended. To obtain a clear contrast between the white dots present on the structure and the background, a plastic black mat was positioned under the structure, above the moving thread.

Data extraction and analysis. Data extraction was performed starting from the recording of the compression tests using Fiji (ImageJ) [9] and the track mate plugin [10]. The recording was imported in ImageJ as an image stack with 165 frames containing the compression, at the same time the image stack was cropped to show only the structure. Subsequently, a binary thresholding was performed on the stack to show only the white reference dots (Fig. 2d). Using the Trackmate plugin, the displacement of each of the 36 dots was extracted. The Difference of Gaussians (DoG) detector was used selecting an object diameter of 3 mm and a quality threshold of 6, to allow tracking of the 36 points with no interference. To track the points, the simple linear assignment problem (LAP) tracker was selected, with a linking max distance of 3 mm and a max frame gap of 6. This allowed to track all the 36 points during the movement avoiding possible track loss from the software. The obtained data were exported in a CSV file containing the track number, the frame numbers and the X,Y coordinates of the point for each of the analysed frames. The mean displacement was then calculated for each point, firstly by averaging the relative displacement frame by frame of the three compression tests performed on a structure, and then by calculating the average of all three replicas. Then standard deviation (STD) and the standard error of estimate (σest) were also calculated. The standard error of estimate was found using the following formula:

$$ \sigma_{est} = \sqrt{\frac{\sum (Y-Y')^2}{N}} \qquad (1) $$

## Results

Localized deformation analysis. The location of the points that were painted on the net with white paint visible in Fig. 3. This aspect has a significant effect on the simulation results and the experimental tests replicability. Three regions were identified, where all the points exhibited similar behaviours when compared to the simulations. Points 1-6 (Fig. 3 - blue region) and 31-36 (Fig. 3 - red region) located at the structure edges, demonstrated large variations between the simulations and physical test data (up to 400%). The displacements results of Point 2 and 22 were reported in Fig. 3 as references for the behaviour of the other points of the external columns. Despite the big difference in percentage values between the simulated values and the tested results, the overall relative displacement of these points is usually less than 0.2 mm, thus the error resulting
Galaxy Black filament. An infill of 100% was chosen, the nozzle temperature was 125° and the bed temperature was 60°.

Experimental compression tests. To allow the comparison with the simulations results, the 36 points at the structural nodes (average diameter 2.5 mm) were painted on the structure using white paint (Fig. 2c), and compression tests were performed using a bespoke Velmex XSlide positioning stage powered by an Arcus DMX-A2-DRV-17 engine. The locking mechanism allowed the study of the deformation of the structure by providing two planes that were at the same height in relation to the ground, on top of which the edges were located to assure the stability of the structure during the test. The locking mechanism was also 3D printed (Fig. 2c). The locking mechanism was attached to the positioning stage and the structure was firmly locked in place using pins that did not allow movement in the Y direction, while the locking mechanism itself prevented the free movement in the XZ axis. A compression of 6mm was imposed (resolution of 1 mm/10.000 steps), with an acceleration of 0,03 mm/s2 until a speed of 1 mm/s was reached. The movement of the structure, and the subsequent displacement of the points, were recorded using a HD Philips camera at 24 frames per second held above the positioning stage by a tripod. The recording started 1s before the start of the compression and was stopped 1s after the compression ended. To obtain a clear contrast between the white dots present on the structure and the background, a plastic black mat was positioned under the structure, above the moving thread.

Data extraction and analysis. Data extraction was performed starting from the recording of the compression tests using Fiji (ImageJ) [9] and the track mate plugin [10]. The recording was imported in ImageJ as an image stack with 165 frames containing the compression, at the same time the image stack was cropped to show only the structure. Subsequently, a binary thresholding was performed on the stack to show only the white reference dots (Fig. 2d). Using the Trackmate plugin, the displacement of each of the 36 dots was extracted. The Difference of Gaussians (DoG) detector was used selecting an object diameter of 3 mm and a quality threshold of 6, to allow tracking of the 36 points with no interference. To track the points, the simple linear assignment problem (LAP) tracker was selected, with a linking max distance of 3 mm and a max frame gap of 6. This allowed to track all the 36 points during the movement avoiding possible track loss from the software. The obtained data were exported in a CSV file containing the track number, the frame numbers and the X,Y coordinates of the point for each of the analysed frames. The mean displacement was then calculated for each point, firstly by averaging the relative displacement frame by frame of the three compression tests performed on a structure, and then by calculating the average of all three replicas. Then standard deviation (STD) and the standard error of estimate (σest) were also calculated. The standard error of estimate was found using the following formula:

$$ \sigma_{est} = \sqrt{\frac{\sum (Y-Y')^2}{N}} \qquad (1) $$

## Results

Localized deformation analysis. The location of the points that were painted on the net with white paint visible in Fig. 3. This aspect has a significant effect on the simulation results and the experimental tests replicability. Three regions were identified, where all the points exhibited similar behaviours when compared to the simulations. Points 1-6 (Fig. 3 - blue region) and 31-36 (Fig. 3 - red region) located at the structure edges, demonstrated large variations between the simulations and physical test data (up to 400%). The displacements results of Point 2 and 22 were reported in Fig. 3 as references for the behaviour of the other points of the external columns. Despite the big difference in percentage values between the simulated values and the tested results, the overall relative displacement of these points is usually less than 0.2 mm, thus the error resulting
from these regions should not affect the points in the central yellow region. For these reasons, more emphasis was placed on a comparison between the simulation results and the experimental results of points 7 – 30 that were located further from the edges of the structure, in the central region (Fig. 3 – yellow region).

<figure><img src="image_380.png" /><figcaption>Figure 3. Points comparison between simulations and experiments, the analysis led us to discard points between 1 and 6 and between 31 and 36 because of how unpredictable they were, points from 7 to 30 showed a trend that was closer to the expected one.</figcaption></figure>

Standard error of estimate. For each of the points in the central yellow region (indicated by the yellow box in Fig. 3), the average displacement and standard error of estimate (σest) were calculated (Fig. 4). Among the three repetitions of the structures with an α value of 7.125°, clear trends of points that exhibited the highest and lowest variation were noticed. Specifically, points 7, 18, 19, 23, 24 and 25 had error values between 1 and 1.8 while for the remaining points the dataset were strongly correlated with σest below 0.8. Among the three replicas, Structure 1 (Fig. 4a) is the one with the lowest σest in all points, with most points showing a value under 0.8; structure 2 (Fig. 4b) is the one with the highest σest in all points but with values of σest<1.2 for most of the points; and Structure 3 (Fig. 4b) being much closer to the average of the three structures combined (Fig. 4d). The average value of σest of the three structures combined is 0.687.
from these regions should not affect the points in the central yellow region. For these reasons, more emphasis was placed on a comparison between the simulation results and the experimental results of points 7 – 30 that were located further from the edges of the structure, in the central region (Fig. 3 – yellow region).

<figure><img src="image_386.png" /><figcaption>Figure 3. Points comparison between simulations and experiments, the analysis led us to discard points between 1 and 6 and between 31 and 36 because of how unpredictable they were, points from 7 to 30 showed a trend that was closer to the expected one.</figcaption></figure>

Standard error of estimate. For each of the points in the central yellow region (indicated by the yellow box in Fig. 3), the average displacement and standard error of estimate (σest) were calculated (Fig. 4). Among the three repetitions of the structures with an α value of 7.125°, clear trends of points that exhibited the highest and lowest variation were noticed. Specifically, points 7, 18, 19, 23, 24 and 25 had error values between 1 and 1.8 while for the remaining points the dataset were strongly correlated with σest below 0.8. Among the three replicas, Structure 1 (Fig. 4a) is the one with the lowest σest in all points, with most points showing a value under 0.8; structure 2 (Fig. 4b) is the one with the highest σest in all points but with values of σest<1.2 for most of the points; and Structure 3 (Fig. 4b) being much closer to the average of the three structures combined (Fig. 4d). The average value of σest of the three structures combined is 0.687.
<figure><img src="image_381.png" /><figcaption>Figure 4. Standard Error of Estimate of three physical repeats of the structure (α=7.125°) (a, b, c) and the combined values of the three (d).</figcaption></figure>

Fig. 5 (a and b) shows the values of the standard deviation (STD) expressed in % values of the combination of the three replicas. For the X axes, the values are below 10% (Fig. 5a) for 21 of the 24 examined points, notable outliers are present such as points 14, 19 and 30 having STD values between 30% and 60%. In Fig. 5b representing the Y axes, it can be noticed that the values of the STD fall under 10% with no notable outliers. Fig. 5c allows to understand the values in a clearer way, for example points 23, 24, 25 that appear to have a higher absolute *oest* values compared to the rest of the points, are more similar to the other points when the percentage values are observed. The average error in Fig. 5c is 23.86% with points 12 and 14 being outliers with error values over 100%. This error can be imputed to the small total deformation that these two points show, the value of the relative displacement in the y axis is in fact less than 1 mm in both points.

<figure><img src="image_382.png" /><figcaption>Figure 5. Standard Deviation values for the three structures combined both in the X direction (a) and in the Y direction (b); (c) shows the % values of the Standard Error of Estimation of the graph present in Fig. 4d.</figcaption></figure>
<figure><img src="image_387.png" /></figure>

<figure><img src="image_388.png" /></figure>

<figure><img src="image_389.png" /></figure>

<figure><img src="image_390.png" /><figcaption>Figure 4. Standard Error of Estimate of three physical repeats of the structure (α=7.125°) (a, b, c) and the combined values of the three (d).</figcaption></figure>

Fig. 5 (a and b) shows the values of the standard deviation (STD) expressed in % values of the combination of the three replicas. For the X axes, the values are below 10% (Fig. 5a) for 21 of the 24 examined points, notable outliers are present such as points 14, 19 and 30 having STD values between 30% and 60%. In Fig. 5b representing the Y axes, it can be noticed that the values of the STD fall under 10% with no notable outliers. Fig. 5c allows to understand the values in a clearer way, for example points 23, 24, 25 that appear to have a higher absolute *oest* values compared to the rest of the points, are more similar to the other points when the percentage values are observed. The average error in Fig. 5c is 23.86% with points 12 and 14 being outliers with error values over 100%. This error can be imputed to the small total deformation that these two points show, the value of the relative displacement in the y axis is in fact less than 1 mm in both points.

<figure><img src="image_391.png" /></figure>

<figure><img src="image_392.png" /></figure>

<figure><img src="image_393.png" /><figcaption>Figure 5. Standard Deviation values for the three structures combined both in the X direction (a) and in the Y direction (b); (c) shows the % values of the Standard Error of Estimation of the graph present in Fig. 4d.</figcaption></figure>
## Discussion and Conclusions

In Fig. 3 it is possible to notice two main areas where the experimental results do not coincide with the simulations, those two areas are points 1-6 and 31-36 that were very close to the locking mechanism. The instability of the two areas closest to the edges is an indicator that the locking mechanism introduced some instabilities at the edges, specifically during the starting phase of the compression where it is possible to notice the biggest displacement of those points. However, despite this instability, the displacement of the points remains for most of them less than 0.2mm, where for the other points the displacement is usually of several millimeters. Therefore, the error introduced by these points has no impact on the points in the central area that show high levels of displacement accuracy.

In Fig. 4 (a,b and c) it is noticeable how, despite showing the same trend on the points with the highest error, Net 1 is characterized by an overall standard error of estimate lower than Nets 2 and 3. This can be due to a better locking achieved during the testing phase of Net 1 compared to Nets 2 and 3. The three structures all show comparable values when it comes to est, indicating that the FFF process does not lead to significant differences between different structures produced with the same equipment. Moreover, the performed simulations align closely with the experimental results obtained with the positioning stage with a prediction error in the Standard Error of Estimate below 20% for most points, and a standard deviation usually below 10%, indicating the similarity of the results to the real-life situation. The visual comparison between the videos of the deformed structure and the simulations also shows a very similar deformed configuration between the simulations and the compression tests, in accordance with the average error observed in other studies of lattice structures [11,12,13] where errors ranging from 7.4% and 28.2% are reported between the predicted and tested stress-strains curves. In general, AM structures show to be less stiff than the simulated structures, leading to higher deformations than expected. These results show not only that the production process is reliable, but also that the used extraction data process is reliable, and it could be used in future works to extract data from other deformable structures without contact: TrackMate is an already established addon to Fiji (ImageJ) that is used in various fields for single particle tracking [14] and for automatic cell tracking [15]. This study demonstrates that it is also possible to study the displacement of specific points within a structure in reliable way.

## References

[1] P. Ginestra, S. Pandini, E. Ceretti, Hybrid multi-layered scaffolds produced via grain extrusion and electrospinning for 3D cell culture tests, Rapid Prototyping Journal, 26(3), 2019, 593-602.

[2] J. Banks, Adding Value in Additive Manufacturing: Researchers in the United Kingdom and Europe Look to 3D Printing for Customization, in IEEE Pulse, vol. 4, no. 6, pp. 22-26, Nov. 2013. https://doi.org/10.1109/MPUL.2013.2279617

[3] H. Dodziuk, Applications of 3D printing in healthcare. Kardiochirurgia i Torakochirurgia Polska/Polish Journal of Thoracic and Cardiovascular Surgery, 2016;13(3):283-293. https://doi.org/10.5114/kitp.2016.62625

[4] A.A. Zadpoor, Mechanical meta-materials, Mater. Horizons, 3 (5) (2016), pp. 371-381

[5] C.P. de Jonge, H.M.A. Kolken, A.A. Zadpoor, Non-Auxetic Mechanical Metamaterials, Materials. 2019; 12(4):635. https://doi.org/10.3390/ma12040635

[6] H.M.A. Kolken, C.P. de Jonge, T. van der Sloten, A. Fontecha Garcia, B. Pouran, K. Willemsen, H. Weinans, A.A. Zadpoor, Additively manufactured space-filling meta-implants, Acta Biomaterialia, Volume 125, 2021, Pages 345-357, ISSN 1742-7061, https://doi.org/10.1016/j.actbio.2021.02.020
## Discussion and Conclusions

In Fig. 3 it is possible to notice two main areas where the experimental results do not coincide with the simulations, those two areas are points 1-6 and 31-36 that were very close to the locking mechanism. The instability of the two areas closest to the edges is an indicator that the locking mechanism introduced some instabilities at the edges, specifically during the starting phase of the compression where it is possible to notice the biggest displacement of those points. However, despite this instability, the displacement of the points remains for most of them less than 0.2mm, where for the other points the displacement is usually of several millimeters. Therefore, the error introduced by these points has no impact on the points in the central area that show high levels of displacement accuracy.

In Fig. 4 (a,b and c) it is noticeable how, despite showing the same trend on the points with the highest error, Net 1 is characterized by an overall standard error of estimate lower than Nets 2 and 3. This can be due to a better locking achieved during the testing phase of Net 1 compared to Nets 2 and 3. The three structures all show comparable values when it comes to est, indicating that the FFF process does not lead to significant differences between different structures produced with the same equipment. Moreover, the performed simulations align closely with the experimental results obtained with the positioning stage with a prediction error in the Standard Error of Estimate below 20% for most points, and a standard deviation usually below 10%, indicating the similarity of the results to the real-life situation. The visual comparison between the videos of the deformed structure and the simulations also shows a very similar deformed configuration between the simulations and the compression tests, in accordance with the average error observed in other studies of lattice structures [11,12,13] where errors ranging from 7.4% and 28.2% are reported between the predicted and tested stress-strains curves. In general, AM structures show to be less stiff than the simulated structures, leading to higher deformations than expected. These results show not only that the production process is reliable, but also that the used extraction data process is reliable, and it could be used in future works to extract data from other deformable structures without contact: TrackMate is already established as an established addon to Fiji (ImageJ) that is used in various fields for single particle tracking [14] and for automatic cell tracking [15]. This study demonstrates that it is also possible to study the displacement of specific points within a structure in reliable way.

## References

[1] P. Ginestra, S. Pandini, E. Ceretti, Hybrid multi-layered scaffolds produced via grain extrusion and electrospinning for 3D cell culture tests, Rapid Prototyping Journal, 26(3), 2019, 593-602.

[2] J. Banks, Adding Value in Additive Manufacturing: Researchers in the United Kingdom and Europe Look to 3D Printing for Customization, in IEEE Pulse, vol. 4, no. 6, pp. 22-26, Nov. 2013. https://doi.org/10.1109/MPUL.2013.2279617

[3] H. Dodziuk, Applications of 3D printing in healthcare. Kardiochirurgia i Torakochirurgia Polska/Polish Journal of Thoracic and Cardiovascular Surgery, 2016;13(3):283-293. https://doi.org/10.5114/kitp.2016.62625

[4] A.A. Zadpoor, Mechanical meta-materials, Mater. Horizons, 3 (5) (2016), pp. 371-381

[5] C.P. de Jonge, H.M.A. Kolken, A.A. Zadpoor, Non-Auxetic Mechanical Metamaterials, Materials. 2019; 12(4):635. https://doi.org/10.3390/ma12040635

[6] H.M.A. Kolken, C.P. de Jonge, T. van der Sloten, A. Fontecha Garcia, B. Pouran, K. Willemsen, H. Weinans, A.A. Zadpoor, Additively manufactured space-filling meta-implants, Acta Biomaterialia, Volume 125, 2021, Pages 345-357, ISSN 1742-7061, https://doi.org/10.1016/j.actbio.2021.02.020
[7] D. Gastaldi, G. Parisi, R. Lucchini, S. Bignozzi, P.S. Ginestra, G. Filardo, R. Contro, E.Kon, P.Vena, A Predictive Model for the Elastic Properties of a Collagen-Hydroxyapatite Porous Scaffold for Multi-Layer Osteochondral Substitutes, International Journal of Applied Mechanics; Vol. 7, No. 4 (2015) 1550063.

[8] S. Farah, D.G. Anderson, R. Langer, Physical and mechanical properties of PLA, and their functions in widespread applications — A comprehensive review, Advanced Drug Delivery Reviews, Volume 107, 2016, Pages 367-392, ISSN 0169-409X.

[9] J. Schindelin, I. Arganda-Carreras, Frise, E., et. al. Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), (2012), 676-682.
https://doi.org/10.1038/nmeth.2019

[10] Ershov, D., Phan, M.-S., Pylvänäinen, J. W., Rigaud, S. U., Le Blanc, L., Charles-Orszag, A., ... Tinevez, J.-Y. (2022). TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nature Methods, 19(7), 829-832. https://doi.org/10.1038/s41592-022-01507-1

[11] A.M. Abou-Ali, O. Al-Ketan, Dong-Wook Lee, Reza Rowshan, Rashid K. Abu Al-Rub, Mechanical behavior of polymeric selective laser sintered ligament and sheet based lattices of triply periodic minimal surface architectures, Materials & Design, Volume 196, 2020, 109100, ISSN 0264-1275.

[12] Shuai Ma, Qian Tang, Xiaoxiao Han, Qixiang Feng, Jun Song, Rossitza Setchi, Ying Liu, Yang Liu, Athanasios Goulas, Daniel S. Engstrøm, Yau Yau Tse, Ni Zhen, Manufacturability, Mechanical Properties, Mass-Transport Properties and Biocompatibility of Triply Periodic Minimal Surface (TPMS) Porous Scaffolds Fabricated by Selective Laser Melting, Materials & Design, Volume 195, 2020, 109034, ISSN 0264-1275.

[13] Mohammadreza Moeini, Mickael Begon, Martin Lévesque, Numerical homogenization of a linearly elastic honeycomb lattice structure and comparison with analytical and experimental results, Mechanics of Materials, Volume 167, 2022, 104210, ISSN 0167-6636.

[14] Jean-Yves Tinevez, Nick Perry, Johannes Schindelin, Genevieve M. Hoopes, Gregory D. Reynolds, Emmanuel Laplantine, Sebastian Y. Bednarek, Spencer L. Shorte, Kevin W. Eliceiri, TrackMate: An open and extensible platform for single-particle tracking, Methods, Volume 115, 2017, Pages 80-90, ISSN 1046-2023, https://doi.org/10.1016/j.ymeth.2016.09.016

[15] Fazeli, E., Roy, N. H., Follain, G., Laine, R. F., von Chamier, L., Hänninen, P. E., Eriksson, J. E., Tinevez, J. Y., & Jacquemet, G. (2020). Automated cell tracking using StarDist and TrackMate. F1000Research, 9, 1279. https://doi.org/10.12688/f1000research.27019.1
[7] D. Gastaldi, G. Parisi, R. Lucchini, S. Bignozzi, P.S. Ginestra, G. Filardo, R. Contro, E.Kon, P.Vena, A Predictive Model for the Elastic Properties of a Collagen-Hydroxyapatite Porous Scaffold for Multi-Layer Osteochondral Substitutes, International Journal of Applied Mechanics; Vol. 7, No. 4 (2015) 1550063.

[8] S. Farah, D.G. Anderson, R. Langer, Physical and mechanical properties of PLA, and their functions in widespread applications — A comprehensive review, Advanced Drug Delivery Reviews, Volume 107, 2016, Pages 367-392, ISSN 0169-409X.

[9] J. Schindelin, I. Arganda-Carreras, Frise, E., et. al. Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), (2012), 676-682.
https://doi.org/10.1038/nmeth.2019

[10] Ershov, D., Phan, M.-S., Pylvänäinen, J. W., Rigaud, S. U., Le Blanc, L., Charles-Orszag, A., ... Tinevez, J.-Y. (2022). TrackMate 7: integrating state-of-the-art segmentation algorithms into tracking pipelines. Nature Methods, 19(7), 829-832. https://doi.org/10.1038/s41592-022-01507-1

[11] A.M. Abou-Ali, O. Al-Ketan, Dong-Wook Lee, Reza Rowshan, Rashid K. Abu Al-Rub, Mechanical behavior of polymeric selective laser sintered ligament and sheet based lattices of triply periodic minimal surface architectures, Materials & Design, Volume 196, 2020, 109100, ISSN 0264-1275.

[12] Shuai Ma, Qian Tang, Xiaoxiao Han, Qixiang Feng, Jun Song, Rossitza Setchi, Ying Liu, Yang Liu, Athanasios Goulas, Daniel S. Engstrøm, Yau Yau Tse, Ni Zhen, Manufacturability, Mechanical Properties, Mass-Transport Properties and Biocompatibility of Triply Periodic Minimal Surface (TPMS) Porous Scaffolds Fabricated by Selective Laser Melting, Materials & Design, Volume 195, 2020, 109034, ISSN 0264-1275.

[13] Mohammadreza Moeini, Mickael Begon, Martin Lévesque, Numerical homogenization of a linearly elastic honeycomb lattice structure and comparison with analytical and experimental results, Mechanics of Materials, Volume 167, 2022, 104210, ISSN 0167-6636.

[14] Jean-Yves Tinevez, Nick Perry, Johannes Schindelin, Genevieve M. Hoopes, Gregory D. Reynolds, Emmanuel Laplantine, Sebastian Y. Bednarek, Spencer L. Shorte, Kevin W. Eliceiri, TrackMate: An open and extensible platform for single-particle tracking, Methods, Volume 115, 2017, Pages 80-90, ISSN 1046-2023, https://doi.org/10.1016/j.ymeth.2016.09.016

[15] Fazeli, E., Roy, N. H., Follain, G., Laine, R. F., von Chamier, L., Hänninen, P. E., Eriksson, J. E., Tinevez, J. Y., & Jacquemet, G. (2020). Automated cell tracking using StarDist and TrackMate. F1000Research, 9, 1279. https://doi.org/10.12688/f1000research.27019.1
Composites forming processes

<figure><img src="image_383.png" /></figure>
Composites forming processes

<figure><img src="image_394.png" /></figure>
# Basic characterization of the CF-PEKK prepreg and laminates for low temperature applications

POLNIKORN Purith¹,ᵃ*, OLIVIER Philippe¹,ᵇ, CASTANIÉ Bruno¹,ᶜ and
DOUCHIN Bernard¹,ᵈ

¹Clément Ader Institute (ICA, CNRS UMR 5312) CNRS ; IMT MINES ALBI ; INSA ; ISAE-SUPAERO ; Université Toulouse III - Paul Sabatier, Toulouse, France

ᵃpolnikor@insa-toulouse.fr, ᵇPhilippe.olivier@iut-tlse3.fr, ᶜbruno.castanie@insa-toulouse.fr, ᵈbernard.douchin@iut-tlse3.fr

**Keywords:** Thermoplastic Composites, Materials Characterization, Thermomechanical Analysis, Dynamic Mechanical Analysis

**Abstract.** This paper investigates a carbon fibre-reinforced polyetherketoneketone (CF-PEKK) thermoplastic composite used for low-temperature applications like hydrogen tank applications. The degree of crystallinity of the prepreg as received first and then consolidated after hot press has been investigated. The melting temperature and glass transition, as well as the melting enthalpy and degree of crystallinity, were also studied by DSC (differential scanning calorimetry). The fibre volume fraction and void content after consolidation have been measured by acid digestion and an optical microscopy image analysis. Thermomechanical analysis (TMA) was also used in order the coefficients of thermal expansion (CTE) to be determined. Tensile and compressive Dynamic Mechanical Analysis (DMA)test were performed. Storage modulus, loss modulus, and Tan δ relationship have thus been analysed. Multifrequency DMA experiments have been conducted in order to create the master curve thanks to the time-temperature superposition (TTS).

**Introduction**

High-performance thermoplastic matrix composite materials have been widely studied since the beginning of the 1990s, with various studies on CF-PEEK composites. However, this class of materials is not yet widely used in airframe structures. despite the increasing use of composite in Airbus A350 and Boeing 787. Only high-performance thermoplastic matrix like polyimides (TPI), polyetherimides (PEI), polyaryletherketone (PAEK), and polyetheretherketone (PEEK) are used for aircraft structures right now [1].

Despite its heightened interest in terms of potential recyclability and physico-mechanical characteristics, PEEK matrix composites require high-temperature consolidation cycles and might undergo some thermomechanical-induced distortion in large parts in terms of shape and geometry. This is why some other thermoplastic matrices of the polyetherketone family can be used to manufacture composites at temperatures lower than those required. Therefore, this study focused on one of these matrices: polyetherketoneketone (PEKK), whose processing temperature is much lower than that of PEEK. PEKK's are made from diphenylether (DPE), terephathlic acid (T) with para-phenyl links, and isophthalic acid (I) with meta-phenyl links. The presence of terephthalic/isophthalic content (T/I ratio) in PEKK leads to variations in crystallization kinetics and capabilities [2, 3, 4].

Even though the main goal of our study was to investigate the CF-PEKK matrix in low-temperature structures for storing liquid hydrogen, this paper aims to present the basic thermomechanical and chemorheological characterization of this material around room temperature. This involves utilizing various equipment and procedures, such as resin dissolution and microscopy image analysis, density determination, DSC (differential scanning calorimetry), TMA (thermomechanical analysis), and DMA (dynamic mechanical analysis). This

<figure><img src="image_384.png" /></figure>
# Basic characterization of the CF-PEKK prepreg and laminates for low temperature applications

POLNIKORN Purith¹,ª*, OLIVIER Philippe¹,ᵇ, CASTANIÉ Bruno¹,ᶜ and
DOUCHIN Bernard¹,ᵈ

¹Clément Ader Institute (ICA, CNRS UMR 5312) CNRS ; IMT MINES ALBI ; INSA ; ISAE-SUPAERO ; Université Toulouse III - Paul Sabatier, Toulouse, France

ªpolnikor@insa-toulouse.fr, ᵇPhilippe.olivier@iut-tlse3.fr, ᶜbruno.castanie@insa-toulouse.fr, ᵈbernard.douchin@iut-tlse3.fr

**Keywords:** Thermoplastic Composites, Materials Characterization, Thermomechanical Analysis, Dynamic Mechanical Analysis

**Abstract.** This paper investigates a carbon fibre-reinforced polyetherketoneketone (CF-PEKK) thermoplastic composite used for low-temperature applications like hydrogen tank applications. The degree of crystallinity of the prepreg as received first and then consolidated after hot press has been investigated. The melting temperature and glass transition, as well as the melting enthalpy and degree of crystallinity, were also studied by DSC (differential scanning calorimetry). The fibre volume fraction and void content after consolidation have been measured by acid digestion and an optical microscopy image analysis. Thermomechanical analysis (TMA) was also used in order the coefficients of thermal expansion (CTE) to be determined. Tensile and compressive Dynamic Mechanical Analysis (DMA)test were performed. Storage modulus, loss modulus, and Tan δ relationship have thus been analysed. Multifrequency DMA experiments have been conducted in order to create the master curve thanks to the time-temperature superposition (TTS).

**Introduction**

High-performance thermoplastic matrix composite materials have been widely studied since the beginning of the 1990s, with various studies on CF-PEEK composites. However, this class of materials is not yet widely used in airframe structures. despite the increasing use of composite in Airbus A350 and Boeing 787. Only high-performance thermoplastic matrix like polyimides (TPI), polyetherimides (PEI), polyaryletherketone (PAEK), and polyetheretherketone (PEEK) are used for aircraft structures right now [1].

Despite its heightened interest in terms of potential recyclability and physico-mechanical characteristics, PEEK matrix composites require high-temperature consolidation cycles and might undergo some thermomechanical-induced distortion in large parts in terms of shape and geometry. This is why some other thermoplastic matrices of the polyetherketone family can be used to manufacture composites at temperatures lower than those required. Therefore, this study focused on one of these matrices: polyetherketoneketone (PEKK), whose processing temperature is much lower than that of PEEK. PEKK's are made from diphenylether (DPE), terephathlic acid (T) with para-phenyl links, and isophthalic acid (I) with meta-phenyl links. The presence of terephthalic/isophthalic content (T/I ratio) in PEKK leads to variations in crystallization kinetics and capabilities [2, 3, 4].

Even though the main goal of our study was to investigate the CF-PEKK matrix in low-temperature structures for storing liquid hydrogen, this paper aims to present the basic thermomechanical and chemorheological characterization of this material around room temperature. This involves utilizing various equipment and procedures, such as resin dissolution and microscopy image analysis, density determination, DSC (differential scanning calorimetry), TMA (thermomechanical analysis), and DMA (dynamic mechanical analysis). This

<figure><img src="image_395.png" /></figure>
characterization campaign was carried out both on as-received CF-PEKK prepreg and on [0]₆ unidirectional laminates consolidated using a hot plate press. All along this characterization campaign, results obtained on as-received CF-PEKK unidirectional tape are compared with those obtained on consolidated laminates. Along with matrix acid digestion to find out the fibre volume content, a microstructural study of prepreg and consolidated laminate was also carried out.

## Manufacturing process and materials

The CF-PEKK unidirectional prepreg used for this study was provided by Hexcel composite France. Its commercial reference or its technical datasheet are not available. This is why some basic physical characterization has been performed in the aim to enable manufacturing conditions to be chosen for future larger production and test campaigns. We have just said that for PEKK matrices the T/I ratio determines melting temperature (T<sub>m</sub>) and therefore consolidation cycle. Considering T<sub>m</sub> value around 339°C obtained by DSC on as-received carbon/PEKK prepreg, it can be inferred experiments that T/I is of 70/30 T/I. For all laminates, ply stacking was performed in a clean room (T<sub>Cleanroom</sub> = 20 °C and R.H. = 50°C) and consolidated owing a laboratory press (MIB hydro), which enable to goes up to 450 °C with a 10 MPa (100 bar) pressure. In the absence of technical data upon our prepreg, it was decided to rely on open literature to define the parameters of the prepreg consolidation cycle (temperature, time, pressure), see Table 1.

The consolidation cycle is described in Figure 1 together with mould configuration. Unidirectional [0]₆ laminates used in this study were consolidated on a 250x250 mm stainless steel with two layers of thermoplastic 50 µm thick polyimide film (DIATEX France) as release agents

<table><caption>Table 1: Manufacturing parameters from literature [2,3,4]</caption><thead><tr><th colspan="3">Manufacturing parameters</th></tr></thead><tbody><tr><td>Consolidation temperature</td><td>360 to 410</td><td>[°C]</td></tr><tr><td>Consolidation pressure</td><td>10 to 40</td><td>[Bar]</td></tr><tr><td>Consolidation time</td><td>10 to 30</td><td>[min]</td></tr></tbody></table>

<figure><img src="image_385.png" /><figcaption>Figure 1: Laboratory hot press</figcaption></figure>
characterization campaign was carried out both on as-received CF-PEKK prepreg and on [0]₆ unidirectional laminates consolidated using a hot plate press. All along this characterization campaign, results obtained on as-received CF-PEKK unidirectional tape are compared with those obtained on consolidated laminates. Along with matrix acid digestion to find out the fibre volume content, a microstructural study of prepreg and consolidated laminate was also carried out.

## Manufacturing process and materials

The CF-PEKK unidirectional prepreg used for this study was provided by Hexcel composite France. Its commercial reference or its technical datasheet are not available. This is why some basic physical characterization has been performed in the aim to enable manufacturing conditions to be chosen for future larger production and test campaigns. We have just said that for PEKK matrices the T/I ratio determines melting temperature (T<sub>m</sub>) and therefore consolidation cycle. Considering T<sub>m</sub> value around 339°C obtained by DSC on as-received carbon/PEKK prepreg, it can be inferred experiments that T/I is of 70/30 T/I. For all laminates, ply stacking was performed in a clean room (T<sub>Cleanroom</sub> = 20 °C and R.H. = 50°C) and consolidated owing a laboratory press (MIB hydro), which enable to goes up to 450 °C with a 10 MPa (100 bar) pressure. In the absence of technical data upon our prepreg, it was decided to rely on open literature to define the parameters of the prepreg consolidation cycle (temperature, time, pressure), see Table 1.

The consolidation cycle is described in Figure 1 together with mould configuration. Unidirectional [0]₆ laminates used in this study were consolidated on a 250x250 mm stainless steel with two layers of thermoplastic 50 µm thick polyimide film (DIATEX France) as release agents

<table><caption>Table 1: Manufacturing parameters from literature [2,3,4]</caption><thead><tr><th colspan="3">Manufacturing parameters</th></tr></thead><tbody><tr><td>Consolidation temperature</td><td>360 to 410</td><td>[°C]</td></tr><tr><td>Consolidation pressure</td><td>10 to 40</td><td>[Bar]</td></tr><tr><td>Consolidation time</td><td>10 to 30</td><td>[min]</td></tr></tbody></table>

<figure><img src="image_396.png" /><figcaption>Figure 1: Laboratory hot press</figcaption></figure>
## Experimental methods

### Fibre volume fraction and void content

Fibre volume fraction ($V_f$ %) was determined both on as-received CF-PEKK prepreg and on consolidated laminates by matrix acid digestion and optical microscopic image analysis, while the void content ($V_v$%) was determined only on consolidated [0]$_6$ laminate by optical microscopic image analysis.

### Acid digestion

Matrix acid digestion was performed according to the EN 2564:2018 Standard [5]. A series of prepreg and laminated samples were immersed in a concentrated sulfuric acid solution for 5 hours at 260°C. After that stage, the resulting fibre network was filtered, dried, and weighed in order to determine the $V_f$%. Parallely, the density of the specimen was measured according to Method A of ISO 1183 [6]. Lately, some fibres were observed on the sintered glass crucible to ensure that there were no PEKK particles remaining after the experiment

### Image analysis

Determination of $V_f$ % and $V_v$% by image analysis was performed on an Olympus BX41M Microscope together with ImageJ freeware. Prior to any microscopy observation, all specimens were polished according to the PRESI (Polishing equipment manufacturer) polishing procedure designed especially for thermoplastic matrix composites.

$V_f$% and $V_v$% were evaluated (using image J) with a conventional fibre counting approach with the maxima count function enabling to reflect the number of fibres ($N_f$). Knowing the average diameter of carbon fibers average diameter ($d_f$) can also determine with image analysis since we ignore carbon fibre reference. Therefore, $V_f$% can be obtained by [7,8].

$$V_f = \frac{\pi d_f^2 \times N_f / 4}{N_{image} / CF^2} \qquad (1)$$

$N_{image}$ is total the number of all pixel of a given image. CF is a conversion factor of the length of image. For instance, an image with 300 pixels per 100 µm gives CF=3.

$V_v$%, can be also obtained by acid digestion method and optical microscope. For acid digestion, the calculation was used the same method as the calculation of the $V_f$% by using rules of mixtures. For the optical microscopy, the $V_v$% obtained by image analysis but focused on the void area. From that procedure the $V_v$ % can be verified.
## Experimental methods

### Fibre volume fraction and void content

Fibre volume fraction ($V_f$ %) was determined both on as-received CF-PEKK prepreg and on consolidated laminates by matrix acid digestion and optical microscopic image analysis, while the void content ($V_v$%) was determined only on consolidated [0]$_6$ laminate by optical microscopic image analysis.

### Acid digestion

Matrix acid digestion was performed according to the EN 2564:2018 Standard [5]. A series of prepreg and laminated samples were immersed in a concentrated sulfuric acid solution for 5 hours at 260°C. After that stage, the resulting fibre network was filtered, dried, and weighed in order to determine the $V_f$%. Parallely, the density of the specimen was measured according to Method A of ISO 1183 [6]. Lately, some fibres were observed on the sintered glass crucible to ensure that there were no PEKK particles remaining after the experiment

### Image analysis

Determination of $V_f$ % and $V_v$% by image analysis was performed on an Olympus BX41M Microscope together with ImageJ freeware. Prior to any microscopy observation, all specimens were polished according to the PRESI (Polishing equipment manufacturer) polishing procedure designed especially for thermoplastic matrix composites.

$V_f$% and $V_v$% were evaluated (using image J) with a conventional fibre counting approach with the maxima count function enabling to reflect the number of fibres ($N_f$). Knowing the average diameter of carbon fibers average diameter ($d_f$) can also determine with image analysis since we ignore carbon fibre reference. Therefore, $V_f$% can be obtained by [7,8].

$$V_f = \frac{\pi d_f^2 \times N_f / 4}{N_{image} / CF^2} \qquad (1)$$

$N_{image}$ is total the number of all pixel of a given image. CF is a conversion factor of the length of image. For instance, an image with 300 pixels per 100 µm gives CF=3.

$V_v$%, can be also obtained by acid digestion method and optical microscope. For acid digestion, the calculation was used the same method as the calculation of the $V_f$% by using rules of mixtures. For the optical microscopy, the $V_v$% obtained by image analysis but focused on the void area. From that procedure the $V_v$ % can be verified.
<figure><img src="image_386.png" /><figcaption>Figure 2: Acid digestion and optical microscopy analysis for fibre volume fraction and void content: (a) stitching images (b) 8-bit grey scale image conversion (c) conventional maxima method (d) carbon fibres after acid digestion of PEKK matrix.</figcaption></figure>

### Differential scanning calorimetry (DSC)

All measurement was carried out on a TA instrument DSC250. Prepreg samples (average mass around 15mg) were submitted to 5°C/min heat ramps from 40°C to 400 °C in a conventional scan mode. Samples extracted from consolidated composite laminates were submitted to a heat-cool-heat thermal cycle between 40°C and 400 °C with 5°C/min heating and cooling ramps. DSC thermograms enable PEKK matrix glass transition, melting and crystallization temperature to be determined, as well as the crystallization and the melting enthalpies. From these enthalpies the PEKK matrix degree of crystallization can be calculated as follows:

$$X_c = \frac{\Delta H_m - \Delta H_{cc}}{\Delta H_f^0 (1 - W_f)} \times 100\% \qquad (2)$$

In equation (2) $\Delta H_m$ represent the melting enthalpy of PEEK (J/g), $\Delta H_{cc}$ is cold crystallization enthalpy of the matrix (J/g) $\Delta H_f^0$ is theoretical melting enthalpy of crystallization of 100% crystalline matrix (J/g) (PEEK, PEKK = 130 J/g) [9] and $W_f$ is weight fraction of fiber has been calculated from acid digestion experiment.

### Thermomechanical analysis (TMA)

Measurement of coefficient of thermal expansion (CTE) as well as glass transition temperature ($T_g$) were determined from thermal scans performed on a NETZSCH TMA 402 F1 in expansion
<figure><img src="image_397.png" /><figcaption>Figure 2: Acid digestion and optical microscopy analysis for fibre volume fraction and void content: (a) stitching images (b) 8-bit grey scale image conversion (c) conventional maxima method (d) carbon fibres after acid digestion of PEKK matrix.</figcaption></figure>

### Differential scanning calorimetry (DSC)

All measurement was carried out on a TA instrument DSC250. Prepreg samples (average mass around 15mg) were submitted to 5°C/min heat ramps from 40°C to 400 °C in a conventional scan mode. Samples extracted from consolidated composite laminates were submitted to a heat-cool-heat thermal cycle between 40°C and 400 °C with 5°C/min heating and cooling ramps. DSC thermograms enable PEKK matrix glass transition, melting and crystallization temperature to be determined, as well as the crystallization and the melting enthalpies. From these enthalpies the PEKK matrix degree of crystallization can be calculated as follows:

$$X_c = \frac{\Delta H_m - \Delta H_{cc}}{\Delta H_f^0 (1 - W_f)} \times 100\% \qquad (2)$$

In equation (2) $\Delta H_m$ represent the melting enthalpy of PEEK (J/g), $\Delta H_{cc}$ is cold crystallization enthalpy of the matrix (J/g) $\Delta H_f^0$ is theoretical melting enthalpy of crystallization of 100% crystalline matrix (J/g) (PEEK, PEKK = 130 J/g) [9] and $W_f$ is weight fraction of fiber has been calculated from acid digestion experiment.

### Thermomechanical analysis (TMA)

Measurement of coefficient of thermal expansion (CTE) as well as glass transition temperature ($T_g$) were determined from thermal scans performed on a NETZSCH TMA 402 F1 in expansion
mode. [0]₆ is the lay-up of the specimens. The dimension of the specimens was 3×3×1.6 mm (length, width, thickness). Those later range from -150 °C (minimum achievable temperature) to 300 °C heat rate was 5 °C/min with the contact force 1mN on the expansion probe.

### DMA measurement

The thermoviscoelastic behaviour of CF-PEKK was studied using a DMA+100 Metravib equipped with a thermal chamber enabling DMA tests to be performed between -150°C up to 500°C. Here, temperature scans were carried out only between room temperature up to 300°C. Prepreg and composites samples [0]₆ were all tested in tensile mode with carbon fibre perpendicular to tensile force axis.

A first set of DMA tests on prepreg and consolidated composite samples were made under a constant frequency at 1 Hz, while a second set of tests used for building the CF-PEKK master curve (with WLF modelling) were performed at five different frequencies 0.01,0.03,0.1,0.5 and 1 Hz. DMA samples room temperature dimension were 50×10×1.6 mm (length, width, thickness). The first set of DMA experiments enabled the changes in storage (E') and loss (E'') moduli as well as Tan δ (see eq. (3)) to be followed and Tan δ peak temperature corresponding to the main mechanical relaxation peak, manifestation of glass transition of PEKK matrix to be determined.

$$ \text{Tan } \delta = \frac{E''}{E'} \qquad (3) $$

The second set of DMA experiments consists in multifrequencies sweeps between room temperature and 300°C enabling to (i) build the material master curve (thanks to WLF modelling eq. (4) and (5)) and (ii) determine the activation energy of the main mechanical relaxation. For temperature lower than the PEKK matrix T_g (considered as T_g = T_ref = 165.2 °C) we have [10,11].

$$ \log a_t = \frac{E_a}{2.303 \cdot R} \cdot \left( \frac{1}{T} - \frac{1}{T_{\text{Ref}}} \right) \qquad (4) $$

Where E_a denotes the activation energy and R, the ideal gas constant (8.3143 J mol⁻¹ K⁻¹) and T_ref, the reference temperature. Here, T_ref = T_g.

For temperature higher than PEKK matrix T_g the log of the shift factor a_T of the WLF modelling is given by:

$$ \log a_T = \frac{-C_1 (T-T_0)}{C_2+(T-T_0)} \qquad (5) $$

In this equation (5) Where C_1 and C_2 are constants. The parameters C_1 and C_2 may vary when the different reference temperature is chosen [11]. In our case, the T_ref = T_g.

## Results and discussion

### Fiber volume fraction Vf% and Void content Vv%

Obtained result in terms of Vf% and Vv% using matrix acid digestion, optical microscopy and image analysis are given in Table 2. It can be seen that a rather good agreement is obtained for consolidated laminates Vf% and Vv% between the different experimental methods and that the void content is very low. This shows the quality from a microstructural point of view of the consolidation cycle (cf. Figure 1). It is important to note that the void content in Table 2, especially the void content measured by optical microscopy, is measured directly on the images, whereas with the acid digestion method, density is required.
mode. [0]₆ is the lay-up of the specimens. The dimension of the specimens was 3×3×1.6 mm (length, width, thickness). Those later range from -150 °C (minimum achievable temperature) to 300 °C heat rate was 5 °C/min with the contact force 1mN on the expansion probe.

### DMA measurement

The thermoviscoelastic behaviour of CF-PEKK was studied using a DMA+100 Metravib equipped with a thermal chamber enabling DMA tests to be performed between -150°C up to 500°C. Here, temperature scans were carried out only between room temperature up to 300°C. Prepreg and composites samples [0]₆ were all tested in tensile mode with carbon fibre perpendicular to tensile force axis.

A first set of DMA tests on prepreg and consolidated composite samples were made under a constant frequency at 1 Hz, while a second set of tests used for building the CF-PEKK master curve (with WLF modelling) were performed at five different frequencies 0.01,0.03,0.1,0.5 and 1 Hz. DMA samples room temperature dimension were 50×10×1.6 mm (length, width, thickness). The first set of DMA experiments enabled the changes in storage (E') and loss (E'') moduli as well as Tan δ (see eq. (3)) to be followed and Tan δ peak temperature corresponding to the main mechanical relaxation peak, manifestation of glass transition of PEKK matrix to be determined.

$$ \text{Tan } \delta = \frac{E''}{E'} \qquad (3) $$

The second set of DMA experiments consists in multifrequencies sweeps between room temperature and 300°C enabling to (i) build the material master curve (thanks to WLF modelling eq. (4) and (5)) and (ii) determine the activation energy of the main mechanical relaxation. For temperature lower than the PEKK matrix T_g (considered as T_g = T_ref = 165.2 °C) we have [10,11].

$$ \log a_t = \frac{E_a}{2.303 \cdot R} \cdot \left( \frac{1}{T} - \frac{1}{T_{\text{Ref}}} \right) \qquad (4) $$

Where E_a denotes the activation energy and R, the ideal gas constant (8.3143 J mol⁻¹ K⁻¹) and T_ref, the reference temperature. Here, T_ref = T_g.

For temperature higher than PEKK matrix T_g the log of the shift factor a_T of the WLF modelling is given by:

$$ \log a_T = \frac{-C_1 (T-T_0)}{C_2+(T-T_0)} \qquad (5) $$

In this equation (5) Where C_1 and C_2 are constants. The parameters C_1 and C_2 may vary when the different reference temperature is chosen [11]. In our case, the T_ref = T_g.

## Results and discussion

### Fiber volume fraction Vf% and Void content Vv%

Obtained result in terms of Vf% and Vv% using matrix acid digestion, optical microscopy and image analysis are given in Table 2. It can be seen that a rather good agreement is obtained for consolidated laminates Vf% and Vv% between the different experimental methods and that the void content is very low. This shows the quality from a microstructural point of view of the consolidation cycle (cf. Figure 1). It is important to note that the void content in Table 2, especially the void content measured by optical microscopy, is measured directly on the images, whereas with the acid digestion method, density is required.
<table><caption>Table 2: Vf% and Vv%: Result of optical microscopy and image analysis and Acid digestion</caption><thead><tr><th rowspan="2">Hot press</th><th colspan="2">Optical microscopy</th><th colspan="2">Acid digestion</th><th rowspan="2">Density<br>[g/cm3]</th></tr><tr><th>Void Volume<br>content [%]</th><th>Fiber volume<br>fraction [%]</th><th>Void Volume<br>content [%]</th><th>Fiber volume<br>fraction [%]</th></tr></thead><tbody><tr><td>CF-PEKK prepreg as<br>receive [12]</td><td>5.8±0.7</td><td>55.6±1.4</td><td></td><td></td><td></td></tr><tr><td>CF-PEKK consolidated</td><td>0.45 ± 0.16</td><td>55.22±2</td><td>0.4 ± 0.2</td><td>57.3 ± 1.5</td><td>1.56±0.01</td></tr></tbody></table>

### DSC measurement

Figure 3a show a DSC scan thermogram representative of the CF-PEKK thermal behaviour during a 5°C/min heating ramp. From this thermogram three events can be spotted: (i) Glass transition at 157 °C, (ii) exothermic peak corresponding to cold crystallization at 203 °C and (iii) endothermic peak corresponding to the PEKK matrix melting at 339 °C. Cold crystallization and melting enthalpies $H_{cc}$ and $H_{m}$ were obtained by peak integration with respect to the temperature. Results given in table 3 enable the degree of crystallinity of as-received prepreg to be calculated with (eq. (2)): $X_{c} = 4.2\% \pm 2\%$ a rather low value.

Figure 3b shows a typical DSC scan thermogram obtained on a sample extracted from a [0]$_{6}$ consolidated laminate. The heat-cool-heat cycle that the samples are submitted shows several points: (i) first heating ramp reveals a $T_{g}$ of 162.27 °C and the absence of cold crystallization peak, while $T_{m}$ become slightly higher at 346.11°C when compared to $T_{m}$ exhibited by prepreg; (ii) during cooling, a crystallization peak occurs at 264.51 °C ($\Delta H_{c}$ 31.127 J/g); (iii) second heat ramp shows that $T_{g}$ remains around 160.34°C and that melting peak occurred at around 346.11°C with an enthalpy of 27 J/g versus 30 J/g for prepreg. As shown in table 3, values after consolidation $T_{g}$ has under gone a slight increase: 157°C for prepreg versus 163 °C for consolidated laminate. It can also be noticed that $T_{m}$ (Melting temperature) value increase by a few between the prepreg state and consolidated laminate one. Enthalpy values were used to evaluate the degree of crystallinity which grows from 4.2% for prepreg up to 21% for PEKK matrix in consolidated laminates showing that the strong effect of consolidation cycle.

As PEKK usually related to the presence of terephthalic/isophthalic content (T/I ratio) in PEKK leads to variations in crystallization kinetics and capabilities. Due to the lack of information on the CF-PEKK the grade of PEKK has been considered as PEKK 70/30 T/I ratio as in Choupin et al. [3,13].

Quiroga Cortés et al. also studied thermal behaviour of PEKK 60/40, 70/30, and 80/20 grades by DSC [13]. They found that PEKK 70/30 has a $T_{g}$ of 159 °C and exhibits a melting temperature at 335 °C, while PEKK 60/40 has a $T_{g}$ of 156 °C and presents a $T_{m}$ at 300 °C. PEKK 60/40 also exhibits a cold crystallization temperature, $T_{cc}$, after the glass transition at $T_{cc}$ of 241 °C. The crystallinity ratio is 3% for 60/40, 27% for 70/30. Where 60/40 is a quasi-amorphous polymer, and 70/30 and 80/20 are semi-crystalline polymers. This is assuming that the CF-PEKK prepreg as received has more amorphous region or act like 60/40 as [2, 3, 10, 14] mentioned. But the $T_{m}$ is much higher than the typical 60/40 ratio.

Choupin et al. [3] also conducted a study using PEKK 6002 and 7002 with T/I ratios of 60/40 and 70/30, respectively. Their findings revealed that higher T/I ratios result in higher melting temperatures. From this, we assumed that our received prepreg is 70/30 T/I ratio with more amorphous region due to our $T_{m}$ it is much higher than the studies of Quiroga Cortés et al [13] and Choupin [3].

It is important to note that all result of the DSC scans was conduct 4 times on each test. The values and their standard deviation are given in table 3.
<table><caption>Table 2: Vf% and Vv%: Result of optical microscopy and image analysis and Acid digestion</caption><thead><tr><th rowspan="2">Hot press</th><th colspan="2">Optical microscopy</th><th colspan="2">Acid digestion</th><th rowspan="2">Density<br>[g/cm3]</th></tr><tr><th>Void Volume<br>content [%]</th><th>Fiber volume<br>fraction [%]</th><th>Void Volume<br>content [%]</th><th>Fiber volume<br>fraction [%]</th></tr></thead><tbody><tr><td>CF-PEKK prepreg as<br>receive [12]</td><td>5.8±0.7</td><td>55.6±1.4</td><td></td><td></td><td></td></tr><tr><td>CF-PEKK consolidated</td><td>0.45 ± 0.16</td><td>55.22±2</td><td>0.4 ± 0.2</td><td>57.3 ± 1.5</td><td>1.56±0.01</td></tr></tbody></table>

### DSC measurement

Figure 3a show a DSC scan thermogram representative of the CF-PEKK thermal behaviour during a 5°C/min heating ramp. From this thermogram three events can be spotted: (i) Glass transition at 157 °C, (ii) exothermic peak corresponding to cold crystallization at 203 °C and (iii) endothermic peak corresponding to the PEKK matrix melting at 339 °C. Cold crystallization and melting enthalpies $H_{cc}$ and $H_{m}$ were obtained by peak integration with respect to the temperature. Results given in table 3 enable the degree of crystallinity of as-received prepreg to be calculated with (eq. (2)): $X_{c} = 4.2\% \pm 2\%$ a rather low value.

Figure 3b shows a typical DSC scan thermogram obtained on a sample extracted from a [0]$_{6}$ consolidated laminate. The heat-cool-heat cycle that the samples are submitted shows several points: (i) first heating ramp reveals a $T_{g}$ of 162.27 °C and the absence of cold crystallization peak, while $T_{m}$ become slightly higher at 346.11°C when compared to $T_{m}$ exhibited by prepreg; (ii) during cooling, a crystallization peak occurs at 264.51 °C ($\Delta H_{c}$ 31.127 J/g); (iii) second heat ramp shows that $T_{g}$ remains around 160.34°C and that melting peak occurred at around 346.11°C with an enthalpy of 27 J/g versus 30 J/g for prepreg. As shown in table 3, values after consolidation $T_{g}$ has under gone a slight increase: 157°C for prepreg versus 163 °C for consolidated laminate. It can also be noticed that $T_{m}$ (Melting temperature) value increase by a few between the prepreg state and consolidated laminate one. Enthalpy values were used to evaluate the degree of crystallinity which grows from 4.2% for prepreg up to 21% for PEKK matrix in consolidated laminates showing that the strong effect of consolidation cycle.

As PEKK usually related to the presence of terephthalic/isophthalic content (T/I ratio) in PEKK leads to variations in crystallization kinetics and capabilities. Due to the lack of information on the CF-PEKK the grade of PEKK has been considered as PEKK 70/30 T/I ratio as in Choupin et al. [3,13].

Quiroga Cortés et al. also studied thermal behaviour of PEKK 60/40, 70/30, and 80/20 grades by DSC [13]. They found that PEKK 70/30 has a $T_{g}$ of 159 °C and exhibits a melting temperature at 335 °C, while PEKK 60/40 has a $T_{g}$ of 156 °C and presents a $T_{m}$ at 300 °C. PEKK 60/40 also exhibits a cold crystallization temperature, $T_{cc}$, after the glass transition at $T_{cc}$ of 241 °C. The crystallinity ratio is 3% for 60/40, 27% for 70/30. Where 60/40 is a quasi-amorphous polymer, and 70/30 and 80/20 are semi-crystalline polymers. This is assuming that the CF-PEKK prepreg as received has more amorphous region or act like 60/40 as [2, 3, 10, 14] mentioned. But the $T_{m}$ is much higher than the typical 60/40 ratio.

Choupin et al. [3] also conducted a study using PEKK 6002 and 7002 with T/I ratios of 60/40 and 70/30, respectively. Their findings revealed that higher T/I ratios result in higher melting temperatures. From this, we assumed that our received prepreg is 70/30 T/I ratio with more amorphous region due to our $T_{m}$ it is much higher than the studies of Quiroga Cortés et al [13] and Choupin [3].

It is important to note that all result of the DSC scans was conduct 4 times on each test. The values and their standard deviation are given in table 3.
<figure><img src="image_387.png" /><figcaption>Figure 3: DSC result for CF-PEKK prepreg (a) and consolidated (b)</figcaption></figure>

<table><caption>Table 3: Summary of DSC result for CF-PEKK prepreg and consolidated</caption><thead><tr><th></th><th>T<sub>g</sub>(°C)</th><th>T<sub>cc</sub>(°C)</th><th>T<sub>m</sub>(°C)</th><th>ΔH<sub>cc</sub>(J/g)</th><th>ΔH<sub>m</sub>(J/g)</th><th>Degree of crystallinity X<sub>c</sub><br>(%)</th></tr></thead><tbody><tr><td>CF-PEKK prepreg</td><td>157 ± 0.4</td><td>203 ± 0.2</td><td>339 ± 0.5</td><td>25.1 ± 0.5</td><td>30.54 ± 0.5</td><td>4.2% ± 2%</td></tr><tr><td rowspan="2">CF-PEKK consolidated</td><td>162.27 ± 4</td><td>N/A</td><td>346.11 ± 4</td><td>N/A</td><td>30.199 ± 1</td><td>20.67% ± 0.01</td></tr><tr><td>160.34 ± 3</td><td>N/A</td><td>341.76 ± 1</td><td>N/A</td><td>27.17 ± 1.5</td><td>21.26% ± 0.01</td></tr></tbody></table>

Dilatometry (TMA)

The thermal expansion perpendicular to the lamination plane of CF-PEKK composite samples in plotted in Figure 4. As it can be seen that the expansion can be divided into three distinct zones. In the first one between -150°C to 0°C, the coefficient of thermal expansion (CTE) $\alpha_z$ is $20 \times 10^{-6} \text{C}^{-1}$. Between 0°C and the thermomechanical manifestation of glass transition (here $T_g$ =157.42 °C) $\alpha_z$ = $40 \times 10^{-6} \text{C}^{-1}$. In the third zone after glass transition $\alpha_z$ increase up to $100 \times 10^{-6} \text{C}^{-1}$.
<figure><img src="image_398.png" /><figcaption>Figure 3: DSC result for CF-PEKK prepreg (a) and consolidated (b)</figcaption></figure>

<table><caption>Table 3: Summary of DSC result for CF-PEKK prepreg and consolidated</caption><thead><tr><th></th><th>T<sub>g</sub>(°C)</th><th>T<sub>cc</sub>(°C)</th><th>T<sub>m</sub>(°C)</th><th>ΔH<sub>cc</sub> (J/g)</th><th>ΔH<sub>m</sub> (J/g)</th><th>Degree of crystallinity X<sub>c</sub> (%)</th></tr></thead><tbody><tr><td>CF-PEKK prepreg</td><td>157 ± 0.4</td><td>203 ± 0.2</td><td>339 ± 0.5</td><td>25.1 ± 0.5</td><td>30.54 ± 0.5</td><td>4.2% ± 2%</td></tr><tr><td rowspan="2">CF-PEKK consolidated</td><td>162.27 ± 4</td><td>N/A</td><td>346.11 ± 4</td><td>N/A</td><td>30.199 ± 1</td><td>20.67% ± 0.01</td></tr><tr><td>160.34 ± 3</td><td>N/A</td><td>341.76 ± 1</td><td>N/A</td><td>27.17 ± 1.5</td><td>21.26% ± 0.01</td></tr></tbody></table>

Dilatometry (TMA)

The thermal expansion perpendicular to the lamination plane of CF-PEKK composite samples in plotted in Figure 4. As it can be seen that the expansion can be divided into three distinct zones. In the first one between -150°C to 0°C, the coefficient of thermal expansion (CTE) $\alpha_z$ is $20 \times 10^{-6} \text{C}^{-1}$. Between 0°C and the thermomechanical manifestation of glass transition (here $T_g$ =157.42 °C) $\alpha_z$ = $40 \times 10^{-6} \text{C}^{-1}$. In the third zone after glass transition $\alpha_z$ increase up to $100 \times 10^{-6} \text{C}^{-1}$.
Unfortunately, due to the issues with the TMA machine the CTE of the prepreg form cannot be provided here.

<figure><img src="image_388.png" /><figcaption>Figure 4: TMA result of CF-PEKK consolidated. Measurement perpendicular to the lamination plane.</figcaption></figure>

## Dynamical mechanical analysis (DMA)

Figure 5(a and b) enables the behaviour of the prepreg and consolidated laminate to be compared. As it can be seen on Tan δ (equation (3)) curves there are some differences in the main mechanical relaxation peak (or α-peak). First, $T_α$, the maximum temperature of α peak reach 170 °C for the laminate while it was 166 °C for the prepreg. Furthermore, the two peak magnitudes are very different showing that PEKK matrix amorphous network has undergone important changes between the two states. In addition, when paying attention to storage moduli curve E' it can be noticed that for the prepreg (figure 5a), E' undergoes an increase starting around 200 °C. This recalls the cold crystallization exothermic peak recorded on prepreg by DSC (figure3a). This change in E'-which does not appear for consolidated composite in figure 5b- is linked to cold crystallization.

The second part of this work with DMA was to set up a master curve for CF-PEKK consolidated composite. [90]₆ composite samples were submitted to multifrequency test under tension loading. Figure 6 shows the changes in E' storage modulus as function of temperature and frequency (f). Figure 7 give a view of the main mechanical relaxation peak (denoted by α-peak) as a function of frequency. Obtained result enable figures 8a and 8b to be build. They respectively show the change in Log(f) versus 1/T and the master curves for the storage E' and the Loss E'' moduli as a function of frequency. This master curve (equation (4) and (5)) gives value of C1 and C2 WLF constant (see figure8b): $C_1 = 1$ and $C_2 = 5.4$. This representation facilitates the calculation of the activation energy using equation (4), each applicable in distinct temperature ranges. The combined application of these equations yields an activation energy of approximately 2228.84 kJ/mol.
Unfortunately, due to the issues with the TMA machine the CTE of the prepreg form cannot be provided here.

<figure><img src="image_399.png" /><figcaption>Figure 4: TMA result of CF-PEKK consolidated. Measurement perpendicular to the lamination plane.</figcaption></figure>

### Dynamical mechanical analysis (DMA)

Figure 5(a and b) enables the behaviour of the prepreg and consolidated laminate to be compared. As it can be seen on Tan δ (equation (3)) curves there are some differences in the main mechanical relaxation peak (or α-peak). First, $T_α$, the maximum temperature of α peak reach 170 °C for the laminate while it was 166 °C for the prepreg. Furthermore, the two peak magnitudes are very different showing that PEKK matrix amorphous network has undergone important changes between the two states. In addition, when paying attention to storage moduli curve E' it can be noticed that for the prepreg (figure 5a), E' undergoes an increase starting around 200 °C. This recalls the cold crystallization exothermic peak recorded on prepreg by DSC (figure3a). This change in E'- which does not appear for consolidated composite in figure 5b- is linked to cold crystallization.

The second part of this work with DMA was to set up a master curve for CF-PEKK consolidated composite. [90]$_6$ composite samples were submitted to multifrequency test under tension loading. Figure 6 shows the changes in E' storage modulus as function of temperature and frequency (f). Figure 7 give a view of the main mechanical relaxation peak (denoted by α-peak) as a function of frequency. Obtained result enable figures 8a and 8b to be build. They respectively show the change in Log(f) versus 1/T and the master curves for the storage E' and the Loss E'' moduli as a function of frequency. This master curve (equation (4) and (5)) gives value of C1 and C2 WLF constant (see figure8b): $C_1 = 1$ and $C_2 = 5.4$. This representation facilitates the calculation of the activation energy using equation (4), each applicable in distinct temperature ranges. The combined application of these equations yields an activation energy of approximately 2228.84 kJ/mol.
<figure><img src="image_389.png" /></figure>

<figure><img src="image_390.png" /><figcaption>Figure 5: Dynamic mechanical scans on transverse direction for prepreg (a) and consolidated (b) CF-PEKK</figcaption></figure>

<figure><img src="image_391.png" /><figcaption>Figure 6: Multifrequency test from 30 °C to 300 °C with 5 frequencies for CF-PEKK consolidated (Transverse)</figcaption></figure>
<figure><img src="image_400.png" /></figure>

<figure><img src="image_401.png" /><figcaption>Figure 5: Dynamic mechanical scans on transverse direction for prepreg (a) and consolidated (b) CF-PEKK</figcaption></figure>

<figure><img src="image_402.png" /><figcaption>Figure 6: Multifrequency test from 30 °C to 300 °C with 5 frequencies for CF-PEKK consolidated (Transverse)</figcaption></figure>
<figure><img src="image_392.png" /><figcaption>Figure 7: Tan δ at glass transition for CF-PEKK consolidated on transverse. With the shift of Tan δ through different frequencies</figcaption></figure>

<figure><img src="image_393.png" /><figcaption>Figure 8: Activation Energy of Log frequency vs. reciprocal of temperature of peak tanδ (a) and (b) master curve storage and loss moduli at reference temperature of CF-PEKK</figcaption></figure>
<figure><img src="image_403.png" /><figcaption>Figure 7: Tan δ at glass transition for CF-PEKK consolidated on transverse. With the shift of Tan δ through different frequencies</figcaption></figure>

<figure><img src="image_404.png" /><figcaption>Figure 8: Activation Energy of Log frequency vs. reciprocal of temperature of peak tanδ (a) and (b) master curve storage and loss moduli at reference temperature of CF-PEKK</figcaption></figure>
## Conclusion

DSC result to study the prepreg as received and after consolidated by hot press (or Hot press) has been completed and the result of DSC scans has been compared. After the hot press, fiber volume fraction and void content were investigated through acid digestion and optical microscopy. Fibre volume fraction and void content of CF-PEEK is generally matched with several researcher such as [11,16]. Thermomechanical Analysis (TMA) was also used to find the CTE Coefficients. The results are consistent with the literature [15]. Dynamic Mechanical Analysis (DMA) especially in transverse direction from low temperature also studied. Storage modulus, loss modulus and Tan δ relationship has been displayed and discussed. Multifrequency experiment has been conducted in order to construct the master curved by the Time-Temperature superposition (TTS). Williams-Landel-Ferry (WLF equation) and Arrhenius principles can be used to calculate the activation energy. This paper intends to gather the details of the CF-PEKK materials as much as possible. This important result will be used in the manufacturing process of the filament winding process and low velocity impact at low temperature for flat specimens and curved specimens. The CTE in all direction will be determined. As well as the full analysis figure of DMA.

## Acknowledgments

This research was conducted at the Clément Ader Institute (ICA, CNRS UMR 5312) in Toulouse, France. The author expresses sincere gratitude to the Clément Ader Institute for their invaluable experimental and technical support, as well as their generous financial assistance. Additionally, the author extends appreciation to the Embassy of France in Bangkok, specifically through the Franco-Thai Scholarship Program, for their substantial financial support. The collaborative efforts and support from both institutions have played a pivotal role in the successful execution of this research. The authors would like to acknowledge Hexcel Composites France for providing carbon fibre/PEKK matrix prepreg.

## References

[1] Neveu, C. Cornu, P. Olivier, B. Castanié, Manufacturing and impact behaviour of aeronautic overmolded grid-stiffened thermoplastic carbon plates, Composite Structures, Volume 284, 2022, 115228. https://doi.org/10.1016/j.compstruct.2022.115228

[2] Gardner, K. H., Hsiao, B. S., Matheson, R. R., & Wood, B. A. (1992, January). Structure, crystallization and morphology of poly (aryl ether ketone ketone). Polymer, 33(12), 2483-2495. https://doi.org/10.1016/0032-3861(92)91128-o

[3] Choupin, T., Fayolle, B., Régnier, G., Paris, C., Cinquin, J., & Brulé, B. (2018, October). A more reliable DSC-based methodology to study crystallization kinetics: Application to poly(ether ketone ketone) (PEKK) copolymers. Polymer, 155, 109-115. https://doi.org/10.1016/j.polymer.2018.08.060

[4] R. Arquier, I. Iliopoulos, G. Régnier, G. Miquelard-Garnier, Consolidation of continuous-carbon-fiber-reinforced PAEK composites: a review, Materials Today Communications, Volume 32, 2022, 104036, ISSN 2352-4928, https://doi.org/10.1016/j.mtcomm.2022.104036.

[5] Standards, E. (n.d.). EN 2564. https://www.en-standard.eu. https://www.en-standard.eu/csen-2564-aerospace-series-carbon-fibre-laminates-determination-of-the-fibre-resin-and-void-contents/

[6] ISO 1183-1:2019. (2017, September 30). ISO. https://www.iso.org/standard/74990.html

[7] Kim, Y., Choi, C., Kumar, S. K. S., Kim, C. G., Kim, S. W., & Lim, J. H. (2017, November). Thermo-gravimetric analysis method to determine the fiber volume fraction for PAN-based CFRP
## Conclusion

DSC result to study the prepreg as received and after consolidated by hot press (or Hot press) has been completed and the result of DSC scans has been compared. After the hot press, fiber volume fraction and void content were investigated through acid digestion and optical microscopy. Fibre volume fraction and void content of CF-PEEK is generally matched with several researcher such as [11,16]. Thermomechanical Analysis (TMA) was also used to find the CTE Coefficients. The results are consistent with the literature [15]. Dynamic Mechanical Analysis (DMA) especially in transverse direction from low temperature also studied. Storage modulus, loss modulus and Tan δ relationship has been displayed and discussed. Multifrequency experiment has been conducted in order to construct the master curved by the Time-Temperature superposition (TTS). Williams-Landel-Ferry (WLF equation) and Arrhenius principles can be used to calculate the activation energy. This paper intends to gather the details of the CF-PEKK materials as much as possible. This important result will be used in the manufacturing process of the filament winding process and low velocity impact at low temperature for flat specimens and curved specimens. The CTE in all direction will be determined. As well as the full analysis figure of DMA.

## Acknowledgments

This research was conducted at the Clément Ader Institute (ICA, CNRS UMR 5312) in Toulouse, France. The author expresses sincere gratitude to the Clément Ader Institute for their invaluable experimental and technical support, as well as their generous financial assistance. Additionally, the author extends appreciation to the Embassy of France in Bangkok, specifically through the Franco-Thai Scholarship Program, for their substantial financial support. The collaborative efforts and support from both institutions have played a pivotal role in the successful execution of this research. The authors would like to acknowledge Hexcel Composites France for providing carbon fibre/PEKK matrix prepreg.

## References

[1] Neveu, C. Cornu, P. Olivier, B. Castanié, Manufacturing and impact behaviour of aeronautic overmolded grid-stiffened thermoplastic carbon plates, Composite Structures, Volume 284, 2022, 115228. https://doi.org/10.1016/j.compstruct.2022.115228

[2] Gardner, K. H., Hsiao, B. S., Matheson, R. R., & Wood, B. A. (1992, January). Structure, crystallization and morphology of poly (aryl ether ketone ketone). Polymer, 33(12), 2483-2495. https://doi.org/10.1016/0032-3861(92)91128-o

[3] Choupin, T., Fayolle, B., Régnier, G., Paris, C., Cinquin, J., & Brulé, B. (2018, October). A more reliable DSC-based methodology to study crystallization kinetics: Application to poly(ether ketone ketone) (PEKK) copolymers. Polymer, 155, 109-115. https://doi.org/10.1016/j.polymer.2018.08.060

[4] R. Arquier, I. Iliopoulos, G. Régnier, G. Miquelard-Garnier, Consolidation of continuous-carbon-fiber-reinforced PAEK composites: a review, Materials Today Communications, Volume 32, 2022, 104036, ISSN 2352-4928, https://doi.org/10.1016/j.mtcomm.2022.104036.

[5] Standards, E. (n.d.). EN 2564. https://www.en-standard.eu. https://www.en-standard.eu/csen-2564-aerospace-series-carbon-fibre-laminates-determination-of-the-fibre-resin-and-void-contents/

[6] ISO 1183-1:2019. (2017, September 30). ISO. https://www.iso.org/standard/74990.html

[7] Kim, Y., Choi, C., Kumar, S. K. S., Kim, C. G., Kim, S. W., & Lim, J. H. (2017, November). Thermo-gravimetric analysis method to determine the fiber volume fraction for PAN-based CFRP
considering oxidation of carbon fiber and matrix. Composites Part A: Applied Science and Manufacturing, 102, 40-47. https://doi.org/10.1016/j.compositesa.2017.07.024

[8] Cann, M. T., Adams, D. O., & Schneider, C. L. (2008, January 10). Characterization of Fiber Volume Fraction Gradients in Composite Laminates. Journal of Composite Materials, 42(5), 447-466. https://doi.org/10.1177/0021998307086206

[9] Blundell, D., & Osborn, B. (1983, August). The morphology of poly(aryl-ether-ether-ketone). Polymer, 24(8), 953-958. https://doi.org/10.1016/0032-3861(83)90144-1

[10] Woo, E.M., Seferis, J.C. and Schaffnit, R.S. (1991), Viscoelastic characterization of high-performance epoxy matrix composites. Polym Compos, 12: 273-280. https://doi.org/10.1002/pc.750120408

[11] Gerard, J.F., Andrews, S.J. and Macosko, C.W. (1990), Dynamic mechanical measurements: Comparison between bending and torsion methods on a graphite-reinforced and a rubber-modified epoxy. Polym Compos, 11: 90-97. https://doi.org/10.1002/pc.750110204

[12] Raphaël Arquier. Etude des phénomènes physico-chimiques en jeu lors de la consolidation hors autoclave de composites PEKK/FC. http://www.theses.fr/2023HESAE017

[13] Quiroga Cortés, L., Caussé, N., Dantras, E., Lonjon, A., & Lacabanne, C. (2016, February 2). Morphology and dynamical mechanical properties of poly ether ketone ketone (PEKK) with meta phenyl links. Journal of Applied Polymer Science, 133(19), https://doi.org/10.1002/app.43396

[14] Leo, Tanner M., "Characterization of Carbon Fiber PEKK Thermoplastic Prepreg for Process Modeling"(2022). WWU Honors College Senior Projects. 616. https://cedar.wwu.edu/wwu_honors/616

[15] Anna Maria El Bayssari (07 septembre 2022). Etude de l'état des contraintes et des déformations résiduelles dans les composites à matrices thermoplastiques fabriqués par dépose de bandes. https://www.theses.fr/2022NANU4035

[16] Hoang V-T, Kwon B-S, Sung J-W, et al. Postprocessing method-induced mechanical properties of carbon fiber-reinforced thermoplastic composites. Journal of Thermoplastic Composite Materials. 2023;36(1):432-447. https://doi.org/10.1177/0892705720945376
considering oxidation of carbon fiber and matrix. Composites Part A: Applied Science and Manufacturing, 102, 40-47. https://doi.org/10.1016/j.compositesa.2017.07.024

[8] Cann, M. T., Adams, D. O., & Schneider, C. L. (2008, January 10). Characterization of Fiber Volume Fraction Gradients in Composite Laminates. Journal of Composite Materials, 42(5), 447-466. https://doi.org/10.1177/0021998307086206

[9] Blundell, D., & Osborn, B. (1983, August). The morphology of poly(aryl-ether-ether-ketone). Polymer, 24(8), 953-958. https://doi.org/10.1016/0032-3861(83)90144-1

[10] Woo, E.M., Seferis, J.C. and Schaffnit, R.S. (1991), Viscoelastic characterization of high-performance epoxy matrix composites. Polym Compos, 12: 273-280. https://doi.org/10.1002/pc.750120408

[11] Gerard, J.F., Andrews, S.J. and Macosko, C.W. (1990), Dynamic mechanical measurements: Comparison between bending and torsion methods on a graphite-reinforced and a rubber-modified epoxy. Polym Compos, 11: 90-97. https://doi.org/10.1002/pc.750110204

[12] Raphaël Arquier. Etude des phénomènes physico-chimiques en jeu lors de la consolidation hors autoclave de composites PEKK/FC. http://www.theses.fr/2023HESAE017

[13] Quiroga Cortés, L., Caussé, N., Dantras, E., Lonjon, A., & Lacabanne, C. (2016, February 2). Morphology and dynamical mechanical properties of poly ether ketone ketone (PEKK) with meta phenyl links. Journal of Applied Polymer Science, 133(19), https://doi.org/10.1002/app.43396

[14] Leo, Tanner M., "Characterization of Carbon Fiber PEKK Thermoplastic Prepreg for Process Modeling"(2022). WWU Honors College Senior Projects. 616. https://cedar.wwu.edu/wwu_honors/616

[15] Anna Maria El Bayssari (07 septembre 2022). Etude de l'état des contraintes et des déformations résiduelles dans les composites à matrices thermoplastiques fabriqués par dépose de bandes. https://www.theses.fr/2022NANU4035

[16] Hoang V-T, Kwon B-S, Sung J-W, et al. Postprocessing method-induced mechanical properties of carbon fiber-reinforced thermoplastic composites. Journal of Thermoplastic Composite Materials. 2023;36(1):432-447. https://doi.org/10.1177/0892705720945376
# Rheological characterisation and modelling of a glass mat reinforced thermoplastic for the simulation of compression moulding

ALTHAUS Philipp¹,ᵃ*, WESTER Hendrik¹,ᵇ, ROSENBUSCH Daniel¹,ᶜ and
BEHRENS Bernd-Arno¹,ᵈ

¹Institute of Forming Technology and Machines, Leibniz Universität Hannover, An der Universität 2, 30823 Garbsen, Germany

ᵃalthaus@ifum.uni-hannover.de, ᵇwester@ifum.uni-hannover.de,

ᶜrosenbusch@ifum.uni-hannover.de, ᵈbehrens@ifum.uni-hannover.de

**Keywords:** Glass Mat Reinforced Thermoplastic, Material Characterisation, Squeeze Flow

**Abstract.** The use of hybrid components in the automotive industry is steadily increasing due to their lightweight potential. By combining fibre-reinforced plastics with metallic materials, high-strength components with lower weight than monolithic metal parts can be realised. The overall aim of the project "HyFiVe" is to exploit this potential for electric vehicles by developing a scaled battery housing structure made of a glass mat reinforced thermoplastic (GMT) paired with unidirectional reinforced (UD) tapes and a metallic reinforcement frame. The GMT is formed by compression moulding and serves as the base of the battery housing structure. Numerical simulation is an efficient tool for process design that can determine a suitable process window and reduce experimental trial-and-error tests. Particularly, realistic modelling of the GMT flow behaviour is essential for reliable simulation results. In this contribution, the rheological properties of a GMT consisting of a polyamide 6 (PA6) matrix with 30% glass fibre reinforcement were determined. Isothermal compression tests were carried out with a parallel plate rheometer at different temperatures and varying squeeze rates. The squeeze force and punch displacement were evaluated to determine the rheological data of the GMT. Two methods for the modeling of the flow behaviour were considered. At first, pure shear flow was assumed and the viscosity was modelled as a function of the shear rate by means of a power-law. Secondly, a pure biaxial extension was assumed and the true stress was modelled in dependence of true strain and strain rate. Subsequently, for a verification of the material models, the compression tests were simulated in ABAQUS using the Coupled Eulerian-Lagrange (CEL) approach and the results were compared to the experiments.

**Introduction**

Due to the high demand for lightweight construction, conventional materials are increasingly substituted by materials with higher strength. The utilisation of fibre-reinforced plastics (FRP) plays a decisive role in this context [1]. FRP with a thermoplastic matrix are available as pre-impregnated semi-finished products in the form of long-fibre-reinforced thermoplastics (LFT) or glass mat-reinforced thermoplastics (GMT). The latter consists of a fibre mat with randomly arranged continuous fibres, which are impregnated with the matrix material. GMT is characterised by a high specific strength and specific energy absorption capabilities, which makes it particularly suitable for lightweight components in the automotive sector [2]. Application examples include bumper beams, spare wheel wells and battery trays. Further advantages are recyclability and noise cancellation [3]. GMT is also characterised by comparatively simple processing, as forming is usually carried out by compression moulding using standard hydraulic presses. In addition to simple forming, GMT can be used in combined processes. Behrens et al. developed a thermoforming process for the combined forming of organic sheet and GMT [4]. The GMT was

<figure><img src="image_394.png" /></figure>
# Rheological characterisation and modelling of a glass mat reinforced thermoplastic for the simulation of compression moulding

ALTHAUS Philipp¹,ᵃ*, WESTER Hendrik¹,ᵇ, ROSENBUSCH Daniel¹,ᶜ and
BEHRENS Bernd-Arno¹,ᵈ

¹Institute of Forming Technology and Machines, Leibniz Universität Hannover, An der Universität 2, 30823 Garbsen, Germany

ᵃalthaus@ifum.uni-hannover.de, ᵇwester@ifum.uni-hannover.de,

ᶜrosenbusch@ifum.uni-hannover.de, ᵈbehrens@ifum.uni-hannover.de

**Keywords:** Glass Mat Reinforced Thermoplastic, Material Characterisation, Squeeze Flow

**Abstract.** The use of hybrid components in the automotive industry is steadily increasing due to their lightweight potential. By combining fibre-reinforced plastics with metallic materials, high-strength components with lower weight than monolithic metal parts can be realised. The overall aim of the project "HyFiVe" is to exploit this potential for electric vehicles by developing a scaled battery housing structure made of a glass mat reinforced thermoplastic (GMT) paired with unidirectional reinforced (UD) tapes and a metallic reinforcement frame. The GMT is formed by compression moulding and serves as the base of the battery housing structure. Numerical simulation is an efficient tool for process design that can determine a suitable process window and reduce experimental trial-and-error tests. Particularly, realistic modelling of the GMT flow behaviour is essential for reliable simulation results. In this contribution, the rheological properties of a GMT consisting of a polyamide 6 (PA6) matrix with 30% glass fibre reinforcement were determined. Isothermal compression tests were carried out with a parallel plate rheometer at different temperatures and varying squeeze rates. The squeeze force and punch displacement were evaluated to determine the rheological data of the GMT. Two methods for the modeling of the flow behaviour were considered. At first, pure shear flow was assumed and the viscosity was modelled as a function of the shear rate by means of a power-law. Secondly, a pure biaxial extension was assumed and the true stress was modelled in dependence of true strain and strain rate. Subsequently, for a verification of the material models, the compression tests were simulated in ABAQUS using the Coupled Eulerian-Lagrange (CEL) approach and the results were compared to the experiments.

**Introduction**

Due to the high demand for lightweight construction, conventional materials are increasingly substituted by materials with higher strength. The utilisation of fibre-reinforced plastics (FRP) plays a decisive role in this context [1]. FRP with a thermoplastic matrix are available as pre-impregnated semi-finished products in the form of long-fibre-reinforced thermoplastics (LFT) or glass mat-reinforced thermoplastics (GMT). The latter consists of a fibre mat with randomly arranged continuous fibres, which are impregnated with the matrix material. GMT is characterised by a high specific strength and specific energy absorption capabilities, which makes it particularly suitable for lightweight components in the automotive sector [2]. Application examples include bumper beams, spare wheel wells and battery trays. Further advantages are recyclability and noise cancellation [3]. GMT is also characterised by comparatively simple processing, as forming is usually carried out by compression moulding using standard hydraulic presses. In addition to simple forming, GMT can be used in combined processes. Behrens et al. developed a thermoforming process for the combined forming of organic sheet and GMT [4]. The GMT was

<figure><img src="image_405.png" /></figure>
used to integrate complex geometric elements into the component. As the forming could be carried out in one stroke, no additional process steps were necessary. The development of a combined forming process is also the objective of the project "HyFiVe", in which a battery housing structure is formed from GMT, UD tapes and a metallic reinforcement frame. The GMT serves as the basic structure holding the battery modules and cables [5]. The application of numerical simulation methods is essential for efficient process design, as it allows a process window to be identified that ensures the production of good parts. As a result, trial and error testing can be reduced, saving costs, time and materials. However, numerical process design requires detailed knowledge of the material behaviour to obtain realistic simulation results. Especially the rheological data of GMT has to be characterised and modelled to simulate the flow behaviour in the mould cavity.

In contrast to short-fibre-reinforced plastics, the rheological data of GMT cannot be measured in a capillary rheometer, as this would require the semi-finished product to be trimmed, which would shorten the fibres. For this reason, the use of a parallel plate rheometer has been established in literature [6]. With this procedure, a circular specimen of the material is compressed between two parallel plates at a constant squeeze rate and temperature up to a defined final thickness, with the polymer melt flowing in radial direction. During the test, the applied squeeze force and displacement are recorded in order to determine the rheological properties of the material. The test is characterised by its simplicity and is very similar to the compression moulding process, which explains why it is widely used in literature to characterise thermoplastic melts with long fibres. The incompressibility of the material and therefore a constant volume is usually assumed for the evaluation. When modelling the flow behaviour, a distinction is made between two extreme cases. In the first case, which is shown in Fig. 1 a), pure shear flow is assumed. In this case, pure sticking of the material to the tool surface is assumed. In the second flow form, which is shown in Fig. 1 b), a pure biaxial extension of the material is assumed, which is often referred to as a “plug flow”. In this case, the material slips completely off the tool surface.

<figure><img src="image_395.png" /><figcaption>Fig. 1. Pure shear flow a) and pure biaxial extension b) in a parallel plate rheometer.</figcaption></figure>

The first mathematical modelling approach of the material flow in the parallel plate rheometer were published by Scott, who investigated the physical relationships [7]. Under the assumption of a pure shear flow (Fig. 1 a), the squeeze force $F_{Shear}$, which is necessary to move the plate at a constant velocity $u$, can be determined using the Scott equation.

$$F_{Shear} = \left(\frac{u}{4}\right)^n \left(\frac{2n+1}{n}\right)^n \left(\frac{2}{h}\right)^2 \frac{K\pi R^{n+3}}{(n+3)} \quad (1)$$

Here, h stands for the plate distance or the current height of the specimen and R for the radius of the specimen. K and n are the coefficients of a power law that defines the shear viscosity $\eta_{Shear}$ as a function of the shear rate $\dot{\gamma}$.

$$\eta_{Shear} = K \dot{\gamma}^{n-1} \quad (2)$$
used to integrate complex geometric elements into the component. As the forming could be carried out in one stroke, no additional process steps were necessary. The development of a combined forming process is also the objective of the project "HyFiVe", in which a battery housing structure is formed from GMT, UD tapes and a metallic reinforcement frame. The GMT serves as the basic structure holding the battery modules and cables [5]. The application of numerical simulation methods is essential for efficient process design, as it allows a process window to be identified that ensures the production of good parts. As a result, trial and error testing can be reduced, saving costs, time and materials. However, numerical process design requires detailed knowledge of the material behaviour to obtain realistic simulation results. Especially the rheological data of GMT has to be characterised and modelled to simulate the flow behaviour in the mould cavity.

In contrast to short-fibre-reinforced plastics, the rheological data of GMT cannot be measured in a capillary rheometer, as this would require the semi-finished product to be trimmed, which would shorten the fibres. For this reason, the use of a parallel plate rheometer has been established in literature [6]. With this procedure, a circular specimen of the material is compressed between two parallel plates at a constant squeeze rate and temperature up to a defined final thickness, with the polymer melt flowing in radial direction. During the test, the applied squeeze force and displacement are recorded in order to determine the rheological properties of the material. The test is characterised by its simplicity and is very similar to the compression moulding process, which explains why it is widely used in literature to characterise thermoplastic melts with long fibres. The incompressibility of the material and therefore a constant volume is usually assumed for the evaluation. When modelling the flow behaviour, a distinction is made between two extreme cases. In the first case, which is shown in Fig. 1 a), pure shear flow is assumed. In this case, pure sticking of the material to the tool surface is assumed. In the second flow form, which is shown in Fig. 1 b), a pure biaxial extension of the material is assumed, which is often referred to as a “plug flow”. In this case, the material slips completely off the tool surface.

<figure><img src="image_406.png" /><figcaption>Fig. 1. Pure shear flow a) and pure biaxial extension b) in a parallel plate rheometer.</figcaption></figure>

The first mathematical modelling approach of the material flow in the parallel plate rheometer were published by Scott, who investigated the physical relationships [7]. Under the assumption of a pure shear flow (Fig. 1 a), the squeeze force $F_{Shear}$, which is necessary to move the plate at a constant velocity $u$, can be determined using the Scott equation.

$$F_{Shear} = \left(\frac{u}{4}\right)^n \left(\frac{2n+1}{n}\right)^n \left(\frac{2}{h}\right)^2 \frac{K\pi R^{n+3}}{(n+3)} \quad (1)$$

Here, $h$ stands for the plate distance or the current height of the specimen and $R$ for the radius of the specimen. $K$ and $n$ are the coefficients of a power law that defines the shear viscosity $\eta_{Shear}$ as a function of the shear rate $\dot{\gamma}$.

$$\eta_{Shear} = K \dot{\gamma}^{n-1} \quad (2)$$
Under the assumption of a pure biaxial extension of the material (Fig. 1 b), the required squeeze force $F_{ext}$ can be determined according to Kotsikos using Eq. 3 [8].

$$F_{ext} = \pi R^2 A \left(\frac{u}{h}\right)^m \qquad (3)$$

Accordingly, the coefficients A and m can be used to calculate the extensional viscosity $\eta_{ext}$ of the material as a function of the strain rate $\dot{\epsilon}$.

$$\eta_{ext} = A \dot{\epsilon}^{m-1} \qquad (4)$$

However, Kotsikos found that the coefficient A is not constant, but depends on the strain [8]. To take the strain into account, the biaxial extension can also be modelled using the true stress $\sigma$ as a function of the true strain $\epsilon$ and strain rate $\dot{\epsilon}$ [9].

$$\sigma = \frac{F_{ext}}{A} = \frac{F_{ext}}{\frac{A_0 h_0}{h}} = \frac{F_{ext}h}{\pi R^2 h_0}, \quad \varepsilon = \ln\left(\frac{h}{h_0}\right), \quad \dot{\varepsilon} = \frac{u}{h} \qquad (5)$$

Using this method, the extensional viscosity can be calculated according to the relationship in Eq. 6 [9].

$$\eta_{ext} = \frac{\sigma}{\dot{\epsilon}} \qquad (6)$$

Kotsikos et al. carried out isothermal compression tests in a parallel plate rheometer with circular specimen made of GMT with polypropylene (PP) as matrix material [8]. The material flow was modelled both as pure shear flow and pure biaxial extension. As a result, the assumption of a pure shear flow led to inadequate results regarding the predicted squeeze forces, which were significantly underestimated compared to the experimental results. In contrast, the assumption of a pure extensional flow provided better agreement with experimental results. Dweib and Ó Brádaigh also carried out compression tests with GMT made of PP [10]. The originally circular specimen showed an elliptical shape after forming. Due to the anisotropy, two viscosity models were modelled depending on the main flow directions under the assumption of a pure extensional flow. Scanning electron microscopy images showed that more fibres were arranged along one main fibre direction during forming. A significant anisotropy of the viscosity of GMT was also found by Dörr et al., which was attributed to a predominantly in-plane fibre orientation [11]. By taking the anisotropy in the material modelling into account, a higher prediction accuracy could be achieved in the numerical simulation, as it was possible to predict wrinkling during forming. Thattaiparthasarthy et al. carried out isothermal compression tests with a long-fibre-reinforced thermoplastic made of PP and investigated the effects of temperature, fibre length and fibre weight fraction on the viscosities [6]. An increase in temperature and a decrease in fibre length and fibre weight fraction led to a reduction of the viscosity. Huang et al. used a parallel plate rheometer to analyse GMT with PA6 as matrix material [12]. For the modelling, a pure shear flow was assumed and the viscosity was modelled as a function of the shear rate. The material data was used to simulate the compression tests in Moldex3D. However, the resulting squeeze force was significantly underestimated in the simulation. The separation of fibre and matrix was stated as the reason for the deviations. Behrens et al. also characterised GMT with PA6 as matrix material. They assumed a biaxial extension of the material and modelled the flow by means of true stress as a function of true strain and strain rate [13]. In a numerical simulation, good agreement was achieved regarding the predicted material flow and the pressure distribution compared to the experiments. The objective of this contribution is the characterisation of a GMT with a PA6 matrix using a
Under the assumption of a pure biaxial extension of the material (Fig. 1 b), the required squeeze force $F_{ext}$ can be determined according to Kotsikos using Eq. 3 [8].

$$F_{ext} = \pi R^2 A \left(\frac{u}{h}\right)^m \qquad (3)$$

Accordingly, the coefficients A and m can be used to calculate the extensional viscosity $\eta_{ext}$ of the material as a function of the strain rate $\dot{\epsilon}$.

$$\eta_{ext} = A \dot{\epsilon}^{m-1} \qquad (4)$$

However, Kotsikos found that the coefficient A is not constant, but depends on the strain [8]. To take the strain into account, the biaxial extension can also be modelled using the true stress $\sigma$ as a function of the true strain $\epsilon$ and strain rate $\dot{\epsilon}$ [9].

$$\sigma = \frac{F_{ext}}{A} = \frac{F_{ext}}{\frac{A_0 h_0}{h}} = \frac{F_{ext}h}{\pi R^2 h_0}, \quad \epsilon = \ln\left(\frac{h}{h_0}\right), \quad \dot{\epsilon} = \frac{u}{h} \qquad (5)$$

Using this method, the extensional viscosity can be calculated according to the relationship in Eq. 6 [9].

$$\eta_{ext} = \frac{\sigma}{\dot{\epsilon}} \qquad (6)$$

Kotsikos et al. carried out isothermal compression tests in a parallel plate rheometer with circular specimen made of GMT with polypropylene (PP) as matrix material [8]. The material flow was modelled both as pure shear flow and pure biaxial extension. As a result, the assumption of a pure shear flow led to inadequate results regarding the predicted squeeze forces, which were significantly underestimated compared to the experimental results. In contrast, the assumption of a pure extensional flow provided better agreement with experimental results. Dweib and Ó Brádaigh also carried out compression tests with GMT made of PP [10]. The originally circular specimen showed an elliptical shape after forming. Due to the anisotropy, two viscosity models were modelled depending on the main flow directions under the assumption of a pure extensional flow. Scanning electron microscopy images showed that more fibres were arranged along one main fibre direction during forming. A significant anisotropy of the viscosity of GMT was also found by Dörr et al., which was attributed to a predominantly in-plane fibre orientation [11]. By taking the anisotropy in the material modelling into account, a higher prediction accuracy could be achieved in the numerical simulation, as it was possible to predict wrinkling during forming. Thattaiparthasarthy et al. carried out isothermal compression tests with a long-fibre-reinforced thermoplastic made of PP and investigated the effects of temperature, fibre length and fibre weight fraction on the viscosities [6]. An increase in temperature and a decrease in fibre length and fibre weight fraction led to a reduction of the viscosity. Huang et al. used a parallel plate rheometer to analyse GMT with PA6 as matrix material [12]. For the modelling, a pure shear flow was assumed and the viscosity was modelled as a function of the shear rate. The material data was used to simulate the compression tests in Moldex3D. However, the resulting squeeze force was significantly underestimated in the simulation. The separation of fibre and matrix was stated as the reason for the deviations. Behrens et al. also characterised GMT with PA6 as matrix material. They assumed a biaxial extension of the material and modelled the flow by means of true stress as a function of true strain and strain rate [13]. In a numerical simulation, good agreement was achieved regarding the predicted material flow and the pressure distribution compared to the experiments. The objective of this contribution is the characterisation of a GMT with a PA6 matrix using a
parallel plate rheometer to model the material flow in the simulation of compression moulding. Since different assumptions and thus modelling approaches can be found in literature [4-8], both a pure shear flow and a pure extensional flow are assumed for the evaluation of the rheological data. Subsequently, the modelling methods are used in a numerical simulation of the compression tests to identify the approach with the highest prediction accuracy. Due to the high deformations that occur during compression moulding, the CEL approach is used for the simulation.

## Experimental Test Setup

For the compression tests, a parallel plate rheometer made of C45 with a diameter of 200 mm was used (Fig. 2). The characterised material was a GMT consisting of a PA6 matrix and a glass fibre reinforcement with a volume fraction of 30%. The glass fibres had an average length of 50 mm and where randomly oriented. The material was provided by Mitsubishi Chemical Advanced Materials AG in the form of sheets with a thickness of 4.4 mm. Circular specimens with a diameter of 50 mm were prepared by water jet cutting. The tests were carried out isothermally at process-relevant temperatures of 240 °C, 260 °C and 280 °C. Therefore, the plates were heated to the corresponding temperatures by means of integrated heating coils.

<figure><img src="image_396.png" /><figcaption>Fig. 2. Experimental setup for the compression tests.</figcaption></figure>

Three circular specimens were placed on top of each other between the tool plates, which were then brought into contact. The number of specimens and thus the total height of 13.2 mm was chosen based on the planned layer setup in the later compression moulding tests on industrial scale. Analogous to [12], an aluminium foil was inserted between the samples and the parallel plates, to prevent the GMT from sticking to the tool surfaces. After insertion, the specimens were heated to the corresponding test temperature by conduction through the plates. The required heating time was determined beforehand using thermocouples, which were inserted between the GMT specimen during heating. After the required holding time was reached, the plates were moved together at a constant squeeze rate to a minimum tool gap of 2.7 mm, whereby the GMT flowed in radial direction. Four squeeze rates were applied (0.02 mm/s, 1 mm/s, 5 mm/s, 25 mm/s) and each test was performed three times. During the tests, the squeeze force and punch position were recorded to determine the rheological properties of the GMT in the following sections.
parallel plate rheometer to model the material flow in the simulation of compression moulding. Since different assumptions and thus modelling approaches can be found in literature [4-8], both a pure shear flow and a pure extensional flow are assumed for the evaluation of the rheological data. Subsequently, the modelling methods are used in a numerical simulation of the compression tests to identify the approach with the highest prediction accuracy. Due to the high deformations that occur during compression moulding, the CEL approach is used for the simulation.

## Experimental Test Setup

For the compression tests, a parallel plate rheometer made of C45 with a diameter of 200 mm was used (Fig. 2). The characterised material was a GMT consisting of a PA6 matrix and a glass fibre reinforcement with a volume fraction of 30%. The glass fibres had an average length of 50 mm and where randomly oriented. The material was provided by Mitsubishi Chemical Advanced Materials AG in the form of sheets with a thickness of 4.4 mm. Circular specimens with a diameter of 50 mm were prepared by water jet cutting. The tests were carried out isothermally at process-relevant temperatures of 240 °C, 260 °C and 280 °C. Therefore, the plates were heated to the corresponding temperatures by means of integrated heating coils.

<figure><img src="image_407.png" /><figcaption>Fig. 2. Experimental setup for the compression tests.</figcaption></figure>

Three circular specimens were placed on top of each other between the tool plates, which were then brought into contact. The number of specimens and thus the total height of 13.2 mm was chosen based on the planned layer setup in the later compression moulding tests on industrial scale. Analogous to [12], an aluminium foil was inserted between the samples and the parallel plates, to prevent the GMT from sticking to the tool surfaces. After insertion, the specimens were heated to the corresponding test temperature by conduction through the plates. The required heating time was determined beforehand using thermocouples, which were inserted between the GMT specimen during heating. After the required holding time was reached, the plates were moved together at a constant squeeze rate to a minimum tool gap of 2.7 mm, whereby the GMT flowed in radial direction. Four squeeze rates were applied (0.02 mm/s, 1 mm/s, 5 mm/s, 25 mm/s) and each test was performed three times. During the tests, the squeeze force and punch position were recorded to determine the rheological properties of the GMT in the following sections.
## Experimental Results

Fig. 3 shows the measured squeeze forces over the punch displacement for the investigated squeeze rates and temperatures. At the beginning of the compression process, only very low squeeze forces occurred. Subsequently, after a displacement of approx. 2 mm, the squeeze force increases almost linearly during the compression test. From a stroke of approx. 8 mm, the gradient increases exponentially due to the compaction of the fibre bed until it reaches its maximum at a displacement of 10.5 mm [14]. It can be seen that the required forming force increases with higher squeeze rates. Furthermore, higher forming temperatures lead to a decrease of the measured squeeze forces. The geometries of the specimen are shown in Fig. 3 b) before and after forming for two experimental tests. It can be seen, that the shape after forming remains approx. circular, so that an isotropic flow behaviour is assumed in the material modelling.

<figure><img src="image_397.png" /><figcaption>Fig. 3. Squeeze force over punch displacement for the investigated squeeze rates and temperatures a) and specimen before and after forming b).</figcaption></figure>

After the compression tests, some of the specimens were cut and analysed with the digital measuring microscope VR-3200 from Keyence to obtain an indication of the shape of the flow front after forming. The sectional views in Fig. 3 b) show, that the flow fronts have nearly straight edges, which indicates a slip at the tool surface and thus a pure biaxial extension of the specimen. But it must be taken into account, that the GMT was still molten when the tool plates were opened, so that slight changes in the shape of the flow fronts may have occurred after the forming.

## Parameter Fit for Shear Flow

Firstly, the compression tests are analysed assuming a pure shear flow. For this purpose, Scott's equation (Eq. 1) is used to determine the coefficients K and n for the calculation of the shear viscosity. Therefore, Eq. 1 is logarithmised to obtain Eq. 7.

$$ \log(F_{\text{Shear}}) = n \log\left(\frac{u}{4}\right) + \log\left\{\left(\frac{2n+1}{n}\right)^n \left(\frac{2}{h}\right)^2 \frac{K\pi R^{n+3}}{(n+3)}\right\} \quad (7) $$
## Experimental Results

Fig. 3 shows the measured squeeze forces over the punch displacement for the investigated squeeze rates and temperatures. At the beginning of the compression process, only very low squeeze forces occurred. Subsequently, after a displacement of approx. 2 mm, the squeeze force increases almost linearly during the compression test. From a stroke of approx. 8 mm, the gradient increases exponentially due to the compaction of the fibre bed until it reaches its maximum at a displacement of 10.5 mm [14]. It can be seen that the required forming force increases with higher squeeze rates. Furthermore, higher forming temperatures lead to a decrease of the measured squeeze forces. The geometries of the specimen are shown in Fig. 3 b) before and after forming for two experimental tests. It can be seen, that the shape after forming remains approx. circular, so that an isotropic flow behaviour is assumed in the material modelling.

<figure><img src="image_408.png" /><figcaption>Fig. 3. Squeeze force over punch displacement for the investigated squeeze rates and temperatures a) and specimen before and after forming b).</figcaption></figure>

After the compression tests, some of the specimens were cut and analysed with the digital measuring microscope VR-3200 from Keyence to obtain an indication of the shape of the flow front after forming. The sectional views in Fig. 3 b) show, that the flow fronts have nearly straight edges, which indicates a slip at the tool surface and thus a pure biaxial extension of the specimen. But it must be taken into account, that the GMT was still molten when the tool plates were opened, so that slight changes in the shape of the flow fronts may have occurred after the forming.

## Parameter Fit for Shear Flow

Firstly, the compression tests are analysed assuming a pure shear flow. For this purpose, Scott's equation (Eq. 1) is used to determine the coefficients K and n for the calculation of the shear viscosity. Therefore, Eq. 1 is logarithmised to obtain Eq. 7.

$$ \log(F_{\text{Shear}}) = n \log\left(\frac{u}{4}\right) + \log\left\{\left(\frac{2n+1}{n}\right)^n \left(\frac{2}{h}\right)^2 \frac{K\pi R^{n+3}}{(n+3)}\right\} \quad (7) $$
Subsequently, a plot of log($F_{\text{Shear}}$) over log(u/4) is created, which is shown in Fig. 4 a) as an example for various plate distances at 260 °C. From the diagram, the power law coefficient n can be determined as the gradient of the curves and B from the intersection point with the y-axis. It can be seen, that the coefficients vary with the plate distance h. Therefore, averaged values were calculated for each temperature, which are summarised in Table 1. In Fig. 4 b), the resulting shear viscosity are shown as a function of the shear rate for the investigated temperatures. It can be seen that the shear viscosity of the GMT decreases with increasing shear rate and temperature.

<table><caption>Table 1: Power law coefficient for the shear viscosity.</caption><thead><tr><td>Temperature in °C</td><td>K in Pa s</td><td>n</td></tr></thead><tbody><tr><td>240</td><td>132391.97</td><td>0.564</td></tr><tr><td>260</td><td>77917.00</td><td>0.512</td></tr><tr><td>280</td><td>48583.70</td><td>0.470</td></tr></tbody></table>

<figure><img src="image_398.png" /><figcaption>Fig. 4. Logarithmic plot for the determination of the power law coefficients a) and resulting shear viscosity over shear rate b).</figcaption></figure>

## Parameter Fit for Biaxial Extension

Secondly, a pure biaxial extension of the GMT was assumed and the true stress, true strain and strain rate were determined using Eq. 5. In Fig. 5, the true stress is shown as a function of true strain and strain rate for the investigated squeeze rates and temperatures. It can be seen that the stress increases with increasing strain and strain rate. In order to use the true stress-strain curves as an input for the numerical simulation, the curves must be known for a constant strain rate. This requires the modelling of the test results. For this purpose, a 4th degree polynomial approach was fitted for each temperature using the Curve Fitting Toolbox from Matlab. The fitted function with the corresponding coefficients and the coefficient of determination are given in Eq. 8 and Table 2.

$$ \sigma = p_{00} + p_{10} \cdot \varepsilon + p_{01} \cdot \dot{\varepsilon} + p_{20} \cdot \varepsilon^2 + p_{11} \cdot \varepsilon \cdot \dot{\varepsilon} + p_{02} \cdot \dot{\varepsilon}^2 + p_{30} \cdot \varepsilon^3 + p_{21} \cdot \varepsilon^2 \cdot \dot{\varepsilon} + p_{12} \cdot \varepsilon \cdot \dot{\varepsilon}^2 + p_{03} \cdot \dot{\varepsilon}^3 + p_{40} \cdot \varepsilon^4 + p_{31} \cdot \varepsilon^3 \cdot \dot{\varepsilon} + p_{22} \cdot \varepsilon^2 \cdot \dot{\varepsilon}^2 + p_{13} \cdot \varepsilon \cdot \dot{\varepsilon}^3 \quad (8) $$

<table><caption>Table 2: Modell coefficients and R² for biaxial extension.</caption><thead><tr><td></td><td>p<sub>00</sub></td><td>p<sub>10</sub></td><td>p<sub>01</sub></td><td>p<sub>20</sub></td><td>p<sub>11</sub></td><td>p<sub>02</sub></td><td>p<sub>30</sub></td><td>p<sub>21</sub></td><td>p<sub>12</sub></td><td>p<sub>03</sub></td><td>p<sub>40</sub></td><td>p<sub>31</sub></td><td>p<sub>22</sub></td><td>p<sub>13</sub></td><td>R<sup>2</sup></td></tr></thead><tbody><tr><td>240°C</td><td>-0.143</td><td>2.079</td><td>0.094</td><td>-3.050</td><td>4.839</td><td>-0.028</td><td>1.479</td><td>-3.501</td><td>-0.681</td><td>0.003</td><td>-0.195</td><td>1.613</td><td>-0.215</td><td>0.100</td><td>99.29%</td></tr><tr><td>260°C</td><td>1.554</td><td>1.075</td><td>1.183</td><td>-0.451</td><td>0.615</td><td>-0.333</td><td>0.150</td><td>-0.423</td><td>-0.472</td><td>0.272</td><td>0.025</td><td>0.173</td><td>-0.154</td><td>0.275</td><td>99.53%</td></tr><tr><td>280°C</td><td>-0.012</td><td>0.210</td><td>-0.039</td><td>1.060</td><td>1.863</td><td>0.013</td><td>-1.614</td><td>-1.327</td><td>-0.179</td><td>-0.001</td><td>0.596</td><td>0.633</td><td>-0.067</td><td>0.024</td><td>99.64%</td></tr></tbody></table>
Subsequently, a plot of log($F_{\text{Shear}}$) over log(u/4) is created, which is shown in Fig. 4 a) as an example for various plate distances at 260 °C. From the diagram, the power law coefficient n can be determined as the gradient of the curves and B from the intersection point with the y-axis. It can be seen, that the coefficients vary with the plate distance h. Therefore, averaged values were calculated for each temperature, which are summarised in Table 1. In Fig. 4 b), the resulting shear viscosity are shown as a function of the shear rate for the investigated temperatures. It can be seen that the shear viscosity of the GMT decreases with increasing shear rate and temperature.

<table><caption>Table 1: Power law coefficient for the shear viscosity.</caption><thead><tr><td>Temperature in °C</td><td>K in Pa s</td><td>n</td></tr></thead><tbody><tr><td>240</td><td>132391.97</td><td>0.564</td></tr><tr><td>260</td><td>77917.00</td><td>0.512</td></tr><tr><td>280</td><td>48583.70</td><td>0.470</td></tr></tbody></table>

<figure><img src="image_409.png" /><figcaption>Fig. 4. Logarithmic plot for the determination of the power law coefficients a) and resulting shear viscosity over shear rate b).</figcaption></figure>

## Parameter Fit for Biaxial Extension

Secondly, a pure biaxial extension of the GMT was assumed and the true stress, true strain and strain rate were determined using Eq. 5. In Fig. 5, the true stress is shown as a function of true strain and strain rate for the investigated squeeze rates and temperatures. It can be seen that the stress increases with increasing strain and strain rate. In order to use the true stress-strain curves as an input for the numerical simulation, the curves must be known for a constant strain rate. This requires the modelling of the test results. For this purpose, a 4th degree polynomial approach was fitted for each temperature using the Curve Fitting Toolbox from Matlab. The fitted function with the corresponding coefficients and the coefficient of determination are given in Eq. 8 and Table 2.

$$ \sigma = p_{00} + p_{10} \cdot \varepsilon + p_{01} \cdot \dot{\varepsilon} + p_{20} \cdot \varepsilon^2 + p_{11} \cdot \varepsilon \cdot \dot{\varepsilon} + p_{02} \cdot \dot{\varepsilon}^2 + p_{30} \cdot \varepsilon^3 + p_{21} \cdot \varepsilon^2 \cdot \dot{\varepsilon} + p_{12} \cdot \varepsilon \cdot \dot{\varepsilon}^2 + p_{03} \cdot \dot{\varepsilon}^3 + p_{40} \cdot \varepsilon^4 + p_{31} \cdot \varepsilon^3 \cdot \dot{\varepsilon} + p_{22} \cdot \varepsilon^2 \cdot \dot{\varepsilon}^2 + p_{13} \cdot \varepsilon \cdot \dot{\varepsilon}^3 \quad (8) $$

<table><caption>Table 2: Modell coefficients and R² for biaxial extension.</caption><thead><tr><td></td><td>p<sub>00</sub></td><td>p<sub>10</sub></td><td>p<sub>01</sub></td><td>p<sub>20</sub></td><td>p<sub>11</sub></td><td>p<sub>02</sub></td><td>p<sub>30</sub></td><td>p<sub>21</sub></td><td>p<sub>12</sub></td><td>p<sub>03</sub></td><td>p<sub>40</sub></td><td>p<sub>31</sub></td><td>p<sub>22</sub></td><td>p<sub>13</sub></td><td>R<sup>2</sup></td></tr></thead><tbody><tr><td>240°C</td><td>-0.143</td><td>2.079</td><td>0.094</td><td>-3.050</td><td>4.839</td><td>-0.028</td><td>1.479</td><td>-3.501</td><td>-0.681</td><td>0.003</td><td>-0.195</td><td>1.613</td><td>-0.215</td><td>0.100</td><td>99.29%</td></tr><tr><td>260°C</td><td>1.554</td><td>1.075</td><td>1.183</td><td>-0.451</td><td>0.615</td><td>-0.333</td><td>0.150</td><td>-0.423</td><td>-0.472</td><td>0.272</td><td>0.025</td><td>0.173</td><td>-0.154</td><td>0.275</td><td>99.53%</td></tr><tr><td>280°C</td><td>-0.012</td><td>0.210</td><td>-0.039</td><td>1.060</td><td>1.863</td><td>0.013</td><td>-1.614</td><td>-1.327</td><td>-0.179</td><td>-0.001</td><td>0.596</td><td>0.633</td><td>-0.067</td><td>0.024</td><td>99.64%</td></tr></tbody></table>
The resulting models are shown in Fig. 5 as the coloured surfaces. These were used to calculate true stress-strain curves for constant strain rates, which were implemented in the material model for the numerical simulation. A description of the simulation model follows in the next section.

<figure><img src="image_399.png" /><figcaption>Fig. 5. True stress over true strain and strain rate for the investigated squeeze rates and temperatures.</figcaption></figure>

## Numerical Simulation of the Compression Tests

For the verification of the material models, the compression tests were simulated with Abaqus using the CEL method. A three-dimensional model of the compression test was created, which consists of the two tool plates, the GMT specimen and the Eulerian space in which the material can flow. To reduce the computational time, the symmetry was exploited and only one eighth of the setup was considered. The model with the corresponding components is shown in Fig. 6.

<figure><img src="image_400.png" /><figcaption>Fig. 6. Numerical model of the compression tests.</figcaption></figure>

The tools were defined as ideal rigid bodies and meshed with an element edge length of 1 mm. For the Eulerian section, an element edge length of 0.9 mm was applied to ensure that at least three elements are present across the thickness at the smallest punch distance. Two material models were applied to model the flow behaviour of the GMT specimen. Firstly, the specimen was associated
The resulting models are shown in Fig. 5 as the coloured surfaces. These were used to calculate true stress-strain curves for constant strain rates, which were implemented in the material model for the numerical simulation. A description of the simulation model follows in the next section.

<figure><img src="image_410.png" /><figcaption>Fig. 5. True stress over true strain and strain rate for the investigated squeeze rates and temperatures.</figcaption></figure>

## Numerical Simulation of the Compression Tests

For the verification of the material models, the compression tests were simulated with Abaqus using the CEL method. A three-dimensional model of the compression test was created, which consists of the two tool plates, the GMT specimen and the Eulerian space in which the material can flow. To reduce the computational time, the symmetry was exploited and only one eighth of the setup was considered. The model with the corresponding components is shown in Fig. 6.

<figure><img src="image_411.png" /><figcaption>Fig. 6. Numerical model of the compression tests.</figcaption></figure>

The tools were defined as ideal rigid bodies and meshed with an element edge length of 1 mm. For the Eulerian section, an element edge length of 0.9 mm was applied to ensure that at least three elements are present across the thickness at the smallest punch distance. Two material models were applied to model the flow behaviour of the GMT specimen. Firstly, the specimen was associated
with a viscous material model and the shear viscosity was defined using the power law coefficients in Table 1. In the simulation with the viscous material model, a sticking contact was defined between the tool surfaces and the specimen, as the coefficients were evaluated under this assumption. In the second material model, the flow of the GMT was modelled using the true stress-strain curves shown in Fig. 5. The curves were implemented as a function of the strain rate and temperature. Here, a frictionless contact between the tools and the specimen was defined in order to ensure a complete slip of the material on the tool surface. The temperatures were defined in the simulation according to the experimental tests. For verification purposes, only the simulations with a squeeze rate of 5 and 25 mm/s were analysed, as these are in the relevant range of the later compression moulding tests in an industrial scale. Furthermore, these tests could be calculated in an acceptable amount of time due to the short test duration.

## Numerical Results and Discussion

After simulation, the force-displacement curves predicted by the simulation were compared to the experimental results, which are shown in Fig. 7. If a pure shear flow is assumed, the predicted squeeze force is underestimated in all simulations up to a punch displacement of approximately 8 mm. With further increasing displacements, the predicted squeeze force increases exponentially and clearly exceeds the experimental force. This is in accordance to the findings of Kotsikos et al. [8], who calculated the squeeze force analytically. Consequently, the shearing of the material only appears to have a significant influence on the forming force when small punch distances are reached.

Under the assumption of a pure biaxial extension, the predicted squeeze force increases at low punch displacements, which results in a better agreement to the experimental curves. Overall, a higher accuracy of the predicted squeeze force is achieved up to the end of the forming process. This leads to the conclusion, that the assumption of pure biaxial flow provides a more accurate representation of the material behaviour during the compression tests. Consequently, the flow modelling using true stress-strain curves is the most promising approach in this case. Before the biaxial flow model can be applied in the simulation of the industrial process, the applicability has to be verified. If it turns out, that no complete slip occurs between the GMT and the industrial mould surface, the material model might lead to inadequate results. In this case, the compression tests should be repeated with an adhesive layer between the GMT and the tool surface, which prevents slip. This would allow the modelling of a pure shear flow or a combination of shear flow and biaxial extension.
with a viscous material model and the shear viscosity was defined using the power law coefficients in Table 1. In the simulation with the viscous material model, a sticking contact was defined between the tool surfaces and the specimen, as the coefficients were evaluated under this assumption. In the second material model, the flow of the GMT was modelled using the true stress-strain curves shown in Fig. 5. The curves were implemented as a function of the strain rate and temperature. Here, a frictionless contact between the tools and the specimen was defined in order to ensure a complete slip of the material on the tool surface. The temperatures were defined in the simulation according to the experimental tests. For verification purposes, only the simulations with a squeeze rate of 5 and 25 mm/s were analysed, as these are in the relevant range of the later compression moulding tests in an industrial scale. Furthermore, these tests could be calculated in an acceptable amount of time due to the short test duration.

## Numerical Results and Discussion

After simulation, the force-displacement curves predicted by the simulation were compared to the experimental results, which are shown in Fig. 7. If a pure shear flow is assumed, the predicted squeeze force is underestimated in all simulations up to a punch displacement of approximately 8 mm. With further increasing displacements, the predicted squeeze force increases exponentially and clearly exceeds the experimental force. This is in accordance to the findings of Kotsikos et al. [8], who calculated the squeeze force analytically. Consequently, the shearing of the material only appears to have a significant influence on the forming force when small punch distances are reached.

Under the assumption of a pure biaxial extension, the predicted squeeze force increases at low punch displacements, which results in a better agreement to the experimental curves. Overall, a higher accuracy of the predicted squeeze force is achieved up to the end of the forming process. This leads to the conclusion, that the assumption of pure biaxial flow provides a more accurate representation of the material behaviour during the compression tests. Consequently, the flow modelling using true stress-strain curves is the most promising approach in this case. Before the biaxial flow model can be applied in the simulation of the industrial process, the applicability has to be verified. If it turns out, that no complete slip occurs between the GMT and the industrial mould surface, the material model might lead to inadequate results. In this case, the compression tests should be repeated with an adhesive layer between the GMT and the tool surface, which prevents slip. This would allow the modelling of a pure shear flow or a combination of shear flow and biaxial extension.
<figure><img src="image_401.png" /><figcaption>Fig. 7. Comparison of experimental and numerical squeeze forces over punch displacement.</figcaption></figure>

## Conclusion and Outlook

In this contribution, the rheological properties of a GMT (PA6-GF30) were determined to model the material flow in a compression moulding simulation. Isothermal compression tests were carried out in a parallel plate rheometer at varying squeeze rates and temperatures. The flow behaviour was modelled under the assumption of pure shear flow and a pure biaxial extension. It was found that the shear viscosity of the GMT decreases with increasing temperature and strain rate and the true stress increases with strain and strain rate. For a verification of the results, the compression tests were simulated in Abaqus using the CEL method. Here, the first considered assumption of a pure shear flow led to an underestimation of the squeeze force for small punch displacements and to an overestimation for high punch displacements. The second considered assumption of a pure extensional flow led to an enhanced prediction accuracy regarding the resulting squeeze force.

In future work, the material model will be used to simulate the combined forming process of the battery housing structure made of GMT and UD tapes. The UD tapes will be modelled as a composite shell lay-up. For the GMT, the CEL method will be applied, as this proved to be a suitable method for the simulation of squeeze flow. If it turns out that the biaxial extension model does not lead to reliable simulation results for the industrial process, further investigations will be carried out and the material flow will be modelled as pure shear or a combination of shear flow and biaxial extension. The objective of the industrial process simulation is to identify a suitable process window to ensure the production of high-quality components. Furthermore, the tool design can be supported and the amount of time- and cost-consuming trial-and-error tests can be reduced.
<figure><img src="image_412.png" /><figcaption>Fig. 7. Comparison of experimental and numerical squeeze forces over punch displacement.</figcaption></figure>

## Conclusion and Outlook

In this contribution, the rheological properties of a GMT (PA6-GF30) were determined to model the material flow in a compression moulding simulation. Isothermal compression tests were carried out in a parallel plate rheometer at varying squeeze rates and temperatures. The flow behaviour was modelled under the assumption of pure shear flow and a pure biaxial extension. It was found that the shear viscosity of the GMT decreases with increasing temperature and strain rate and the true stress increases with strain and strain rate. For a verification of the results, the compression tests were simulated in Abaqus using the CEL method. Here, the first considered assumption of a pure shear flow led to an underestimation of the squeeze force for small punch displacements and to an overestimation for high punch displacements. The second considered assumption of a pure extensional flow led to an enhanced prediction accuracy regarding the resulting squeeze force.

In future work, the material model will be used to simulate the combined forming process of the battery housing structure made of GMT and UD tapes. The UD tapes will be modelled as a composite shell lay-up. For the GMT, the CEL method will be applied, as this proved to be a suitable method for the simulation of squeeze flow. If it turns out that the biaxial extension model does not lead to reliable simulation results for the industrial process, further investigations will be carried out and the material flow will be modelled as pure shear or a combination of shear flow and biaxial extension. The objective of the industrial process simulation is to identify a suitable process window to ensure the production of high-quality components. Furthermore, the tool design can be supported and the amount of time- and cost-consuming trial-and-error tests can be reduced.
## Acknowledgements

This research and development project is funded by the German Federal Ministry of Education and Research (BMBF) within the funding initiative "Research Campus - Public-Private Partnership for innovation" (funding code: 02P18Q745) and implemented by the Project Management Agency Karlsruhe (PTKA). The author is responsible for the content of this publication.

## References

[1] D.K. Rajak, D.D. Pagar, P.L. Menezes, E. Linul, Fiber-Reinforced Polymer Composites: Manufacturing, Properties, and Applications, Polymers 11 (2019). https://doi.org/10.3390/polym11101667

[2] K. Mocellin, P.-O. Bouchard, R. Bigot, T. Balan (Eds.), Proceedings of the 14th International Conference on the Technology of Plasticity - Current Trends in the Technology of Plasticity, Springer Nature Switzerland, Cham, 2024

[3] J. Karger-Kocsis, Polypropylene Structure, blends and Composites, Springer Netherlands, Dordrecht, 1995.

[4] B.-A. Behrens, S. Hübner, C. Bonk, F. Bohne, M. Micke-Camuz, Development of a Combined Process of Organic Sheet forming and GMT Compression Molding, Procedia Engineering 207 (2017) 101–106. https://doi.org/10.1016/j.proeng.2017.10.745

[5] J. Weichenhain, P. Althaus, S. Hübner, H. Wester, D. Rosenbusch, B.-A. Behrens, Investigation of a Compression Molding Process for the Variant Flexible Production of a GMT Battery Shell, in: B.-A. Behrens, A. Brosius, W.-G. Drossel, W. Hintze, S. Ihlenfeldt, P. Nyhuis (Eds.), Lecture Notes in Production Engineering, Production at the Leading Edge of Technology, Springer International Publishing, Cham, 2022, pp. 20–28.

[6] K.B. Thattaiparthasarthy, S. Pillay, U.K. Vaidya, Rheological characterization of long fiber thermoplastics – Effect of temperature, fiber length and weight fraction, Composites Part A: Applied Science and Manufacturing 40 (2009) 1515–1523. https://doi.org/10.1016/j.compositesa.2009.06.009

[7] J.R. Scott, Theory and application of the parallel-plate plastometer, Trans Inst Rubber Ind (1931) 169–186.

[8] G. Kotsikos, J.H. Bland, A.G. Gibson, H.W. Chandler, Squeeze flow testing of glass mat thermoplastic material, Composites Part A: Applied Science and Manufacturing 27 (1996) 1195–1200. https://doi.org/10.1016/1359-835X(96)00077-2

[9] S. Lee, D. Shin, G. Kim, W. Ji, Numerical model for compression molding process of hybridly laminated thermoplastic composites based on anisotropic rheology, Composites Part C: Open Access 7 (2022) 100215. https://doi.org/10.1016/j.jcomc.2021.100215

[10] M.A. Dweib, C.M. Ó. Brádaigh, Anisotropic modeling of isothermal squeezing flow of Glass-Mat reinforced thermoplastics (GMT), Polym Compos 19 (1998) 588–599. https://doi.org/10.1002/pc.10132

[11] D. Dörr, N. Singh-Heer, R.C. Gergely, L. Schreyer, F. Henning, A.G. Straatman, A. Hrymak, Rheological characterization and macroscopic modeling and simulation of the molding process of a PA6 Glass Mat Thermoplastic (GMT), Composites Part A: Applied Science and Manufacturing 176 (2024) 107780.
## Acknowledgements

This research and development project is funded by the German Federal Ministry of Education and Research (BMBF) within the funding initiative "Research Campus - Public-Private Partnership for innovation" (funding code: 02P18Q745) and implemented by the Project Management Agency Karlsruhe (PTKA). The author is responsible for the content of this publication.

## References

[1] D.K. Rajak, D.D. Pagar, P.L. Menezes, E. Linul, Fiber-Reinforced Polymer Composites: Manufacturing, Properties, and Applications, Polymers 11 (2019). https://doi.org/10.3390/polym11101667

[2] K. Mocellin, P.-O. Bouchard, R. Bigot, T. Balan (Eds.), Proceedings of the 14th International Conference on the Technology of Plasticity - Current Trends in the Technology of Plasticity, Springer Nature Switzerland, Cham, 2024

[3] J. Karger-Kocsis, Polypropylene Structure, blends and Composites, Springer Netherlands, Dordrecht, 1995.

[4] B.-A. Behrens, S. Hübner, C. Bonk, F. Bohne, M. Micke-Camuz, Development of a Combined Process of Organic Sheet forming and GMT Compression Molding, Procedia Engineering 207 (2017) 101–106. https://doi.org/10.1016/j.proeng.2017.10.745

[5] J. Weichenhain, P. Althaus, S. Hübner, H. Wester, D. Rosenbusch, B.-A. Behrens, Investigation of a Compression Molding Process for the Variant Flexible Production of a GMT Battery Shell, in: B.-A. Behrens, A. Brosius, W.-G. Drossel, W. Hintze, S. Ihlenfeldt, P. Nyhuis (Eds.), Lecture Notes in Production Engineering, Production at the Leading Edge of Technology, Springer International Publishing, Cham, 2022, pp. 20–28.

[6] K.B. Thattaiparthasarthy, S. Pillay, U.K. Vaidya, Rheological characterization of long fiber thermoplastics – Effect of temperature, fiber length and weight fraction, Composites Part A: Applied Science and Manufacturing 40 (2009) 1515–1523. https://doi.org/10.1016/j.compositesa.2009.06.009

[7] J.R. Scott, Theory and application of the parallel-plate plastometer, Trans Inst Rubber Ind (1931) 169–186.

[8] G. Kotsikos, J.H. Bland, A.G. Gibson, H.W. Chandler, Squeeze flow testing of glass mat thermoplastic material, Composites Part A: Applied Science and Manufacturing 27 (1996) 1195–1200. https://doi.org/10.1016/1359-835X(96)00077-2

[9] S. Lee, D. Shin, G. Kim, W. Ji, Numerical model for compression molding process of hybridly laminated thermoplastic composites based on anisotropic rheology, Composites Part C: Open Access 7 (2022) 100215. https://doi.org/10.1016/j.jcomc.2021.100215

[10] M.A. Dweib, C.M. Ó. Brádaigh, Anisotropic modeling of isothermal squeezing flow of Glass-Mat reinforced thermoplastics (GMT), Polym Compos 19 (1998) 588–599. https://doi.org/10.1002/pc.10132

[11] D. Dörr, N. Singh-Heer, R.C. Gergely, L. Schreyer, F. Henning, A.G. Straatman, A. Hrymak, Rheological characterization and macroscopic modeling and simulation of the molding process of a PA6 Glass Mat Thermoplastic (GMT), Composites Part A: Applied Science and Manufacturing 176 (2024) 107780.
[12] C.-T. Huang, L.-J. Chen, T.-Y. Chien, Investigation of the Viscoelastic Behavior Variation of Glass Mat Thermoplastics (GMT) in Compression Molding, Polymers 11 (2019). https://doi.org/10.3390/polym11020335

[13] B.-A. Behrens, F. Bohne, R. Lorenz, H. Arndt, S. Hübner, M. Micke-Camuz, Numerical and Experimental Investigation of GMT Compression Molding and Fiber Displacement of UD-Tape Inserts, Procedia Manufacturing 47 (2020) 11-16. https://doi.org/10.1016/j.promfg.2020.04.109

[14] K. Dröder, B.-A. Behrens, F. Bohne, A. Chugreev, H. Schulze, B. Wonnenberg, Numerical and Experimental Investigation of Thermoplastics in Multi-Axis Forming Processes, Procedia CIRP 85 (2019) 96-101. https://doi.org/10.1016/j.procir.2019.09.024
[12] C.-T. Huang, L.-J. Chen, T.-Y. Chien, Investigation of the Viscoelastic Behavior Variation of Glass Mat Thermoplastics (GMT) in Compression Molding, Polymers 11 (2019). https://doi.org/10.3390/polym11020335

[13] B.-A. Behrens, F. Bohne, R. Lorenz, H. Arndt, S. Hübner, M. Micke-Camuz, Numerical and Experimental Investigation of GMT Compression Molding and Fiber Displacement of UD-Tape Inserts, Procedia Manufacturing 47 (2020) 11-16. https://doi.org/10.1016/j.promfg.2020.04.109

[14] K. Dröder, B.-A. Behrens, F. Bohne, A. Chugreev, H. Schulze, B. Wonnenberg, Numerical and Experimental Investigation of Thermoplastics in Multi-Axis Forming Processes, Procedia CIRP 85 (2019) 96-101. https://doi.org/10.1016/j.procir.2019.09.024
# Thermoplastic pultrusion of recycled PET matrix composites

TUCCI Fausto¹,ᵃ*, RUBINO Felice²,ᵇ, ESPERTO Vitantonio¹,ᶜ and
CARLONE Pierpaolo¹,ᵈ

¹Department of Industrial Engineering, University of Salerno, 132 Via Giovanni Paolo II, 84084 Fisciano (SA), Italy

²Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Folcara, 03043, Cassino (FR), Italy

ᵃftucci@unisa.it, ᵇfrubino@unisa.it, ᶜvesperto@unisa.it, ᵈpcarlone@unisa.it

**Keywords:** Pultrusion, Thermoplastic, Sustainable Manufacturing

**Abstract.** Recently, many industry sectors are investigating safe ways to replace conventional materials by adopting thermoplastic matrix composites. Indeed, the adoption of this class of polymeric matrices enables other post-process operations, such as forming and welding. Moreover, the diffusion and the improvement of thermoplastic matrix composites can promote the usage of recycled polymers, which would dramatically improve the environmental sustainability of the production. The aim of this work is the assessment of the thermoplastic pultrusion of preimpregnated tapes made of glass fibers and recycled polyethylene terephthalate (PET) matrix. A thermoplastic pultrusion line consisting of a heating-forming die and a cooling die has been used to manufacture a rectangular cross-section profile having dimensions of 25 mm in width and 4 mm in thickness. The internal temperature has been measured during the process by using a wire thermocouple. The composite produced has been assessed by interlaminar shear strength testing, and its cross-section has been analyzed by optical microscopy to assess the continuity of the matrix, the internal structure, and the distribution of the fibrous reinforcement.

**Introduction**

Generally, thermoplastic matrix composites present several relevant advantages in comparison with fiber reinforced thermosets, in terms of mechanical and physical properties, and, due to this reason, they collected the interest of industry sectors requiring high-performance materials. Moreover, they are post-formable and weldable [1,2]. Nevertheless, their high viscosity did not favor their spreading as a matrix for composite. Indeed, high viscosity determines issues in reinforcement-matrix combination and increases the risk of defects and manufacturing costs. Even if recently many researchers are actively working on thermoplastics viscosity reduction [3], a viable solution for the adoption of these matrices is based on the usage of pre-impregnated reinforcement [4]. In this case, the raw materials are generally more expensive, but, on the other hand, they are easier to be processed and the defects related to the matrix-reinforcement combination are excluded since the impregnation occurs off-line.

Moreover, thermoplastic polymers are remarkably interesting from the point of view of sustainability. Indeed, these materials can be remelted and reused several times, even if contamination and degradation could complicate repetitive recycling. It is noteworthy considering that the remelting and reusing strategy is easily applicable in unreinforced polymers, which do not contain reinforcing phases. The presence of fibers excludes the possibility of an easy separation of the phases. Nevertheless, the formability of the composite when heated opens new possibilities of re-forming the components at its end of life. Another option for more sustainable manufacturing is related to the use of recycled thermoplastics as a matrix for composites [5]. In this case, polyethylene terephthalate and polypropylene are the most available materials. Of course, the

<figure><img src="image_402.png" /></figure>
# Thermoplastic pultrusion of recycled PET matrix composites

TUCCI Fausto<sup>1,a*</sup>, RUBINO Felice<sup>2,b</sup>, ESPERTO Vitantonio<sup>1,c</sup> and CARLONE Pierpaolo<sup>1,d</sup>

<sup>1</sup>Department of Industrial Engineering, University of Salerno, 132 Via Giovanni Paolo II, 84084 Fisciano (SA), Italy

<sup>2</sup>Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, Folcara, 03043, Cassino (FR), Italy

<sup>a</sup>ftucci@unisa.it, <sup>b</sup>frubino@unisa.it, <sup>c</sup>vesperto@unisa.it, <sup>d</sup>pcarlone@unisa.it

**Keywords:** Pultrusion, Thermoplastic, Sustainable Manufacturing

**Abstract.** Recently, many industry sectors are investigating safe ways to replace conventional materials by adopting thermoplastic matrix composites. Indeed, the adoption of this class of polymeric matrices enables other post-process operations, such as forming and welding. Moreover, the diffusion and the improvement of thermoplastic matrix composites can promote the usage of recycled polymers, which would dramatically improve the environmental sustainability of the production. The aim of this work is the assessment of the thermoplastic pultrusion of preimpregnated tapes made of glass fibers and recycled polyethylene terephthalate (PET) matrix. A thermoplastic pultrusion line consisting of a heating-forming die and a cooling die has been used to manufacture a rectangular cross-section profile having dimensions of 25 mm in width and 4 mm in thickness. The internal temperature has been measured during the process by using a wire thermocouple. The composite produced has been assessed by interlaminar shear strength testing, and its cross-section has been analyzed by optical microscopy to assess the continuity of the matrix, the internal structure, and the distribution of the fibrous reinforcement.

**Introduction**

Generally, thermoplastic matrix composites present several relevant advantages in comparison with fiber reinforced thermosets, in terms of mechanical and physical properties, and, due to this reason, they collected the interest of industry sectors requiring high-performance materials. Moreover, they are post-formable and weldable [1,2]. Nevertheless, their high viscosity did not favor their spreading as a matrix for composite. Indeed, high viscosity determines issues in reinforcement-matrix combination and increases the risk of defects and manufacturing costs. Even if recently many researchers are actively working on thermoplastics viscosity reduction [3], a viable solution for the adoption of these matrices is based on the usage of pre-impregnated reinforcement [4]. In this case, the raw materials are generally more expensive, but, on the other hand, they are easier to be processed and the defects related to the matrix-reinforcement combination are excluded since the impregnation occurs off-line.

Moreover, thermoplastic polymers are remarkably interesting from the point of view of sustainability. Indeed, these materials can be remelted and reused several times, even if contamination and degradation could complicate repetitive recycling. It is noteworthy considering that the remelting and reusing strategy is easily applicable in unreinforced polymers, which do not contain reinforcing phases. The presence of fibers excludes the possibility of an easy separation of the phases. Nevertheless, the formability of the composite when heated opens new possibilities of re-forming the components at its end of life. Another option for more sustainable manufacturing is related to the use of recycled thermoplastics as a matrix for composites [5]. In this case, polyethylene terephthalate and polypropylene are the most available materials. Of course, the

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