<figure><img src="image_540.png" /><figcaption>Figure 1 - Description of the implemented algorithm using a flowchart describing its main steps.</figcaption></figure>

From this point on, the algorithm incorporates concepts from another work described in the article "A simple MATLAB draping code for fiber-reinforced composites with application to optimization of manufacturing process parameters" [16]. The processes outlined in this article are implemented to provide data better suited for the automatic programming of a robotic layup process.

For this new surface, a layup simulation is performed. The prepreg is simulated by creating a grid on the surface mold from a specific starting point (see Fig. 2). The choice of the starting point is crucial as it represents the initial contact between the prepreg sheet and the mold during the actual layup. The simulation yields different results for different starting points, mirroring the real process where the first point of contact influences the entire lay-up outcome. The curvatures encountered during layup deform the prepreg based on the starting point and layup directions.

The objective is to simulate layup from various points, studying the deformation of the virtual prepreg sheet, and defining the optimal layup approach that results in the best layup strategy for the specific mold surface to be machined. Finally, all information generated in the previous steps is collected and provided to an algorithm for generating robotic routes and movements.

<figure><img src="image_541.png" /><figcaption>Figure 2 - The fabric modeled as a grid of pin-joined cells. The origin node is chosen as starting point for weft and warp generation. On the right, an example of an origin node for a sub-surface from which the warp and weft lines start.</figcaption></figure>
The selection of the starting point is performed automatically by the algorithm but can of course also be done manually by the user. The previous division of the surface to be processed into sub-surfaces is used by the algorithm to place a starting point at the center of each mesh sub-surface, as the idea is to have a different simulation for each sub-surface of the mold surface and finally compare them.

<figure><img src="image_531.png" /><figcaption>Figure 3 - Examples of two grids placed on the same sphere. On the right side of the graphs there is a bar that indicates the size of the shear angle to which each grid cells are subjected. The Cartesian reference systems shown in the figure are related to the mold under consideration (to the sphere in this case). The two examples shown in the figure also differ in the orientation of the prepreg on the reference sphere: the image on the right shows an orientation of the fibers concordant with the axes of the X-Y plane (taken as a reference); in the image on the left, however, it can be seen that the fibers are not parallel to any of the axes.</figcaption></figure>

To simulate the behavior of the prepreg, a grid is created from a selected point, which will be considered as the starting point of grid creation. The edges of the grid cells represent fiber tows, and the grid nodes represent the cross-over points of the fibers (Fig. 2). Apart from the starting point, another crucial piece of information is the orientation of the prepreg fabric, indicating the warp and weft directions.

In the virtual environment, this involves generating two main curves from the starting point, which serves as one of the vertices of the first grid element and the origin of the grid. These curves depict the first two fiber filaments, representing the intersection of the warp and weft. Starting from the selected point, the triplet of verses matched to it consisting of the normal (N), tangent (T) and binormal (B) verses are considered. These reference unit vectors will be used for the identification of the main curves in the grid. Specifically, forming the principal curves will be the intersections with the discretized surface of the mold with the Normal plane and the Osculating plane. By default, the Osculating plane and the Normal plane are oriented parallel to the Cartesian planes ZX and ZY, respectively. This implies that a projection on the XY plane of the grid will see the fibers parallel to either the X axis or the Y axis. In the case of wanting to run a drafting simulation with a fiber orientation other than the default, the user can define an angle that will result in a rotation of the entire grid. The angle in question is then by default set equal to zero. In the case of a modification of this angle by the user, this value will represent the angle formed between the osculator plane and the ZX plane and the angle formed between the normal plane and the ZY plane: the angle is considered positive when the grid rotates clockwise around the Z axis.
The selection of the starting point is performed automatically by the algorithm but can of course also be done manually by the user. The previous division of the surface to be processed into sub-surfaces is used by the algorithm to place a starting point at the center of each mesh sub-surface, as the idea is to have a different simulation for each sub-surface of the mold surface and finally compare them.

<figure><img src="image_542.png" /><figcaption>Figure 3 - Examples of two grids placed on the same sphere. On the right side of the graphs there is a bar that indicates the size of the shear angle to which each grid cells are subjected. The Cartesian reference systems shown in the figure are related to the mold under consideration (to the sphere in this case). The two examples shown in the figure also differ in the orientation of the prepreg on the reference sphere: the image on the right shows an orientation of the fibers concordant with the axes of the X-Y plane (taken as a reference); in the image on the left, however, it can be seen that the fibers are not parallel to any of the axes.</figcaption></figure>

To simulate the behavior of the prepreg, a grid is created from a selected point, which will be considered as the starting point of grid creation. The edges of the grid cells represent fiber tows, and the grid nodes represent the cross-over points of the fibers (Fig. 2). Apart from the starting point, another crucial piece of information is the orientation of the prepreg fabric, indicating the warp and weft directions.

In the virtual environment, this involves generating two main curves from the starting point, which serves as one of the vertices of the first grid element and the origin of the grid. These curves depict the first two fiber filaments, representing the intersection of the warp and weft. Starting from the selected point, the triplet of verses matched to it consisting of the normal (N), tangent (T) and binormal (B) verses are considered. These reference unit vectors will be used for the identification of the main curves in the grid. Specifically, forming the principal curves will be the intersections with the discretized surface of the mold with the Normal plane and the Osculating plane. By default, the Osculating plane and the Normal plane are oriented parallel to the Cartesian planes ZX and ZY, respectively. This implies that a projection on the XY plane of the grid will see the fibers parallel to either the X axis or the Y axis. In the case of wanting to run a drafting simulation with a fiber orientation other than the default, the user can define an angle that will result in a rotation of the entire grid. The angle in question is then by default set equal to zero. In the case of a modification of this angle by the user, this value will represent the angle formed between the osculator plane and the ZX plane and the angle formed between the normal plane and the ZY plane: the angle is considered positive when the grid rotates clockwise around the Z axis.
<figure><img src="image_532.png" /><figcaption>Figure 4 - Stages of grid implementation: on the left, the origin node and main lines (blue and red), representing the warp and weft, are placed on the mesh; in the middle, the vertices of the first cell are identified according to the resolution chosen by the user; on the right, the generation of subsequent cells following one of the main lines, cells are created one at a time using the vertices of the previous cell as starting points.</figcaption></figure>

The creation of grid cells is an iterative process. The main curves divide the mold mesh into four sectors: the grid creation process creates the grid cells for each sector separately (the order in which the sectors are considered is not relevant). The first three nodes are identified on the main curves: the first is the origin point, the second and third are placed on the warp and weft curves respectively at a given distance. The user defines this distance, determining the size of the grid element: the distance chosen by the user is called “step” from now on. The accuracy of the simulation relies heavily on this variable: smaller grid elements result in a greater number of elements and more accurate results from the algorithm. However, it's essential to consider that a higher number of grid elements increases the computational load on the processor. Thus, finding the right trade-off between desired accuracy and computational load is a key consideration. The fourth grid point is found by imposing two conditions: contact with the mold surface and maintaining the step distance between adjacent nodes (Fig. 4).

Because the user has set the step value, the sides of the grid cells are equal to each other: on a plane, the grid cells are always square. But on a non-planar surface, the cells deviate from the square shape: the further away from the starting point, the more the cells deform into rhombuses.

<figure><img src="image_533.png" /><figcaption>Figure 5 - Shearing of woven fabric: the tows rotate at their cross-over points in the weave. The shear angle is denoted with $\gamma$.</figcaption></figure>

After the first cell is formed, the other cells are created following one of the two main curves. For the second cell, for example, the new starting point is the node of the previous cell placed on the main curve, the second one will be taken on the main curve one step away, the third node will be the fourth found vertex of the first cell, and the fourth and last vertex of the second cell will be found by imposing the same conditions as in the previous case: same distance from the adjacent nodes and contact with the mesh. The algorithm continues by forming one row of cells at a time starting with one of the main curvatures. The same procedure is repeated for the creation and positioning of all grid nodes until the entire surface of the given mold is covered.
<figure><img src="image_543.png" /><figcaption>Figure 4 - Stages of grid implementation: on the left, the origin node and main lines (blue and red), representing the warp and weft, are placed on the mesh; in the middle, the vertices of the first cell are identified according to the resolution chosen by the user; on the right, the generation of subsequent cells following one of the main lines, cells are created one at a time using the vertices of the previous cell as starting points.</figcaption></figure>

The creation of grid cells is an iterative process. The main curves divide the mold mesh into four sectors: the grid creation process creates the grid cells for each sector separately (the order in which the sectors are considered is not relevant). The first three nodes are identified on the main curves: the first is the origin point, the second and third are placed on the warp and weft curves respectively at a given distance. The user defines this distance, determining the size of the grid element: the distance chosen by the user is called “step” from now on. The accuracy of the simulation relies heavily on this variable: smaller grid elements result in a greater number of elements and more accurate results from the algorithm. However, it's essential to consider that a higher number of grid elements increases the computational load on the processor. Thus, finding the right trade-off between desired accuracy and computational load is a key consideration. The fourth grid point is found by imposing two conditions: contact with the mold surface and maintaining the step distance between adjacent nodes (Fig. 4).

Because the user has set the step value, the sides of the grid cells are equal to each other: on a plane, the grid cells are always square. But on a non-planar surface, the cells deviate from the square shape: the further away from the starting point, the more the cells deform into rhombuses.

<figure><img src="image_544.png" /><figcaption>Figure 5 - Shearing of woven fabric: the tows rotate at their cross-over points in the weave. The shear angle is denoted with $\gamma$.</figcaption></figure>

After the first cell is formed, the other cells are created following one of the two main curves. For the second cell, for example, the new starting point is the node of the previous cell placed on the main curve, the second one will be taken on the main curve one step away, the third node will be the fourth found vertex of the first cell, and the fourth and last vertex of the second cell will be found by imposing the same conditions as in the previous case: same distance from the adjacent nodes and contact with the mesh. The algorithm continues by forming one row of cells at a time starting with one of the main curvatures. The same procedure is repeated for the creation and positioning of all grid nodes until the entire surface of the given mold is covered.
The angle formed by the two sides of the cell represents the shear angle and, therefore, the deformation of the prepregs in that specific area during the layup.

The shear angles are then collected (Fig. 5). The algorithm is repeated by descending different points of the mold surface as grid origin and the cutting angles of the prepreg cells of each configuration are collected and then eventually compared. In the end, the point from which the resulting shear angles are smallest is chosen.

So far, the algorithm revealed the most suitable sub-surface for the beginning of the layup process for the specific selected surface. That is, the most suitable portion of the mold has been selected for the first contact between mold and prepreg and the first sub-surface to layup. Assuming that a mold of complex shape, consisting of multiple curvatures, is being considered, thus a mold consisting of multiple sub-surfaces, the need arises at this point to determine the best strategies for laying down the prepreg: it must be determined which portions of the mold surface should be laid down next. Essentially, the problem translates into the order of sub-surfaces affected by the robotic layup process.

Having determined the layup order, routes for the robotic layup can now be developed for the specific mold under consideration. A sequence of paths is developed for each sub-surface. Taking as a reference the most significant edge of the sub-surface under consideration, a series of parallel paths are generated at a given spacing from each other: this spacing depends on the thickness of the roller with which the robot has been equipped.

## Results and discussion

With the algorithm described above, a tool has been created for the automatic programming of the robotic layup for a generic complex-shaped mold. The user only needs to provide the discretized surface to the algorithm, and the program automatically works to provide the most suitable strategies for the surface to be processed. The implemented algorithm has been tested for different mold geometries. One of the main goals of this study is to be able to use the presented tool for robotic layup process with generic mold shapes: no matter how many different curvatures and sub-surfaces are present on the mold to be processed. For a better understanding of the results obtained, only two geometries have been shown as examples of the result provided by the implemented algorithm. Figure 6 shows the mentioned molds.

<figure><img src="image_534.png" /><figcaption>Figure 6 - Two of the molds tested with the implemented algorithm taken as examples for the reported results.</figcaption></figure>

By detecting the curvatures present on the mold surface, the algorithm automatically divides the given surface into sub-surfaces: adjacent mesh elements having the same curvature are collected and grouped into sub-surfaces. In this way, the user can also choose to process the entire given surface or only a portion of it by selecting the desired subsurface to be processed: the user is prompted to click on the MATLAB graph of the surface by a message in the dialogue box and confirm the choice by pressing enter. Once the selection is made, the algorithm generates a new
The angle formed by the two sides of the cell represents the shear angle and, therefore, the deformation of the prepregs in that specific area during the layup.

The shear angles are then collected (Fig. 5). The algorithm is repeated by descending different points of the mold surface as grid origin and the cutting angles of the prepreg cells of each configuration are collected and then eventually compared. In the end, the point from which the resulting shear angles are smallest is chosen.

So far, the algorithm revealed the most suitable sub-surface for the beginning of the layup process for the specific selected surface. That is, the most suitable portion of the mold has been selected for the first contact between mold and prepreg and the first sub-surface to layup. Assuming that a mold of complex shape, consisting of multiple curvatures, is being considered, thus a mold consisting of multiple sub-surfaces, the need arises at this point to determine the best strategies for laying down the prepreg: it must be determined which portions of the mold surface should be laid down next. Essentially, the problem translates into the order of sub-surfaces affected by the robotic layup process.

Having determined the layup order, routes for the robotic layup can now be developed for the specific mold under consideration. A sequence of paths is developed for each sub-surface. Taking as a reference the most significant edge of the sub-surface under consideration, a series of parallel paths are generated at a given spacing from each other: this spacing depends on the thickness of the roller with which the robot has been equipped.

## Results and discussion

With the algorithm described above, a tool has been created for the automatic programming of the robotic layup for a generic complex-shaped mold. The user only needs to provide the discretized surface to the algorithm, and the program automatically works to provide the most suitable strategies for the surface to be processed. The implemented algorithm has been tested for different mold geometries. One of the main goals of this study is to be able to use the presented tool for robotic layup process with generic mold shapes: no matter how many different curvatures and sub-surfaces are present on the mold to be processed. For a better understanding of the results obtained, only two geometries have been shown as examples of the result provided by the implemented algorithm. Figure 6 shows the mentioned molds.

<figure><img src="image_545.png" /><figcaption>Figure 6 - Two of the molds tested with the implemented algorithm taken as examples for the reported results.</figcaption></figure>

By detecting the curvatures present on the mold surface, the algorithm automatically divides the given surface into sub-surfaces: adjacent mesh elements having the same curvature are collected and grouped into sub-surfaces. In this way, the user can also choose to process the entire given surface or only a portion of it by selecting the desired subsurface to be processed: the user is prompted to click on the MATLAB graph of the surface by a message in the dialogue box and confirm the choice by pressing enter. Once the selection is made, the algorithm generates a new
discretized surface composed of only the user's selection (Fig. 7), and the algorithm proceeds as described above.

<figure><img src="image_535.png" /><figcaption>Figure 7 - Sub-surface selection process: on the left, the mesh of the entire mold; the user is asked to click on the mesh areas of the mold surface to be processed during the layup process, and the selected sub-surfaces are highlighted in green (center); on the right, a new mesh is created consisting of only the selected parts of the surface.</figcaption></figure>

The selection allows reducing the number of elements to be processed during the flow of the algorithm: thus, the load for the CPU processor will be reduced proportionally.

Subsequently, the algorithm goes on to identify the sequences of the selected sub-surfaces. Each selected subsurface is associated with an origin point, and an iteration is performed for each of these origins. The origin establishes a specific scenario that the prepreg will find during layup. This means that as many sub-surfaces are selected, as many scenarios will be present.

For each origin point, the draping model is run, which will provide an array in which the shear angles of the grid elements are collected for that specific configuration. Then an average shear angle is associated with each subsurface. The average shear angle represents the average deformation that the prepreg will undergo during the layup process in that specific configuration, i.e., starting the layup from the sub-surface associated with the origin point under consideration.

This means that the lower the average value of the shear angle, the less defects will be encountered during the layup process, consequently leading to a better quality of the component produced both from the point of view of the uniformity of the surface realized, which will be closer to the processed mold, and consequently also from the point of view of aesthetics, and the point of view of mechanical characteristics.

This part of the code is executed for each subsurface and the average shear angles are compared: the configuration that shows the lowest average shear angle will be chosen as the best part of the mold surface to start the layup.

Then, this information will be collected for the second phase of the code, in which the second-best subsurface to be covered is identified. The loop continues until all the sub-surfaces are processed and the sequence of sub-surfaces is finally identified.

In the Fig. 8, the result of each iteration of the loop is shown for two different molds taken as examples: the sub-surface to be processed is shown in yellow; the surface identified as the best sub-surface to be chosen in that specific iteration is identified in green.
discretized surface composed of only the user's selection (Fig. 7), and the algorithm proceeds as described above.

<figure><img src="image_546.png" /><figcaption>Figure 7 - Sub-surface selection process: on the left, the mesh of the entire mold; the user is asked to click on the mesh areas of the mold surface to be processed during the layup process, and the selected sub-surfaces are highlighted in green (center); on the right, a new mesh is created consisting of only the selected parts of the surface.</figcaption></figure>

The selection allows reducing the number of elements to be processed during the flow of the algorithm: thus, the load for the CPU processor will be reduced proportionally.

Subsequently, the algorithm goes on to identify the sequences of the selected sub-surfaces. Each selected subsurface is associated with an origin point, and an iteration is performed for each of these origins. The origin establishes a specific scenario that the prepreg will find during layup. This means that as many sub-surfaces are selected, as many scenarios will be present.

For each origin point, the draping model is run, which will provide an array in which the shear angles of the grid elements are collected for that specific configuration. Then an average shear angle is associated with each subsurface. The average shear angle represents the average deformation that the prepreg will undergo during the layup process in that specific configuration, i.e., starting the layup from the sub-surface associated with the origin point under consideration.

This means that the lower the average value of the shear angle, the less defects will be encountered during the layup process, consequently leading to a better quality of the component produced both from the point of view of the uniformity of the surface realized, which will be closer to the processed mold, and consequently also from the point of view of aesthetics, and the point of view of mechanical characteristics.

This part of the code is executed for each subsurface and the average shear angles are compared: the configuration that shows the lowest average shear angle will be chosen as the best part of the mold surface to start the layup.

Then, this information will be collected for the second phase of the code, in which the second-best subsurface to be covered is identified. The loop continues until all the sub-surfaces are processed and the sequence of sub-surfaces is finally identified.

In the Fig. 8, the result of each iteration of the loop is shown for two different molds taken as examples: the sub-surface to be processed is shown in yellow; the surface identified as the best sub-surface to be chosen in that specific iteration is identified in green.
<figure><img src="image_536.png" /><figcaption>Figure 8 - Examples of determining the order of sub-surfaces to be processed during the robotic layup process. Sub-surfaces not yet chosen are colored yellow; chosen sub-surfaces are colored green as they are identified as the most suitable surface in that particular iteration. Above is an example of a mold in which six sub-surfaces were chosen; below is a more complex mold in which fourteen sub-surfaces were chosen.</figcaption></figure>

Once the order of the sub-surfaces has been identified for the layup process, the paths for the robotic arm are implemented. For each sub-surface, a sequence of paths is generated that are all parallel to a border of the sub-surface under consideration that is considered most significant due to its extension. These paths are spaced by a distance that corresponds to the thickness of the roller chosen to lay that specific part of the surface. The selection of the roller is done automatically by the algorithm by comparing the radius of curvature of the sub-surface under examination with that of the available rollers: if the curvature under examination is very small compared to the rollers with which the robotic arm has been equipped, that surface is considered as a fitting and is treated with a narrower punch and a roller with a fitted edge that can better process that area. Again, for the same sub-surface, the next sequence of paths is generated following a direction perpendicular to the previous one in order to consolidate the area of surface that has just been laid (Fig. 9).
<figure><img src="image_547.png" /><figcaption>Figure 8 - Examples of determining the order of sub-surfaces to be processed during the robotic layup process. Sub-surfaces not yet chosen are colored yellow; chosen sub-surfaces are colored green as they are identified as the most suitable surface in that particular iteration. Above is an example of a mold in which six sub-surfaces were chosen; below is a more complex mold in which fourteen sub-surfaces were chosen.</figcaption></figure>

Once the order of the sub-surfaces has been identified for the layup process, the paths for the robotic arm are implemented. For each sub-surface, a sequence of paths is generated that are all parallel to a border of the sub-surface under consideration that is considered most significant due to its extension. These paths are spaced by a distance that corresponds to the thickness of the roller chosen to lay that specific part of the surface. The selection of the roller is done automatically by the algorithm by comparing the radius of curvature of the sub-surface under examination with that of the available rollers: if the curvature under examination is very small compared to the rollers with which the robotic arm has been equipped, that surface is considered as a fitting and is treated with a narrower punch and a roller with a fitted edge that can better process that area. Again, for the same sub-surface, the next sequence of paths is generated following a direction perpendicular to the previous one in order to consolidate the area of surface that has just been laid (Fig. 9).
<figure><img src="image_537.png" /><figcaption>Figure 9 - On the left are the selected sub-surfaces colored green; on the right are the paths generated for the specific selection.</figcaption></figure>

In order to correctly interpret the proposed work, it should be kept in mind that the algorithm implemented during the present study is intended to program robotic movements during the robotic layup process. For the effective application of the programmed movements, there is a need for careful set-up of the equipment and the mold. Not least is the correct positioning of the prepreg sheet in accordance with the drafting schedule calculated by the algorithm. If, for example, the algorithm has estimated that the best starting point of the layup is a concave area of the mold, proper pre-positioning of the prepreg in that area will promote the proper execution of the layup, avoiding unwanted adhesions of the end-effector with parts of the prepreg that are not intended to be processed at that particular point in the process. At the beginning of the implemented code, the dimensions of the rollers used (in terms of thickness and diameter) as well as the configuration of the end-effector are set to take into account the overall dimensions. The correct choice of rollers for the particular mold to be processed is an important aspect to consider. If the mold to be covered is small or has very tight radii of curvature, it is important to properly equip the robotic arm with appropriately sized rollers.

## Summary

The work presented in this paper provides an algorithm for automated programming of the lay-up of prepreg sheets on molds of complex geometry. This tool not only automatically implements the robotic lay-up movements and provides a simulation of them, but also automatically identifies the lay-up strategy that best fits the mold specified by the user. Clearly, the end user always has the possibility of modifying the decisions made automatically by the software. But what has been implemented during this work also allows the robotic layup process to be faster in the design phase of the layup strategy, as well as in the practical phase performed by a robotic arm and no longer manually by a laminator. This also entails a reduction of human intervention in decision-making and planning processes, which could be reduced to a simple supervision of the results produced by the computer at the end of the implemented code. In addition to this aspect, the following benefits were also achieved:

* the most suitable layup strategies can be chosen for a specific mold;

* the presence of defects could be reduced;

* coding and testing time can be optimized, and the process can become faster.

## References

[1] H. Parmar, T. Khan, F. Tucci, R. Umer, and P. Carlone, "Advanced Robotics and Additive Manufacturing of Composites: Towards a New Era in Industry 4.0 Key Words:".

[2] F. Tucci, D. Larrea-Wachtendorff, G. Ferrari and P. Carlone, "Pulling force analysis in injection pultrusion of glass/epoxy composites," in *Materials and Manufacturing Processes*, 2022, Volume 37, Issue 15, 1715–1726. https://doi.org/10.1080/10426914.2022.2049296
<figure><img src="image_548.png" /><figcaption>Figure 9 - On the left are the selected sub-surfaces colored green; on the right are the paths generated for the specific selection.</figcaption></figure>

In order to correctly interpret the proposed work, it should be kept in mind that the algorithm implemented during the present study is intended to program robotic movements during the robotic layup process. For the effective application of the programmed movements, there is a need for careful set-up of the equipment and the mold. Not least is the correct positioning of the prepreg sheet in accordance with the drafting schedule calculated by the algorithm. If, for example, the algorithm has estimated that the best starting point of the layup is a concave area of the mold, proper pre-positioning of the prepreg in that area will promote the proper execution of the layup, avoiding unwanted adhesions of the end-effector with parts of the prepreg that are not intended to be processed at that particular point in the process. At the beginning of the implemented code, the dimensions of the rollers used (in terms of thickness and diameter) as well as the configuration of the end-effector are set to take into account the overall dimensions. The correct choice of rollers for the particular mold to be processed is an important aspect to consider. If the mold to be covered is small or has very tight radii of curvature, it is important to properly equip the robotic arm with appropriately sized rollers.

## Summary

The work presented in this paper provides an algorithm for automated programming of the lay-up of prepreg sheets on molds of complex geometry. This tool not only automatically implements the robotic lay-up movements and provides a simulation of them, but also automatically identifies the lay-up strategy that best fits the mold specified by the user. Clearly, the end user always has the possibility of modifying the decisions made automatically by the software. But what has been implemented during this work also allows the robotic layup process to be faster in the design phase of the layup strategy, as well as in the practical phase performed by a robotic arm and no longer manually by a laminator. This also entails a reduction of human intervention in decision-making and planning processes, which could be reduced to a simple supervision of the results produced by the computer at the end of the implemented code. In addition to this aspect, the following benefits were also achieved:

* the most suitable layup strategies can be chosen for a specific mold;

* the presence of defects could be reduced;

* coding and testing time can be optimized, and the process can become faster.

## References

[1] H. Parmar, T. Khan, F. Tucci, R. Umer, and P. Carlone, "Advanced Robotics and Additive Manufacturing of Composites: Towards a New Era in Industry 4.0 Key Words:".

[2] F. Tucci, D. Larrea-Wachtendorff, G. Ferrari and P. Carlone, "Pulling force analysis in injection pultrusion of glass/epoxy composites," in *Materials and Manufacturing Processes*, 2022, Volume 37, Issue 15, 1715–1726. https://doi.org/10.1080/10426914.2022.2049296
[3] F. Tucci, P. Carlone, A.T. Silvestri, H. Parmar and A. Astarita, “Dissimilar friction stir lap welding of AA2198-AA6082: Process analysis and joint characterization,” in *CIRP Journal of Manufacturing Science and Technology*, Volume 35, 753 – 764. https://doi.org/10.1016/j.cirpj.2021.09.007

[4] F. Tucci, F. Rubino and P. Carlone, “Strain and temperature measurement in pultrusion processes by fiber Bragg grating sensors,” in *AIP Conference Proceedings*, 2018, volume 1960, article 020036. https://doi.org/10.1063/1.5034837

[5] A. Gambardella, V. Esperto, F. Tucci, and P. Carlone, “Automated programming for the robotic layup process,” in *Materials Research Proceedings*, Association of American Publishers, 2023, pp. 367–374. https://doi.org/10.21741/9781644902479-40

[6] J. S.-H. performance composites and undefined 2008, “ATL & AFP: Defining the megatrends in composite aerostructures,” *Ray; 1999*.

[7] F. Tucci, F. Rubino, G. Pasquino and P.Carlone, “Thermoplastic Pultrusion Process of Polypropylene/Glass Tapes,” *Polymers*, Volume 15, Issue 10, Article number 2374, 2023. https://doi.org/10.3390/polym15102374.

[8] A. Gambardella, V. Esperto, F. Tucci, and P. Carlone, “Defects Reduction in the Robotic Layup Process,” *Key Eng Mater*, vol. 926, pp. 1437–1444, Jul. 2022. https://doi.org/10.4028/p-7v9349

[9] V. Esperto, A. Gambardella, G. Pasquino, F. Tucci, M. Durante, and P. Carlone, “Modeling and simulation of the robotic layup of fibrous preforms for liquid composite molding,” in *ESAFORM 2021 - 24th International Conference on Material Forming*, PoPuPS (University of LiFge Library), 2021. https://doi.org/10.25518/esaform21.475

[10] M. P. Elkington, C. Ward, and K. D. Potter, “Automated Layup of Sheet Prepregs on Complex Moulds,” 2016. [Online]. Available: http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/

[11] R. K. Malhan et al., “Automated planning for robotic layup of composite prepreg,” *Robot Comput Integr Manuf*, vol. 67, Feb. 2021. https://doi.org/10.1016/j.rcim.2020.102020

[12] Andreas. Björnsson, *Automated layup and forming of prepreg laminates*. Linköping University Electronic Press, 2017.

[13] C. Grant, “Automated processes for composite aircraft structure,” *Industrial Robot*, vol. 33, no. 2, pp. 117–121, 2006. https://doi.org/10.1108/01439910610651428

[14] P. Kaufmann, G. Braun, A. Buchheim, and M. Malecha, “Automated draping of wide textiles on double curved surfaces,” in *ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics*, SciTePress, 2019, pp. 50–58. https://doi.org/10.5220/0007833200500058

[15] Technische Universität München, IEEE Robotics and Automation Society, and Institute of Electrical and Electronics Engineers, *2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) : 20-24 Aug. 2018*.

[16] C. Krogh et al., “A simple MATLAB draping code for fiber-reinforced composites with application to optimization of manufacturing process parameters,” *Structural and Multidisciplinary Optimization*, vol. 64, no. 1, pp. 457–471, Jul. 2021. https://doi.org/10.1007/s00158-021-02925-z
[3] F. Tucci, P. Carlone, A.T. Silvestri, H. Parmar and A. Astarita, “Dissimilar friction stir lap welding of AA2198-AA6082: Process analysis and joint characterization,” in *CIRP Journal of Manufacturing Science and Technology*, Volume 35, 753 – 764. https://doi.org/10.1016/j.cirpj.2021.09.007

[4] F. Tucci, F. Rubino and P. Carlone, “Strain and temperature measurement in pultrusion processes by fiber Bragg grating sensors,” in *AIP Conference Proceedings*, 2018, volume 1960, article 020036. https://doi.org/10.1063/1.5034837

[5] A. Gambardella, V. Esperto, F. Tucci, and P. Carlone, “Automated programming for the robotic layup process,” in *Materials Research Proceedings*, Association of American Publishers, 2023, pp. 367–374. https://doi.org/10.21741/9781644902479-40

[6] J. S.-H. performance composites and undefined 2008, “ATL & AFP: Defining the megatrends in composite aerostructures,” *Ray; 1999*.

[7] F. Tucci, F. Rubino, G. Pasquino and P.Carlone, “Thermoplastic Pultrusion Process of Polypropylene/Glass Tapes,” *Polymers*, Volume 15, Issue 10, Article number 2374, 2023. https://doi.org/10.3390/polym15102374.

[8] A. Gambardella, V. Esperto, F. Tucci, and P. Carlone, “Defects Reduction in the Robotic Layup Process,” *Key Eng Mater*, vol. 926, pp. 1437–1444, Jul. 2022. https://doi.org/10.4028/p-7v9349

[9] V. Esperto, A. Gambardella, G. Pasquino, F. Tucci, M. Durante, and P. Carlone, “Modeling and simulation of the robotic layup of fibrous preforms for liquid composite molding,” in *ESAFORM 2021 - 24th International Conference on Material Forming*, PoPuPS (University of LiFge Library), 2021. https://doi.org/10.25518/esaform21.475

[10] M. P. Elkington, C. Ward, and K. D. Potter, “Automated Layup of Sheet Prepregs on Complex Moulds,” 2016. [Online]. Available: http://www.bristol.ac.uk/red/research-policy/pure/user-guides/ebr-terms/

[11] R. K. Malhan et al., “Automated planning for robotic layup of composite prepreg,” *Robot Comput Integr Manuf*, vol. 67, Feb. 2021. https://doi.org/10.1016/j.rcim.2020.102020

[12] Andreas. Björnsson, *Automated layup and forming of prepreg laminates*. Linköping University Electronic Press, 2017.

[13] C. Grant, “Automated processes for composite aircraft structure,” *Industrial Robot*, vol. 33, no. 2, pp. 117–121, 2006. https://doi.org/10.1108/01439910610651428

[14] P. Kaufmann, G. Braun, A. Buchheim, and M. Malecha, “Automated draping of wide textiles on double curved surfaces,” in *ICINCO 2019 - Proceedings of the 16th International Conference on Informatics in Control, Automation and Robotics*, SciTePress, 2019, pp. 50–58. https://doi.org/10.5220/0007833200500058

[15] Technische Universität München, IEEE Robotics and Automation Society, and Institute of Electrical and Electronics Engineers, *2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) : 20-24 Aug. 2018*.

[16] C. Krogh et al., “A simple MATLAB draping code for fiber-reinforced composites with application to optimization of manufacturing process parameters,” *Structural and Multidisciplinary Optimization*, vol. 64, no. 1, pp. 457–471, Jul. 2021. https://doi.org/10.1007/s00158-021-02925-z
# Mechanical and chemical combined recycling process for CFRP scraps

BOCCARUSSO Luca¹,ª*, DE FAZIO Dario¹,ᵇ, DURANTE Massimo¹,ᶜ, FORMISANO Antonio¹,ᵈ, LANGELLA Antonio¹,ᵉ

¹Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale V. Tecchio 80, 80125 Naples, Italy

ªluca.boccarusso@unina.it, ᵇdario.defazio@unina.it, ᶜmdurante@unina.it, ᵈaformisa@unina.it, ᵉantgella@unina.it

**Keywords:** CFRPs, Recycling, Adhesion, Mechanical Properties

**Abstract.** Composite materials are increasingly employed in many industrial sectors. Among others, carbon fibers are primarily used as reinforcing agents in high-performance composites with synthetic resin matrices such as epoxies, polyimides, vinyl esters, phenolics, and certain thermoplastics. However, when carbon fibers are coupled with thermosetting matrices, the resulting composites are not easily recyclable. When these products reach their end-of-life (EoL), there are several difficulties in their recycling and in the reuse of the carbon fiber reinforcement. Several recycling process methods exist, but one of the most promising and investigated in recent years is the mechanical one, which, unlike other approaches, does not require the use of high temperatures to decompose the polymeric matrix. However, the presence of residual matrix on the surface of the fibers negatively affects their potential reuse for the production of new composites. In comparison to well-known mechanical recycling methods such as shredding, crushing, and hammer milling processes, this work presents a combined recycling process comprising mechanical recycling by milling and a soft chemical treatment at temperatures significantly lower than those reached during conventional thermal recycling processes. Recycled fibers were then used to produce new composite laminates using an epoxy resin as the matrix. The effects of the chemical treatment on the adhesion between recycled fibers and the new resin were evaluated through pull-out and bending tests.

**Introduction**

The growing exploration of lightweight structural materials with enhanced mechanical properties positions Carbon Fiber Reinforced Polymers (CFRPs) as a highly appealing alternative to their metallic counterparts. The growing demand for this material category in recent decades stems from its inherent tailorability and design flexibility, allowing for extensive use across different sectors such as aerospace, marine, automotive, and energy [1-5].

Indeed, there has been a noticeable surge in the demand for CFRP in these industrial fields, reaching around 70 kTons in 2010 and escalating to almost 170 kTons in 2020 [6]. It is important to highlight that the increasing demand for CFPRs is directly linked to an escalating production of virgin carbon fibers (vCF). This involves costly and energy-intensive processes that result in the emission of various gases, including ammonia (NH₃), carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxides (NOₓ), and other volatile compounds. A Life Cycle Assessment (LCA) analysis revealed that the energy consumption for vCF production ranges from approximately 198 to 595 MJ/kg [7], significantly higher than the 13 to 32 MJ/kg required for the production of virgin glass fiber (vGF). However, a further increase is anticipated in the imminent future, as it is estimated that the demand for carbon fiber composite materials will rise to around 190 kTons by 2050 [6, 8]. Consequently, the substantial amount of CFRP materials in all industrial sectors will

<figure><img src="image_538.png" /></figure>
# Mechanical and chemical combined recycling process for CFRP scraps

BOCCARUSSO Luca¹,ª*, DE FAZIO Dario¹,ᵇ, DURANTE Massimo¹,ᶜ, FORMISANO Antonio¹,ᵈ, LANGELLA Antonio¹,ᵉ

¹Department of Chemical, Materials and Production Engineering, University of Naples Federico II, Piazzale V. Tecchio 80, 80125 Naples, Italy

ªluca.boccarusso@unina.it, ᵇdario.defazio@unina.it, ᶜmdurante@unina.it, ᵈaformisa@unina.it, ᵉantgella@unina.it

**Keywords:** CFRPs, Recycling, Adhesion, Mechanical Properties

**Abstract.** Composite materials are increasingly employed in many industrial sectors. Among others, carbon fibers are primarily used as reinforcing agents in high-performance composites with synthetic resin matrices such as epoxies, polyimides, vinyl esters, phenolics, and certain thermoplastics. However, when carbon fibers are coupled with thermosetting matrices, the resulting composites are not easily recyclable. When these products reach their end-of-life (EoL), there are several difficulties in their recycling and in the reuse of the carbon fiber reinforcement. Several recycling process methods exist, but one of the most promising and investigated in recent years is the mechanical one, which, unlike other approaches, does not require the use of high temperatures to decompose the polymeric matrix. However, the presence of residual matrix on the surface of the fibers negatively affects their potential reuse for the production of new composites. In comparison to well-known mechanical recycling methods such as shredding, crushing, and hammer milling processes, this work presents a combined recycling process comprising mechanical recycling by milling and a soft chemical treatment at temperatures significantly lower than those reached during conventional thermal recycling processes. Recycled fibers were then used to produce new composite laminates using an epoxy resin as the matrix. The effects of the chemical treatment on the adhesion between recycled fibers and the new resin were evaluated through pull-out and bending tests.

**Introduction**

The growing exploration of lightweight structural materials with enhanced mechanical properties positions Carbon Fiber Reinforced Polymers (CFRPs) as a highly appealing alternative to their metallic counterparts. The growing demand for this material category in recent decades stems from its inherent tailorability and design flexibility, allowing for extensive use across different sectors such as aerospace, marine, automotive, and energy [1-5].

Indeed, there has been a noticeable surge in the demand for CFRP in these industrial fields, reaching around 70 kTons in 2010 and escalating to almost 170 kTons in 2020 [6]. It is important to highlight that the increasing demand for CFPRs is directly linked to an escalating production of virgin carbon fibers (vCF). This involves costly and energy-intensive processes that result in the emission of various gases, including ammonia (NH₃), carbon monoxide (CO), carbon dioxide (CO₂), nitrogen oxides (NOₓ), and other volatile compounds. A Life Cycle Assessment (LCA) analysis revealed that the energy consumption for vCF production ranges from approximately 198 to 595 MJ/kg [7], significantly higher than the 13 to 32 MJ/kg required for the production of virgin glass fiber (vGF). However, a further increase is anticipated in the imminent future, as it is estimated that the demand for carbon fiber composite materials will rise to around 190 kTons by 2050 [6, 8]. Consequently, the substantial amount of CFRP materials in all industrial sectors will

<figure><img src="image_549.png" /></figure>
inevitably lead to an increase in the volume of waste materials that must be managed when they
are decommissioned at the end of their life cycle.

In this context, there is a clear imperative for a secondary application of these materials, aligning
with the principles of a circular economy. This approach seeks to reuse discarded materials,
concurrently reducing additional waste production and the use of harmful chemicals [9].

Recognizing the imminent challenge posed by the disposal and waste management of end-of-life CFRPs, the European Union has implemented directives like 2008/98/EC and 2000/53/EC. These directives focus on principles of prevention, smart utilization of composite materials, incorporation of recycled fibers into new industrial products, and pollution payment [8, 10].

Presently, a significant portion of discarded materials typically ends up in landfills or is incinerated to generate energy. However, these methods are not in line with the principles of the circular economy and cannot be considered as an appropriate way to recover carbon fibers [11]. In contrast, composite recycling processes comprising thermal, chemical, and mechanical methods, emerges as a more sustainable process aligning with circular economy principles [12]. Chemical and thermal methods have been widely investigated in recent years, and despite achieving interesting mechanical properties in composites produced using these recycled carbon fibers (rCF), both methods are characterized by high costs, specific laboratory apparatus that can impede easy industrial scaling up, long processing times, and environmental impact [13].

The mechanical recycling process usually consists of a reduction of CFRP materials in small pieces using shredding, milling or grinding techniques that on some occasions can be combined to obtain recycled material with the desired dimension [14].

A survey of mechanical recycling processes [8] indicates that these techniques result in the generation of short recovered fibers. Due to their geometric features, size, and residual matrix content, these fibers significantly impair the overall mechanical properties of the recycled composite material. Such limitations pose challenges to the utilization of mechanically recycled composites as load-bearing structures, restricting their application to occasions where they can serve as fillers in composite materials with lower properties. Furthermore, the adoption of shredding or hammer-milling processes necessitates the use of specialized industrial machinery, characterized by low flexibility and substantial size.

To attempt to overcome these aspects, this study explores the possibility of using a combination of two processes (mechanical and chemical), aiming to harness the advantages of each one while mitigating their respective drawbacks. In detail, the study considered the use of a soft chemical treatment based on acetone applied to coarse fibers obtained from a mechanical recycling process through milling. Consequently, the effect of the chemical treatment on the adhesion strength between fibers and matrix, as well as the flexural properties of recycled samples, was investigated.

## Experimental Procedure

For the experimental campaign, carbon fibers tows were manually extracted from a 200 g/m² twill wave carbon/epoxy prepreg (150 mm x 150 mm) supplied by Toray. Each tow was placed on a mould plate, paying attention to the optimal alignment of the fibres, and then cured using the compression molding technique at 150 °C for 8 hours with a pressure of approximately 0.8 MPa. At the end of the polymerization phase, all tows were carefully cut into bundles characterized by an overall length of almost 100 mm. Therefore, aiming to understand the effect of the chemical treatment on the adhesion efficiency between the carbon fibers and a new epoxy matrix, the polymerized tows were treated with a peroxide and acetone solution ($H_2O_2$ 20wt.% and $C_3H_6O$ 80wt.%). Subsequently, 10 tows were placed in a cylindrical stainless-steel vessel (110 mm in length and 28 mm in diameter) tightly sealed together with 30g of the chemical solution. The vessel was placed in an oven for 1 hour, and two treatment temperatures were investigated: 100°C and 200°C.
inevitably lead to an increase in the volume of waste materials that must be managed when they are decommissioned at the end of their life cycle.

In this context, there is a clear imperative for a secondary application of these materials, aligning with the principles of a circular economy. This approach seeks to reuse discarded materials, concurrently reducing additional waste production and the use of harmful chemicals [9].

Recognizing the imminent challenge posed by the disposal and waste management of end-of-life CFRPs, the European Union has implemented directives like 2008/98/EC and 2000/53/EC. These directives focus on principles of prevention, smart utilization of composite materials, incorporation of recycled fibers into new industrial products, and pollution payment [8, 10].

Presently, a significant portion of discarded materials typically ends up in landfills or is incinerated to generate energy. However, these methods are not in line with the principles of the circular economy and cannot be considered as an appropriate way to recover carbon fibers [11]. In contrast, composite recycling processes comprising thermal, chemical, and mechanical methods, emerges as a more sustainable process aligning with circular economy principles [12]. Chemical and thermal methods have been widely investigated in recent years, and despite achieving interesting mechanical properties in composites produced using these recycled carbon fibers (rCF), both methods are characterized by high costs, specific laboratory apparatus that can impede easy industrial scaling up, long processing times, and environmental impact [13].

The mechanical recycling process usually consists of a reduction of CFRP materials in small pieces using shredding, milling or grinding techniques that on some occasions can be combined to obtain recycled material with the desired dimension [14].

A survey of mechanical recycling processes [8] indicates that these techniques result in the generation of short recovered fibers. Due to their geometric features, size, and residual matrix content, these fibers significantly impair the overall mechanical properties of the recycled composite material. Such limitations pose challenges to the utilization of mechanically recycled composites as load-bearing structures, restricting their application to occasions where they can serve as fillers in composite materials with lower properties. Furthermore, the adoption of shredding or hammer-milling processes necessitates the use of specialized industrial machinery, characterized by low flexibility and substantial size.

To attempt to overcome these aspects, this study explores the possibility of using a combination of two processes (mechanical and chemical), aiming to harness the advantages of each one while mitigating their respective drawbacks. In detail, the study considered the use of a soft chemical treatment based on acetone applied to coarse fibers obtained from a mechanical recycling process through milling. Consequently, the effect of the chemical treatment on the adhesion strength between fibers and matrix, as well as the flexural properties of recycled samples, was investigated.

## Experimental Procedure

For the experimental campaign, carbon fibers tows were manually extracted from a 200 g/m² twill wave carbon/epoxy prepreg (150 mm x 150 mm) supplied by Toray. Each tow was placed on a mould plate, paying attention to the optimal alignment of the fibres, and then cured using the compression molding technique at 150 °C for 8 hours with a pressure of approximately 0.8 MPa. At the end of the polymerization phase, all tows were carefully cut into bundles characterized by an overall length of almost 100 mm. Therefore, aiming to understand the effect of the chemical treatment on the adhesion efficiency between the carbon fibers and a new epoxy matrix, the polymerized tows were treated with a peroxide and acetone solution ($H_2O_2$ 20wt.% and $C_3H_6O$ 80wt.%). Subsequently, 10 tows were placed in a cylindrical stainless-steel vessel (110 mm in length and 28 mm in diameter) tightly sealed together with 30g of the chemical solution. The vessel was placed in an oven for 1 hour, and two treatment temperatures were investigated: 100°C and 200°C.
At the end of the chemical treatment, the tows were cooled in air to room temperature and then washed with acetone to remove any trace of the dissolved matrix from the fiber surface.

To produce pull-out samples for both untreated and treated tows, they were immersed in a resin bath (Fig. 1). However, before resin casting, the same prepreg fabric used earlier was employed as tabs applied to the free end of the carbon tow. A total of 3 carbon tows were aligned on the tab, allowing for curing through the compression molding technique at 150 °C for 1 hour with a pressure of approximately 0.3 MPa.

Subsequently, the carbon tows were embedded for a length of 5 mm in an SX10 epoxy resin supplied by Mates and were held upright to ensure the defined embedded bundle length. The epoxy resin was then allowed to cure for a period of 24 hours at room temperature. At the end of the sample preparation, scissors were used to separate the specimens from each other.

<figure><img src="image_539.png" /><figcaption>Figure 1. Pull-out samples configuration (image not to scale).</figcaption></figure>

Pull-out tests were conducted using a universal testing machine MTS Alliance RT/50 equipped with a 1kN piezoelectric load cell. A tensile load was applied along the fiber direction of all samples under inspection adopting a testing speed of 1 mm/min.

After evaluating the effect of the chemical treatment on fiber/matrix adhesion, samples for flexural testing were produced using recycled fibers. To achieve this, 20 layers of CFRP pre-preg were used to manufacture a virgin CFRP laminate (200 mm x 200 mm x 4 mm) through the compression molding process. After the laminate polymerization, it underwent a mechanical recycling process through milling. For this purpose, a CNC machine (C.B. Ferrari) and a three-flute HSS end mill tool (20 mm in diameter) were used for the peripheral down-mill machining of the produced laminate. Machining parameters included a radial depth of cut of 3.00 mm, a feed rate (f) of 1000 mm/min, and a spindle speed (s) of 1000 rpm.

The resulting CFRP chips were then sieved using a shaking table to achieve satisfactory separation between fibers with a mean size > 0.1 mm (referred to as coarse fibers) and powders (mean size < 0.1 mm). The powders were discarded and not used in this study. Subsequently, the coarse fibers underwent the chemical treatment as described earlier.

To produce recycled CFRP samples, the obtained recycled fibers (both treated and untreated) were washed in acetone and then dried at 70°C for 30 minutes. Subsequently, the fibers were manually impregnated with SX10 epoxy resin and placed in a mold at room temperature for 24
At the end of the chemical treatment, the tows were cooled in air to room temperature and then washed with acetone to remove any trace of the dissolved matrix from the fiber surface.

To produce pull-out samples for both untreated and treated tows, they were immersed in a resin bath (Fig. 1). However, before resin casting, the same prepreg fabric used earlier was employed as tabs applied to the free end of the carbon tow. A total of 3 carbon tows were aligned on the tab, allowing for curing through the compression molding technique at 150 °C for 1 hour with a pressure of approximately 0.3 MPa.

Subsequently, the carbon tows were embedded for a length of 5 mm in an SX10 epoxy resin supplied by Mates and were held upright to ensure the defined embedded bundle length. The epoxy resin was then allowed to cure for a period of 24 hours at room temperature. At the end of the sample preparation, scissors were used to separate the specimens from each other.

<figure><img src="image_550.png" /><figcaption>Figure 1. Pull-out samples configuration (image not to scale).</figcaption></figure>

Pull-out tests were conducted using a universal testing machine MTS Alliance RT/50 equipped with a 1kN piezoelectric load cell. A tensile load was applied along the fiber direction of all samples under inspection adopting a testing speed of 1 mm/min.

After evaluating the effect of the chemical treatment on fiber/matrix adhesion, samples for flexural testing were produced using recycled fibers. To achieve this, 20 layers of CFRP pre-preg were used to manufacture a virgin CFRP laminate (200 mm x 200 mm x 4 mm) through the compression molding process. After the laminate polymerization, it underwent a mechanical recycling process through milling. For this purpose, a CNC machine (C.B. Ferrari) and a three-flute HSS end mill tool (20 mm in diameter) were used for the peripheral down-mill machining of the produced laminate. Machining parameters included a radial depth of cut of 3.00 mm, a feed rate (f) of 1000 mm/min, and a spindle speed (s) of 1000 rpm.

The resulting CFRP chips were then sieved using a shaking table to achieve satisfactory separation between fibers with a mean size > 0.1 mm (referred to as coarse fibers) and powders (mean size < 0.1 mm). The powders were discarded and not used in this study. Subsequently, the coarse fibers underwent the chemical treatment as described earlier.

To produce recycled CFRP samples, the obtained recycled fibers (both treated and untreated) were washed in acetone and then dried at 70°C for 30 minutes. Subsequently, the fibers were manually impregnated with SX10 epoxy resin and placed in a mold at room temperature for 24
hours to produce different samples that differ in the type of reinforcement fibers, i.e. treated and untreated. The fiber weight percentage content was fixed at 25%.

The flexural behavior of each sample type was then investigated according to ASTM D790 standard, with three specimens tested for each sample type. For this purpose, three-point bending tests were carried out using the same testing machine adopted for the pull-out test. Specimens (120 mm x 21 mm x 4 mm) were tested with a span length of 64 mm (span-to-depth ratio equal to 16). Additionally, three unreinforced samples were also tested as reference. The flexural strength was calculated by means of the following equation:

$$ \sigma_f = \frac{3FL}{2bt^2} $$

All tested specimens were examined using a 3D Digital Microscope HRX-01.

## Results and discussions

In Fig. 2, the results of the pull-out tests are presented. These results clearly demonstrate the positive impact of chemical treatments on the adhesion force between the carbon fibers and the epoxy resin. The pull-out force increased by approximately 152% compared to that of untreated fibers when a chemical treatment at 100 °C was performed and by about 252% for the chemical treatment at 200 °C. These enhancements can be attributed to the synergistic effects of the constituents in the chemical solution.

Specifically, acetone ($C_3H_6O$) played a crucial role in cleaning the fibers by removing the original epoxy matrix from the fiber surface. Thorough cleaning is essential to ensure optimal adhesion between the fibers and resins.

On the other hand, hydrogen peroxide ($H_2O_2$) was employed to activate the surface of the fibers through a controlled oxidation process. As a known oxidizing agent, hydrogen peroxide enhanced the chemical reactivity of the fiber surface, thereby increasing their susceptibility to chemical bonding with polymeric resins, such as epoxy resins.

These results were further validated by examining Fig. 3, where the samples at the end of the pull-out tests were illustrated. In this figure, the embedded length was denoted as L1, and the capillary rise length was denoted as L2. Notably, the capillary rise length exhibited an increase when the chemical treatment was applied, reaching its peak value of approximately 1760 µm at a treatment temperature of 200 °C. The capillary rise length is directly linked to the wettability of the fibers by the resin; hence, a higher length signifies improved wettability.
hours to produce different samples that differ in the type of reinforcement fibers, i.e. treated and untreated. The fiber weight percentage content was fixed at 25%.

The flexural behavior of each sample type was then investigated according to ASTM D790 standard, with three specimens tested for each sample type. For this purpose, three-point bending tests were carried out using the same testing machine adopted for the pull-out test. Specimens (120 mm x 21 mm x 4 mm) were tested with a span length of 64 mm (span-to-depth ratio equal to 16). Additionally, three unreinforced samples were also tested as reference. The flexural strength was calculated by means of the following equation:

$$ \sigma_f = \frac{3FL}{2bt^2} $$

All tested specimens were examined using a 3D Digital Microscope HRX-01.

## Results and discussions

In Fig. 2, the results of the pull-out tests are presented. These results clearly demonstrate the positive impact of chemical treatments on the adhesion force between the carbon fibers and the epoxy resin. The pull-out force increased by approximately 152% compared to that of untreated fibers when a chemical treatment at 100 °C was performed and by about 252% for the chemical treatment at 200 °C. These enhancements can be attributed to the synergistic effects of the constituents in the chemical solution.

Specifically, acetone ($C_3H_6O$) played a crucial role in cleaning the fibers by removing the original epoxy matrix from the fiber surface. Thorough cleaning is essential to ensure optimal adhesion between the fibers and resins.

On the other hand, hydrogen peroxide ($H_2O_2$) was employed to activate the surface of the fibers through a controlled oxidation process. As a known oxidizing agent, hydrogen peroxide enhanced the chemical reactivity of the fiber surface, thereby increasing their susceptibility to chemical bonding with polymeric resins, such as epoxy resins.

These results were further validated by examining Fig. 3, where the samples at the end of the pull-out tests were illustrated. In this figure, the embedded length was denoted as L1, and the capillary rise length was denoted as L2. Notably, the capillary rise length exhibited an increase when the chemical treatment was applied, reaching its peak value of approximately 1760 µm at a treatment temperature of 200 °C. The capillary rise length is directly linked to the wettability of the fibers by the resin; hence, a higher length signifies improved wettability.
<figure><img src="image_540.png" /><figcaption>Figure 2. Typical force vs displacement curves carried out from the pull-out test for each sample type.</figcaption></figure>

It is also interesting to note that on the portions of the untreated carbon tow that was immersed in the resin (Fig.3a), the surface appears quite smooth and free of matrix residues, suggesting a relatively low adhesion with the epoxy resin SX10. In contrast, for the treated carbon tow, the presence of residual epoxy resin was observed (Fig.3b). It is important to highlight that no fiber failure have ever been observed and that all samples at the end of the test appeared as depicted in Fig.3c, where, by way of example, a specimen treated at 200°C is shown. To obtain the tows depicted in Figs.3a and 3b, these were manually removed from the resin mortar.

<figure><img src="image_541.png" /><figcaption>Figure 3. Typical samples at the end of the pull-out tests: a) untreated fibres, b) treated fibres at 100 °C and c) treated fibres at 200 °C.</figcaption></figure>

Interesting results were also obtained from the three-point bending tests. In Fig.4, the results of the bending tests are presented, including a reference representing an unreinforced sample type.
<figure><img src="image_551.png" /><figcaption>Figure 2. Typical force vs displacement curves carried out from the pull-out test for each sample type.</figcaption></figure>

It is also interesting to note that on the portions of the untreated carbon tow that was immersed in the resin (Fig.3a), the surface appears quite smooth and free of matrix residues, suggesting a relatively low adhesion with the epoxy resin SX10. In contrast, for the treated carbon tow, the presence of residual epoxy resin was observed (Fig.3b). It is important to highlight that no fiber failure have ever been observed and that all samples at the end of the test appeared as depicted in Fig.3c, where, by way of example, a specimen treated at 200°C is shown. To obtain the tows depicted in Figs.3a and 3b, these were manually removed from the resin mortar.

<figure><img src="image_552.png" /><figcaption>Figure 3. Typical samples at the end of the pull-out tests: a) untreated fibres, b) treated fibres at 100 °C and c) treated fibres at 200 °C.</figcaption></figure>

Interesting results were also obtained from the three-point bending tests. In Fig.4, the results of the bending tests are presented, including a reference representing an unreinforced sample type.
The flexural stress-strain curves offer valuable insights into the mechanical properties of the recycled samples in terms of the interfacial adhesion efficiency between the surface of the recovered fibers and the new epoxy matrix.

Specifically, it was observed that all reinforced samples exhibit a linear elastic response with a reduction in the strain at breaking compared to the reference sample type (pure epoxy sample). However, they exhibited higher flexural strength and modulus. The results emphasize how the chemical treatment significantly influences the flexural properties, both in terms of strength and modulus. The flexural strength of specimens reinforced with fibers subjected to chemical treatment, at 100°C and 200°C respectively, experienced an increase of approximately 55% and 100% compared to samples reinforced with untreated fibers. Similarly, the flexural modulus showed an increase of about 75% and 155%, depending on the treatment temperature.

These results were further confirmed by examining Fig. 5, where typical samples at the end of the bending tests were reported. It is possible to highlight that the sample reinforced with untreated recycled fibers (Fig. 5a) is significantly affected by pull-out and fiber bundle debonding phenomena near the failure region. As observed in the pull-out tests, the surface of the untreated reinforcement is devoid of any trace of the epoxy matrix. This condition can be attributed to the poor adhesion between the untreated recovered fibers and the new matrix.

On the other hand, when observing the sample produced with the treated recovered reinforcement (Fig. 5b), a well-defined failure region with limited fiber pull-out phenomena can be detected. In addition, compared to the untreated sample, the individual fibers that characterize the tow are discernible due to the chemical treatment that dissolved the old matrix within the fiber bundle. Moreover, as highlighted in Fig. 3, traces of epoxy resin residues are observed on the fiber's surface, indicating increased adhesion at the fiber-matrix interface. These factors contribute to the enhanced flexural properties exhibited by the treated sample.

<figure><img src="image_542.png" /><figcaption>Figure 4. Typical stress-strain curves (a) and mean flexural strength and modulus (b) for each sample category.</figcaption></figure>
The flexural stress-strain curves offer valuable insights into the mechanical properties of the recycled samples in terms of the interfacial adhesion efficiency between the surface of the recovered fibers and the new epoxy matrix.

Specifically, it was observed that all reinforced samples exhibit a linear elastic response with a reduction in the strain at breaking compared to the reference sample type (pure epoxy sample). However, they exhibited higher flexural strength and modulus. The results emphasize how the chemical treatment significantly influences the flexural properties, both in terms of strength and modulus. The flexural strength of specimens reinforced with fibers subjected to chemical treatment, at 100°C and 200°C respectively, experienced an increase of approximately 55% and 100% compared to samples reinforced with untreated fibers. Similarly, the flexural modulus showed an increase of about 75% and 155%, depending on the treatment temperature.

These results were further confirmed by examining Fig. 5, where typical samples at the end of the bending tests were reported. It is possible to highlight that the sample reinforced with untreated recycled fibers (Fig. 5a) is significantly affected by pull-out and fiber bundle debonding phenomena near the failure region. As observed in the pull-out tests, the surface of the untreated reinforcement is devoid of any trace of the epoxy matrix. This condition can be attributed to the poor adhesion between the untreated recovered fibers and the new matrix.

On the other hand, when observing the sample produced with the treated recovered reinforcement (Fig. 5b), a well-defined failure region with limited fiber pull-out phenomena can be detected. In addition, compared to the untreated sample, the individual fibers that characterize the tow are discernible due to the chemical treatment that dissolved the old matrix within the fiber bundle. Moreover, as highlighted in Fig. 3, traces of epoxy resin residues are observed on the fiber's surface, indicating increased adhesion at the fiber-matrix interface. These factors contribute to the enhanced flexural properties exhibited by the treated sample.

<figure><img src="image_553.png" /><figcaption>Figure 4. Typical stress-strain curves (a) and mean flexural strength and modulus (b) for each sample category.</figcaption></figure>
<figure><img src="image_543.png" /><figcaption>Figure 5. Typical detail area of the fracture zone of specimens at the end of the flexural test: (a) specimen reinforced with untreated fibres, (b) specimen reinforced with fibres treated at 200°C.</figcaption></figure>

**Conclusions**

In this study, a preliminary investigation into the potential utilization of a mechanical recycling process involving milling, combined with a soft chemical process using a solution based on acetone and peroxide, has been presented. The results demonstrate that mechanical milling can be a valuable approach for mechanical recycling, utilizing common CNC machines. This is in contrast to the common use of shredding or hammer-milling processes, which implies the use of dedicated industrial machines with low flexibility and large size.

The use of soft chemical treatment has indeed proven effective in removing the residual matrix from the fibers. In contrast to common chemical recycling processes, no particularly hazardous solvents or excessively high temperatures were employed.

Pull-out test results have shown that the adhesion strength between chemically treated carbon tows and the new epoxy resin can increase by 250% compared to untreated tows. This result is further emphasized by bending tests conducted on specimens consisting of recycled short fibers and epoxy resin as the matrix. In this case, increases of 100% and 155% were observed, respectively, for flexural strength and flexural modulus.

**References**

[1] F. Pinto, L. Boccarusso, D. De Fazio, S. Cuomo, M. Durante, M. Meo, Carbon/hemp bio-hybrid composites: Effects of the stacking sequence on flexural, damping and impact properties, Compos. Struct. 242 (2020) 112148. https://doi.org/10.1016/j.compstruct.2020.112148
<figure><img src="image_554.png" /><figcaption>Figure 5. Typical detail area of the fracture zone of specimens at the end of the flexural test: (a) specimen reinforced with untreated fibres, (b) specimen reinforced with fibres treated at 200°C.</figcaption></figure>

**Conclusions**

In this study, a preliminary investigation into the potential utilization of a mechanical recycling process involving milling, combined with a soft chemical process using a solution based on acetone and peroxide, has been presented. The results demonstrate that mechanical milling can be a valuable approach for mechanical recycling, utilizing common CNC machines. This is in contrast to the common use of shredding or hammer-milling processes, which implies the use of dedicated industrial machines with low flexibility and large size.

The use of soft chemical treatment has indeed proven effective in removing the residual matrix from the fibers. In contrast to common chemical recycling processes, no particularly hazardous solvents or excessively high temperatures were employed.

Pull-out test results have shown that the adhesion strength between chemically treated carbon tows and the new epoxy resin can increase by 250% compared to untreated tows. This result is further emphasized by bending tests conducted on specimens consisting of recycled short fibers and epoxy resin as the matrix. In this case, increases of 100% and 155% were observed, respectively, for flexural strength and flexural modulus.

**References**

[1] F. Pinto, L. Boccarusso, D. De Fazio, S. Cuomo, M. Durante, M. Meo, Carbon/hemp bio-hybrid composites: Effects of the stacking sequence on flexural, damping and impact properties, Compos. Struct. 242 (2020) 112148. https://doi.org/10.1016/j.compstruct.2020.112148
[2] P. Boisse, R. Akkerman, P. Carlone, L. Kärger, S.V. Lomov, J.A. Sherwood, Advances in composite forming through 25 years of ESAFORM, Int. J Mater. Form. (2022) 15-39. https://doi.org/10.1007/s12289-022-01682-8

[3] A. Viscusi, M. Durante, A. Astarita, L. Boccarusso, L. Carrino, A.S. Perna, Experimental Evaluation of Metallic Coating on Polymer by Cold Spray, Proc Manufact. 47 (2020) 761-765. https://doi.org/10.1016/j.promfg.2020.04.232

[4] I. Papa, P. Russo, A. Astarita, A. Viscusi, A.S. Perna, L. Carrino, V. Lopresto, Impact behaviour of a novel composite structure made of a polymer reinforced composite with a 3D printed metallic coating, Comp. Struct. 245 (2020) 112346. https://doi.org/10.1016/j.compstruct.2020.112346

[5] A. Formisano, L. Carrino, D. De Fazio, M. Durante, A. Viscusi, Enhanced Aluminium Foam Based Cylindrical Sandwiches: Bending Behaviour and Numerical Modeling, Int. Rev. Model. Simul. 11 (2018). https://doi.org/10.15866/iremos.v11i4.15631

[6] Y. Yang, R. Boom, B. Irion, D.J. Van Heerden, P. Kuiper, H. de Wit, Recycling of composite materials, Chem. Eng. Process. Process Intensif. 51 (2012) 53-68. https://doi.org/10.1016/j.cep.2011.09.007

[7] F. Meng F, E.A. Olivetti, Y. Zhao, J.C. Chang, SJ. Pickering, J. McKechnie, Comparing Life Cycle Energy and Global Warming Potential of Carbon Fiber Composite Recycling Technologies and Waste Management Options. ACS Sustain. Chem. Eng. 6 (2018) 9854-65. https://doi.org/10.1021/acssuschemeng.8b01026

[8] D. De Fazio, L. Boccarusso, A. Formisano, A. Viscusi, M. Durante, A Review on the Recycling Technologies of Fibre-Reinforced Plastic (FRP) Materials Used in Industrial Fields, J. Mar. Sci. Eng. 11 (2023) 851. https://doi.org/10.3390/jmse11040851

[9] E. MacArthur, Towards the circular economy, economic and business rationale for an accelerated transition, Ellen MacArthur Foundation, Cowes, UK, 2013.

[10] S. Gharde, B. Kandasubramanian, Mechanothermal and chemical recycling methodologies for the Fibre Reinforced Plastic (FRP), Environmental Technology & Innovation, 14 (2019) 100311. https://doi.org/10.1016/j.eti.2019.01.005

[11] D. De Fazio, L. Boccarusso, A. Formisano, A. Langella, F. Memola Capece Minutolo and M. Durante, Mechanical recycling of CFRPs: manufacturing and characterization of recycled laminates, Italian Manufacturing Association Conference: XVI AITeM, Materials Research Forum LLC, 35 (2023). https://doi.org/10.21741/9781644902714-48

[12] S.J. Pickering, Recycling technologies for thermoset composite materials-current status, Composites Part A: Applied Science and Manufacturing, 37 (2006) 1206-1215. https://doi.org/10.1016/j.compositesa.2005.05.030

[13] L. Mazzocchetti, T. Benelli, E. D'Angelo, C. Leonardi, G. Zattini, L. Giorgini, Validation of carbon fibers recycling by pyrogasification: The influence of oxidation conditions to obtain clean fibers and promote fiber/matrix adhesion in epoxy composites, Compos. Sci. Technol. 112 (2018) 504-514. https://doi.org/10.1016/j.compscitech.2018.07.007

[14] M. Durante, L. Boccarusso, D. De Fazio, A. Formisano, A. Langella, Investigation on the Mechanical Recycling of Carbon Fiber-Reinforced Polymers by Peripheral Down-Milling, Polymers 15 (2023) 854. https://doi.org/10.3390/polym15040854
[2] P. Boisse, R. Akkerman, P. Carlone, L. Kärger, S.V. Lomov, J.A. Sherwood, Advances in composite forming through 25 years of ESAFORM, Int. J Mater. Form. (2022) 15-39. https://doi.org/10.1007/s12289-022-01682-8

[3] A. Viscusi, M. Durante, A. Astarita, L. Boccarusso, L. Carrino, A.S. Perna, Experimental Evaluation of Metallic Coating on Polymer by Cold Spray, Proc Manufact. 47 (2020) 761-765. https://doi.org/10.1016/j.promfg.2020.04.232

[4] I. Papa, P. Russo, A. Astarita, A. Viscusi, A.S. Perna, L. Carrino, V. Lopresto, Impact behaviour of a novel composite structure made of a polymer reinforced composite with a 3D printed metallic coating, Comp. Struct. 245 (2020) 112346. https://doi.org/10.1016/j.compstruct.2020.112346

[5] A. Formisano, L. Carrino, D. De Fazio, M. Durante, A. Viscusi, Enhanced Aluminium Foam Based Cylindrical Sandwiches: Bending Behaviour and Numerical Modeling, Int. Rev. Model. Simul. 11 (2018). https://doi.org/10.15866/iremos.v11i4.15631

[6] Y. Yang, R. Boom, B. Irion, D.J. Van Heerden, P. Kuiper, H. de Wit, Recycling of composite materials, Chem. Eng. Process. Process Intensif. 51 (2012) 53-68. https://doi.org/10.1016/j.cep.2011.09.007

[7] F. Meng F, E.A. Olivetti, Y. Zhao, J.C. Chang, SJ. Pickering, J. McKechnie, Comparing Life Cycle Energy and Global Warming Potential of Carbon Fiber Composite Recycling Technologies and Waste Management Options. ACS Sustain. Chem. Eng. 6 (2018) 9854-65. https://doi.org/10.1021/acssuschemeng.8b01026

[8] D. De Fazio, L. Boccarusso, A. Formisano, A. Viscusi, M. Durante, A Review on the Recycling Technologies of Fibre-Reinforced Plastic (FRP) Materials Used in Industrial Fields, J. Mar. Sci. Eng. 11 (2023) 851. https://doi.org/10.3390/jmse11040851

[9] E. MacArthur, Towards the circular economy, economic and business rationale for an accelerated transition, Ellen MacArthur Foundation, Cowes, UK, 2013.

[10] S. Gharde, B. Kandasubramanian, Mechanothermal and chemical recycling methodologies for the Fibre Reinforced Plastic (FRP), Environmental Technology & Innovation, 14 (2019) 100311. https://doi.org/10.1016/j.eti.2019.01.005

[11] D. De Fazio, L. Boccarusso, A. Formisano, A. Langella, F. Memola Capece Minutolo and M. Durante, Mechanical recycling of CFRPs: manufacturing and characterization of recycled laminates, Italian Manufacturing Association Conference: XVI AITeM, Materials Research Forum LLC, 35 (2023). https://doi.org/10.21741/9781644902714-48

[12] S.J. Pickering, Recycling technologies for thermoset composite materials-current status, Composites Part A: Applied Science and Manufacturing, 37 (2006) 1206-1215. https://doi.org/10.1016/j.compositesa.2005.05.030

[13] L. Mazzocchetti, T. Benelli, E. D'Angelo, C. Leonardi, G. Zattini, L. Giorgini, Validation of carbon fibers recycling by pyrogasification: The influence of oxidation conditions to obtain clean fibers and promote fiber/matrix adhesion in epoxy composites, Compos. Sci. Technol. 112 (2018) 504-514. https://doi.org/10.1016/j.compscitech.2018.07.007

[14] M. Durante, L. Boccarusso, D. De Fazio, A. Formisano, A. Langella, Investigation on the Mechanical Recycling of Carbon Fiber-Reinforced Polymers by Peripheral Down-Milling, Polymers 15 (2023) 854. https://doi.org/10.3390/polym15040854
# Simulation and monitoring of the infusion of thick composites with thermoplastic acrylic resin

SIDDIG Nihad¹,ᵃ*, LE BOT Philippe¹,ᵇ, FOUCHÉ Olivier¹,ᶜ and DENIS Yvan¹,ᵈ

¹ Nantes Université, IRT Jules Verne, 1 Mail des 20 000 Lieues, 44340 Bouguenais, France

ᵃnihad.siddig@irt-jules-verne.fr, ᵇphilippe.lebot@irt-jules-verne.fr,

ᶜolivier.fouche@irt-jules-verne.fr, ᵈyvan.denis@irt-jules-verne.fr

**Keywords:** Composites, Thermoplastic, Monitoring, Simulation, Polymerization

**Abstract.** The ZEBRA project aims to advance the circular economy by creating wind turbine blades that can be completely recycled. Currently, Wind turbine blades are fabricated through Vacuum-Assisted Resin Infusion (VARI) using thermoset resins. In this endeavor, the recyclable thermoplastic resin Elium® from Arkema is utilized as a sustainable alternative to traditional thermoset resins. The production of thick and sizable components using reactive resins presents various intertwined physical aspects and difficulties, notably concerning potential overheating during the Elium® radical polymerization process. The optimization of this process necessitates the use of simulation to save the expensive time and effort caused by the experiments. However, to be reliable, these numerical methods must be validated to allow accurate predictions for potential defects with thick and complex parts. The challenge lies in flow front detection in the through-thickness direction. In this work, infusion tests were conducted for thick parts in a testing bench instrumented with a robust monitoring system. QRS sensors are placed through the part thickness to detect the front arrival instantaneously. The simulations are compared and validated to the signals of the QRS sensors for validation. Then the model was used to predict the flow behavior for more complex parts. A 3D flow is observed by the differences in permeability between the flow medium and the fabric, which induces a high difference in resin arrival times to the sensors depending on the position of sensors through the part thickness. The flow simulations showed a good approximation of the experimental results. However, deviations are observed in the flow front position, caused by the disturbance induced by the presence of the sensors.

**Introduction**

The ZEBRA (Zero wastE Blade ReseArch) project, led by French research center IRT Jules Verne, gathers ARKEMA, CANOE, SUEZ, LM Wind Power, Owens Corning, and ENGIE, aims to manufacture 100% recyclable wind turbine blades while being economically and environmentally feasible with a circular approach. Manufacturing wind blades typically involves the vacuum-assisted resin infusion (VARI) technique with thermoset resin. To overcome the recycling challenges of wind blade manufacturing [1], the project substitutes conventional thermoset resin with Elium® thermoplastic resin, developed by Arkema. LM Wind Power fabricated two 100% recyclable wind blades throughout the project's lifetime. The second recyclable wind blade with a carbon spar cap manufactured by LM Wind Power is shown in Figure 1. The vacuum-assisted resin infusion (VARI) process for wind blades can be intricate and variable, leading to defects in the final product [2]. Identifying risks and defects in the process is crucial for ensuring high-quality parts, traditionally done through time-consuming and costly experimental testing [3].

<figure><img src="image_544.png" /></figure>
# Simulation and monitoring of the infusion of thick composites with thermoplastic acrylic resin

SIDDIG Nihad¹,ᵃ*, LE BOT Philippe¹,ᵇ, FOUCHÉ Olivier¹,ᶜ and DENIS Yvan¹,ᵈ

¹ Nantes Université, IRT Jules Verne, 1 Mail des 20 000 Lieues, 44340 Bouguenais, France

ᵃnihad.siddig@irt-jules-verne.fr, ᵇphilippe.lebot@irt-jules-verne.fr,

ᶜolivier.fouche@irt-jules-verne.fr, ᵈyvan.denis@irt-jules-verne.fr

**Keywords:** Composites, Thermoplastic, Monitoring, Simulation, Polymerization

**Abstract.** The ZEBRA project aims to advance the circular economy by creating wind turbine blades that can be completely recycled. Currently, Wind turbine blades are fabricated through Vacuum-Assisted Resin Infusion (VARI) using thermoset resins. In this endeavor, the recyclable thermoplastic resin Elium® from Arkema is utilized as a sustainable alternative to traditional thermoset resins. The production of thick and sizable components using reactive resins presents various intertwined physical aspects and difficulties, notably concerning potential overheating during the Elium® radical polymerization process. The optimization of this process necessitates the use of simulation to save the expensive time and effort caused by the experiments. However, to be reliable, these numerical methods must be validated to allow accurate predictions for potential defects with thick and complex parts. The challenge lies in flow front detection in the through-thickness direction. In this work, infusion tests were conducted for thick parts in a testing bench instrumented with a robust monitoring system. QRS sensors are placed through the part thickness to detect the front arrival instantaneously. The simulations are compared and validated to the signals of the QRS sensors for validation. Then the model was used to predict the flow behavior for more complex parts. A 3D flow is observed by the differences in permeability between the flow medium and the fabric, which induces a high difference in resin arrival times to the sensors depending on the position of sensors through the part thickness. The flow simulations showed a good approximation of the experimental results. However, deviations are observed in the flow front position, caused by the disturbance induced by the presence of the sensors.

**Introduction**

The ZEBRA (Zero wastE Blade ReseArch) project, led by French research center IRT Jules Verne, gathers ARKEMA, CANOE, SUEZ, LM Wind Power, Owens Corning, and ENGIE, aims to manufacture 100% recyclable wind turbine blades while being economically and environmentally feasible with a circular approach. Manufacturing wind blades typically involves the vacuum-assisted resin infusion (VARI) technique with thermoset resin. To overcome the recycling challenges of wind blade manufacturing [1], the project substitutes conventional thermoset resin with Elium® thermoplastic resin, developed by Arkema. LM Wind Power fabricated two 100% recyclable wind blades throughout the project's lifetime. The second recyclable wind blade with a carbon spar cap manufactured by LM Wind Power is shown in Figure 1. The vacuum-assisted resin infusion (VARI) process for wind blades can be intricate and variable, leading to defects in the final product [2]. Identifying risks and defects in the process is crucial for ensuring high-quality parts, traditionally done through time-consuming and costly experimental testing [3].

<figure><img src="image_555.png" /></figure>
<figure><img src="image_545.png" /><figcaption>Figure 1: The second 100% recyclable ZEBRA 77m wind blade manufactured by LM Wind Power</figcaption></figure>

To overcome the process challenges, numerical simulation models the manufacturing process, identifying potential risks impacting quality and cost [4][5]. However, for these numerical methods to be trustworthy, validation is crucial to enable accurate predictions for potential defects, especially in the case of thick and complex parts [6][7][8]. The difficulty lies in detecting the flow front in the through-thickness direction. In this study, infusion tests were carried out on thick parts using a testing bench equipped with a robust monitoring system [9]. Sensors are strategically placed throughout the part thickness to instantly detect the front arrival. The simulations were then compared and validated against the signals from the sensors for accuracy. Subsequently, the model was employed to predict the flow behavior for more complex parts. The paper investigates simulating thick continuous fiber/reactive thermoplastic resin composites for wind energy applications. The study aims to establish a solid foundation for process optimization, linking experimental characterization and numerical simulations, explore the use of QRS sensors in the flow front detection through the part thickness, compare two simulation strategies and cross-check these results with sensor acquisitions.

## Experimental Characterization and Monitoring

This section presents the materials employed in the infusion process, the experimental approach and the monitoring system.

### Materials

The thermoplastic resin Elium® developed by Arkema was used for its recyclability. Elium® is categorized as an acrylic reactive resin capable of producing large-scale fiber-reinforced plastics [10]. The resin undergoes free radical polymerization, where the catalyst, Butanox 50 peroxide at a 2% concentration, initiates the transformation of the monomer methyl methacrylate into poly methyl methacrylate. A 50/50 mixture of Elium® 191 SA and Elium® 191 XO was utilized for infusion experiments. This resin has a density of 1.01 g/cm3 and a viscosity of approximately 0.1 Pa.s at 23°C. To ensure precision, the dynamic viscosity of the resin is assessed using a flow cup, adhering to ISO 2431 standards. Composite panels were fabricated using a stack of Owens Corning high-performance unidirectional glass fabric, resulting in a panel measuring 600 mm in length, 300 mm in width, approximately 50 mm in thickness and 78 plies of fabric. To aid resin flow, the flow medium Infuplex OM 70, measuring 400 mm in width, 250 mm in length, and 1 mm in thickness, was employed. Permeability tests were previously conducted on both the reinforcement and distribution medium.

### Experimental Bench

The experimental bench is used for conducting infusion tests on the presented components. The design and setup of this bench are critical factors influencing the reliability and accuracy of experiments. The features of the experimental bench are shown in Figure 2, shedding light on its role in facilitating controlled and representative conditions for the infusion process. In that
<figure><img src="image_556.png" /><figcaption>Figure 1: The second 100% recyclable ZEBRA 77m wind blade manufactured by LM Wind Power</figcaption></figure>

To overcome the process challenges, numerical simulation models the manufacturing process, identifying potential risks impacting quality and cost [4][5]. However, for these numerical methods to be trustworthy, validation is crucial to enable accurate predictions for potential defects, especially in the case of thick and complex parts [6][7][8]. The difficulty lies in detecting the flow front in the through-thickness direction. In this study, infusion tests were carried out on thick parts using a testing bench equipped with a robust monitoring system [9]. Sensors are strategically placed throughout the part thickness to instantly detect the front arrival. The simulations were then compared and validated against the signals from the sensors for accuracy. Subsequently, the model was employed to predict the flow behavior for more complex parts. The paper investigates simulating thick continuous fiber/reactive thermoplastic resin composites for wind energy applications. The study aims to establish a solid foundation for process optimization, linking experimental characterization and numerical simulations, explore the use of QRS sensors in the flow front detection through the part thickness, compare two simulation strategies and cross-check these results with sensor acquisitions.

## Experimental Characterization and Monitoring

This section presents the materials employed in the infusion process, the experimental approach and the monitoring system.

### Materials

The thermoplastic resin Elium® developed by Arkema was used for its recyclability. Elium® is categorized as an acrylic reactive resin capable of producing large-scale fiber-reinforced plastics [10]. The resin undergoes free radical polymerization, where the catalyst, Butanox 50 peroxide at a 2% concentration, initiates the transformation of the monomer methyl methacrylate into poly methyl methacrylate. A 50/50 mixture of Elium® 191 SA and Elium® 191 XO was utilized for infusion experiments. This resin has a density of 1.01 g/cm3 and a viscosity of approximately 0.1 Pa.s at 23°C. To ensure precision, the dynamic viscosity of the resin is assessed using a flow cup, adhering to ISO 2431 standards. Composite panels were fabricated using a stack of Owens Corning high-performance unidirectional glass fabric, resulting in a panel measuring 600 mm in length, 300 mm in width, approximately 50 mm in thickness and 78 plies of fabric. To aid resin flow, the flow medium Infuplex OM 70, measuring 400 mm in width, 250 mm in length, and 1 mm in thickness, was employed. Permeability tests were previously conducted on both the reinforcement and distribution medium.

### Experimental Bench

The experimental bench is used for conducting infusion tests on the presented components. The design and setup of this bench are critical factors influencing the reliability and accuracy of experiments. The features of the experimental bench are shown in Figure 2, shedding light on its role in facilitating controlled and representative conditions for the infusion process. In that
experimental bench, the tool is made of transparent glass, allowing resin front position acquisition at the lower tool side.

<figure><img src="image_546.png" /><figcaption>Figure 2: The infusion test bench</figcaption></figure>

## QRS sensors

The infusion process is closely monitored using Quantic Resistive sensors (QRS®) from SeNSE-IN, strategically positioned throughout the thickness of the material (Figure 3). QRS® sensors are nano-structured strain, transducing strain at the nanoscale into a macroscopic resistive signal for consumption of only some mW. That is why their first applications were initially dedicated to SHM objectives [11]. SeNSE-IN designs them now for process monitoring applications. They can be positioned on the surface or in the core of the composite material between plies (Figure 4), and they can be made of the same resin as the composite if necessary. The connection from active part sensors (with a surface area of about 1 cm²) to the data acquisition system is guaranteed by 0.2 mm diameter copper wires.

These sensors play a crucial role in capturing and instantaneously detecting the arrival of the flow front during infusion tests. By being embedded throughout the part thickness, the QRS® sensors provide a comprehensive understanding of the infusion dynamics, offering real-time data on the progression of the resin through the material. This detailed monitoring not only enhances our ability to validate numerical simulations but also serves as a key element in ensuring the accuracy and reliability of the experimental outcomes.

Additional sensors enable a better understanding of the infusion process: a Keyence flow meter FD-XC2063 and a vacuum sensor (Balluff BSP00ZK).

<figure><img src="image_547.png" /><figcaption>Figure 3: Sensor placement in the composite part, fQRS refers QRS sensors dedicated to flow measurement</figcaption></figure>
experimental bench, the tool is made of transparent glass, allowing resin front position acquisition at the lower tool side.

<figure><img src="image_557.png" /><figcaption>Figure 2: The infusion test bench</figcaption></figure>

## QRS sensors

The infusion process is closely monitored using Quantic Resistive sensors (QRS®) from SeNSE-IN, strategically positioned throughout the thickness of the material (Figure 3). QRS® sensors are nano-structured strain, transducing strain at the nanoscale into a macroscopic resistive signal for consumption of only some mW. That is why their first applications were initially dedicated to SHM objectives [11]. SeNSE-IN designs them now for process monitoring applications. They can be positioned on the surface or in the core of the composite material between plies (Figure 4), and they can be made of the same resin as the composite if necessary. The connection from active part sensors (with a surface area of about 1 cm²) to the data acquisition system is guaranteed by 0.2 mm diameter copper wires.

These sensors play a crucial role in capturing and instantaneously detecting the arrival of the flow front during infusion tests. By being embedded throughout the part thickness, the QRS® sensors provide a comprehensive understanding of the infusion dynamics, offering real-time data on the progression of the resin through the material. This detailed monitoring not only enhances our ability to validate numerical simulations but also serves as a key element in ensuring the accuracy and reliability of the experimental outcomes.

Additional sensors enable a better understanding of the infusion process: a Keyence flow meter FD-XC2063 and a vacuum sensor (Balluff BSP00ZK).

<figure><img src="image_558.png" /><figcaption>Figure 3: Sensor placement in the composite part, fQRS refers QRS sensors dedicated to flow measurement</figcaption></figure>
<figure><img src="image_548.png" /><figcaption>Figure 4: Focus on Sensor integration in the composite part</figcaption></figure>

## Monitoring System for the Infusion Process

The infusion process monitoring is performed by two commercial systems. An HBM QuantumX 1601B device allows monitoring of the flow meter and vacuum sensor as well as some thermocouples in order to ensure the quality of the infusion process. A specific data acquisition system is dedicated to QRS© sensors. This QR-KUB® device, developed by SeNSE-IN, allows to visualize and store of up to 8 QRS sensor signals with 2 additive thermocouples (Figure 5).

<figure><img src="image_549.png" /><figcaption>Figure 5: Data acquisition system</figcaption></figure>

## Modeling and Simulation

The infusion process combines several coupled physical phenomena: fluid mechanics represented in resin flow, thermal (transient heat transfer), chemical (resin polymerization kinetics), in addition to solid mechanics to model the potential deformation during and after impregnation (distortion) of the medium because of using flexible tooling. In this paper, the focus is drawn to resin flow, heat transfer and resin polymerization.

## Simulation tool

The commercially available simulation software PAM-RTM® developed by ESI Group is used to simulate the infusion process. It is a Finite Element (FE) solution for modeling the manufacturing processes of structural and non-structural composites reinforced by continuous fibers. The software allows the coupling of the physical phenomena involved in the infusion process [9].

## Boundary and initial conditions

Pressure Dirichlet boundary conditions are prescribed at the injection lines with the atmospheric pressure (Figure 7). The vacuum pressure of 0.2 bar is applied at the outlet along the opposite side. The initial temperature was assigned to the part and the mold. All the other surfaces subjected to natural convection boundary conditions.
<figure><img src="image_559.png" /><figcaption>Figure 4: Focus on Sensor integration in the composite part</figcaption></figure>

## Monitoring System for the Infusion Process

The infusion process monitoring is performed by two commercial systems. An HBM QuantumX 1601B device allows monitoring of the flow meter and vacuum sensor as well as some thermocouples in order to ensure the quality of the infusion process. A specific data acquisition system is dedicated to QRS© sensors. This QR-KUB® device, developed by SeNSE-IN, allows to visualize and store of up to 8 QRS sensor signals with 2 additive thermocouples (Figure 5).

<figure><img src="image_560.png" /><figcaption>Figure 5: Data acquisition system</figcaption></figure>

## Modeling and Simulation

The infusion process combines several coupled physical phenomena: fluid mechanics represented in resin flow, thermal (transient heat transfer), chemical (resin polymerization kinetics), in addition to solid mechanics to model the potential deformation during and after impregnation (distortion) of the medium because of using flexible tooling. In this paper, the focus is drawn to resin flow, heat transfer and resin polymerization.

## Simulation tool

The commercially available simulation software PAM-RTM® developed by ESI Group is used to simulate the infusion process. It is a Finite Element (FE) solution for modeling the manufacturing processes of structural and non-structural composites reinforced by continuous fibers. The software allows the coupling of the physical phenomena involved in the infusion process [9].

## Boundary and initial conditions

Pressure Dirichlet boundary conditions are prescribed at the injection lines with the atmospheric pressure (Figure 7). The vacuum pressure of 0.2 bar is applied at the outlet along the opposite side. The initial temperature was assigned to the part and the mold. All the other surfaces subjected to natural convection boundary conditions.
## Process physics

The flow of the incompressible fluid in the fibrous medium is governed, if the change of thickness is neglected, by the simplified form of the mass balance equation (Eq. 1) [12]:

$$ \nabla \cdot (\vec{U}) = 0 \qquad (1) $$

Where $\vec{U}$ is the liquid velocity. Darcy's law describes the flow of Newtonian fluids through porous media (Eq. 2); it relates the liquid velocity to the permeability tensor $K$, the resin viscosity $\mu$ and the pressure gradient $\nabla P$ [13].

$$ \vec{U} = \frac{-K}{\mu} \nabla P \qquad (2) $$

The energy balance equation (Eq. 3) governs the heat transfer problem. The terms of the energy balance equation on the left-hand side are, respectively from left to right: the transient and advection terms. On the right-hand side of the equation, the first part of the equation is the conduction term. The internal heat generation in the system is represented by $\dot{R}$ [14].

$$ \rho c_p \frac{\partial T}{\partial t} + \rho_r c_{p,r} \vec{U} \cdot \nabla T = -\nabla \cdot (\lambda \cdot \nabla T) + \dot{R} \qquad (3) $$

where T is the temperature, $\rho$ and $\rho_r$ are respectively the composite and the resin density. $c_p$ and $c_{p,r}$ are the composite and the resin specific heat capacity, respectively. The subscript r denotes the resin. $\lambda$ is the thermal conductivity of the composite material. The term $\dot{R}$ in the energy equation is the volumetric heat generation related to resin polymerization.

## Results and Discussion

In this section, the key findings are presented and discussed, shedding light on the intricacies of resin infusion within thick components.

### Sensor response analysis

The responses obtained from the QRS sensors provide crucial information about the dynamics of resin flow. The sensor data allows to identify patterns, anomalies, and correlations that contribute to a comprehensive understanding of the infusion dynamics through the part thickness. The comparison is only based on the resin arrival to the sensors, the curve is not considered.

<figure><img src="image_550.png" /><figcaption>Figure 6: The sensor response in the experimental setting</figcaption></figure>
## Process physics

The flow of the incompressible fluid in the fibrous medium is governed, if the change of thickness is neglected, by the simplified form of the mass balance equation (Eq. 1) [12]:

$$ \nabla \cdot (\vec{U}) = 0 \qquad (1) $$

Where $\vec{U}$ is the liquid velocity. Darcy's law describes the flow of Newtonian fluids through porous media (Eq. 2); it relates the liquid velocity to the permeability tensor $K$, the resin viscosity $\mu$ and the pressure gradient $\nabla P$ [13].

$$ \vec{U} = \frac{-K}{\mu} \nabla P \qquad (2) $$

The energy balance equation (Eq. 3) governs the heat transfer problem. The terms of the energy balance equation on the left-hand side are, respectively from left to right: the transient and advection terms. On the right-hand side of the equation, the first part of the equation is the conduction term. The internal heat generation in the system is represented by $\dot{R}$ [14].

$$ \rho c_p \frac{\partial T}{\partial t} + \rho_r c_{p,r} \vec{U} \cdot \nabla T = -\nabla \cdot (\lambda \cdot \nabla T) + \dot{R} \qquad (3) $$

where T is the temperature, $\rho$ and $\rho_r$ are respectively the composite and the resin density. $c_p$ and $c_{p,r}$ are the composite and the resin specific heat capacity, respectively. The subscript r denotes the resin. $\lambda$ is the thermal conductivity of the composite material. The term $\dot{R}$ in the energy equation is the volumetric heat generation related to resin polymerization.

## Results and Discussion

In this section, the key findings are presented and discussed, shedding light on the intricacies of resin infusion within thick components.

### Sensor response analysis

The responses obtained from the QRS sensors provide crucial information about the dynamics of resin flow. The sensor data allows to identify patterns, anomalies, and correlations that contribute to a comprehensive understanding of the infusion dynamics through the part thickness. The comparison is only based on the resin arrival to the sensors, the curve is not considered.

<figure><img src="image_561.png" /><figcaption>Figure 6: The sensor response in the experimental setting</figcaption></figure>
Figure 6 plots the normalized sensor response in the three QRS sensors placed at different levels (Fig. 3). The sensor response is in Ohm, and it is normalized to the maximum value of each sensor. The three virtual sensors were placed at the same experiment positions: the first sensor above the ply in contact with the mold (ply 1), the second sensor at the central ply (38) and the last sensor under the upper ply (76). The first sensor detects resin right after the beginning of impregnation. This sensor is the nearest to the injection line and the flow medium which facilitates resin flow. The second sensor detects resin 7 minutes after the resin's arrival to sensor 1. The last sensor is the last sensor to detect the resin arrival, as the flow velocity decreases with time as it advances further than the distribution medium. This result shows that the variations in permeability between the flow medium and the fabric give rise to a substantial 3D flow.

## Modeling and simulation of thick parts

The representation of the model is implemented using the PAM-RTM commercial tool to simulate the impregnation process. Similarly, to the infusion experiment, the part has a thickness of 50 mm with a sequential infusion through two infusion lines, two distribution media and one vacuum line. The two models are represented in Figure 7, the models are identical except for the meshing method through the part thickness. The first model, to the left, contains one element per ply. The model to the right (model 2), contains 10 elements as a total through the part thickness. The two models have the same number of in-plane elements. Representing one element per ply increases the model accuracy and its capacity to predict the flow front position with good precision. Reducing the number of elements to only 10 through the part thickness decreases the calculation time, but also deteriorates the precision. This study will help estimate the model simulation preferences to compromise and save cost and time along with having a good estimation of process physics. The sensor placement remains the same for the two models.

<figure><img src="image_551.png" /><figcaption>Figure 7: Numerical model implementation for thick parts</figcaption></figure>

Figure 8 shows the flow front contours of the two models. The pattern seems to be similar for the models at the global dynamics shown by the upper contours, however, a slight difference in filling time is observed between the two models. The fewer the number of elements, the higher the filling time; this is due to the precision related to the difference in element numbers in the two models. The software homogenizes the permeability through each element with the second scenario, leading to an overestimation in the permeability value. Comparing two through-thickness sections at the same time instant (1011s), we observe faster impregnation and higher precision with the refined mesh of Model 1. These sections illustrate how thick parts substantiate the
Figure 6 plots the normalized sensor response in the three QRS sensors placed at different levels (Fig. 3). The sensor response is in Ohm, and it is normalized to the maximum value of each sensor. The three virtual sensors were placed at the same experiment positions: the first sensor above the ply in contact with the mold (ply 1), the second sensor at the central ply (38) and the last sensor under the upper ply (76). The first sensor detects resin right after the beginning of impregnation. This sensor is the nearest to the injection line and the flow medium which facilitates resin flow. The second sensor detects resin 7 minutes after the resin's arrival to sensor 1. The last sensor is the last sensor to detect the resin arrival, as the flow velocity decreases with time as it advances further than the distribution medium. This result shows that the variations in permeability between the flow medium and the fabric give rise to a substantial 3D flow.

## Modeling and simulation of thick parts

The representation of the model is implemented using the PAM-RTM commercial tool to simulate the impregnation process. Similarly, to the infusion experiment, the part has a thickness of 50 mm with a sequential infusion through two infusion lines, two distribution media and one vacuum line. The two models are represented in Figure 7, the models are identical except for the meshing method through the part thickness. The first model, to the left, contains one element per ply. The model to the right (model 2), contains 10 elements as a total through the part thickness. The two models have the same number of in-plane elements. Representing one element per ply increases the model accuracy and its capacity to predict the flow front position with good precision. Reducing the number of elements to only 10 through the part thickness decreases the calculation time, but also deteriorates the precision. This study will help estimate the model simulation preferences to compromise and save cost and time along with having a good estimation of process physics. The sensor placement remains the same for the two models.

<figure><img src="image_562.png" /><figcaption>Figure 7: Numerical model implementation for thick parts</figcaption></figure>

Figure 8 shows the flow front contours of the two models. The pattern seems to be similar for the models at the global dynamics shown by the upper contours, however, a slight difference in filling time is observed between the two models. The fewer the number of elements, the higher the filling time; this is due to the precision related to the difference in element numbers in the two models. The software homogenizes the permeability through each element with the second scenario, leading to an overestimation in the permeability value. Comparing two through-thickness sections at the same time instant (1011s), we observe faster impregnation and higher precision with the refined mesh of Model 1. These sections illustrate how thick parts substantiate the
influence of 3D flow especially in the presence of the distribution media, which induces differences in flow front velocities.

<figure><img src="image_552.png" /><figcaption>Figure 8: Filling comparison between model 1 and 2 in the in-plane and through-thickness directions</figcaption></figure>

The three virtual sensors were placed at the same experiment positions. These sensors allow to follow the time evolution of pressure. The pressure is used as an indication of resin arrival, as a pressure rise is translated by resin arrival at the sensor.

<figure><img src="image_553.png" /><figcaption>Figure 9: Pressure evolution with time at the virtual sensors</figcaption></figure>

Figure 9 shows the pressure evolution with time at the three sensor locations for the two models. The continuous and dotted lines represent model 1 and model 2, respectively. A similar trend is observed between the two models, however, variations in resin arrival times are observed in the two models. Model 2 shows a lag of 2-4 minutes in resin detection time compared to model 1. Smooth curves are observed in Model 1, where higher precision is expected.

## Comparison with experimental results

In the comparative analysis of numerical and experimental results, the sensor responses are a pivotal point of convergence. The data obtained from the QRS sensors during the infusion process is systematically compared with the predictions derived from numerical simulations regarding resin arrival time to sensors. This comparison allows us to assess the accuracy and reliability of the simulation models in replicating the dynamic behavior observed in the experimental setting.
influence of 3D flow especially in the presence of the distribution media, which induces differences in flow front velocities.

<figure><img src="image_563.png" /><figcaption>Figure 8: Filling comparison between model 1 and 2 in the in-plane and through-thickness directions</figcaption></figure>

The three virtual sensors were placed at the same experiment positions. These sensors allow to follow the time evolution of pressure. The pressure is used as an indication of resin arrival, as a pressure rise is translated by resin arrival at the sensor.

<figure><img src="image_564.png" /><figcaption>Figure 9: Pressure evolution with time at the virtual sensors</figcaption></figure>

Figure 9 shows the pressure evolution with time at the three sensor locations for the two models. The continuous and dotted lines represent model 1 and model 2, respectively. A similar trend is observed between the two models, however, variations in resin arrival times are observed in the two models. Model 2 shows a lag of 2-4 minutes in resin detection time compared to model 1. Smooth curves are observed in Model 1, where higher precision is expected.

## Comparison with experimental results

In the comparative analysis of numerical and experimental results, the sensor responses are a pivotal point of convergence. The data obtained from the QRS sensors during the infusion process is systematically compared with the predictions derived from numerical simulations regarding resin arrival time to sensors. This comparison allows us to assess the accuracy and reliability of the simulation models in replicating the dynamic behavior observed in the experimental setting.
<figure><img src="image_554.png" /><figcaption>Figure 10: Comparison of sensor acquisitions with simulation data</figcaption></figure>

The comparison between the experimental sensor acquisitions and the numerical results is shown in Figure 10. The virtual sensor curves show the time evolution of the filling percentage (factor), varying from 0 to 1. In this figure, the continuous lines represent the normalized sensor signal. The virtual sensors are represented by the dotted lines (Model 1) and the dashed lines (Model 2). Generally speaking, the sensors detect the resin arrival earlier than the virtual sensors. This is because the QRS sensors have a wire that intrusively influences the resin flow. These wires create race-tracking channels, which transmit resin faster than the other parts of the fabric, leading to its arrival at a shorter time than the surrounding fabric. This influence is not observed by simulation, because the simulation does not consider the race-tracking induced by the sensor wire.

Comparing the two simulation strategies, Model 1 seems more accurate than Model 2 as the filling factor value of 1 at the complete sensor filling is more relevant to the experiment. Moreover, the virtual sensor response in Model 1 is smoother and disturbance-free. Model 2 seems to fluctuate under the influence of the insufficient number of through-thickness elements. At a macroscopic scale, the filling times are close, but the local dynamic is still very sensitive to the mesh size, for the same reasons explained before. Comparing the responses of the QRS, the virtual pressure and the filling factor sensors, it is observed that the QRS and the virtual filling factor sensors detect the flow front as soon as the resin reaches the sensor, and the signal evolves till the complete sensor filling. The pressure virtual sensors only detect the resin arrival when the sensor is 100% filled, resulting in a pressure rise. Care should be taken about the sensor choice for the monitoring of the infusion process. Vision camera is widely used to follow the in-plane flow front advancement. It is still challenging to control the infusion process in zones not covered by cameras, especially through the part thickness. When QRS sensors are used, the sensor acquisitions should be treated with special consideration. The resin detection time indicated by the sensor does not necessarily indicate the flow arrival to this specific zone of the part, because of the flow disturbance created by the sensor.

## Conclusions

In this study, infusion tests were carried out on thick components using a testing bench equipped with a robust monitoring system. QRS sensors were strategically positioned across the thickness of the part to promptly identify the arrival of the front in the out-of-plane direction. The simulations were cross-referenced against the signals recorded by the QRS sensors for validation purposes. Two simulation models were presented, the difference lies in the number of elements through the part thickness. The finer mesh gave a more precise prediction of the experiment. The three QRS
<figure><img src="image_565.png" /><figcaption>Figure 10: Comparison of sensor acquisitions with simulation data</figcaption></figure>

The comparison between the experimental sensor acquisitions and the numerical results is shown in Figure 10. The virtual sensor curves show the time evolution of the filling percentage (factor), varying from 0 to 1. In this figure, the continuous lines represent the normalized sensor signal. The virtual sensors are represented by the dotted lines (Model 1) and the dashed lines (Model 2). Generally speaking, the sensors detect the resin arrival earlier than the virtual sensors. This is because the QRS sensors have a wire that intrusively influences the resin flow. These wires create race-tracking channels, which transmit resin faster than the other parts of the fabric, leading to its arrival at a shorter time than the surrounding fabric. This influence is not observed by simulation, because the simulation does not consider the race-tracking induced by the sensor wire.

Comparing the two simulation strategies, Model 1 seems more accurate than Model 2 as the filling factor value of 1 at the complete sensor filling is more relevant to the experiment. Moreover, the virtual sensor response in Model 1 is smoother and disturbance-free. Model 2 seems to fluctuate under the influence of the insufficient number of through-thickness elements. At a macroscopic scale, the filling times are close, but the local dynamic is still very sensitive to the mesh size, for the same reasons explained before. Comparing the responses of the QRS, the virtual pressure and the filling factor sensors, it is observed that the QRS and the virtual filling factor sensors detect the flow front as soon as the resin reaches the sensor, and the signal evolves till the complete sensor filling. The pressure virtual sensors only detect the resin arrival when the sensor is 100% filled, resulting in a pressure rise. Care should be taken about the sensor choice for the monitoring of the infusion process. Vision camera is widely used to follow the in-plane flow front advancement. It is still challenging to control the infusion process in zones not covered by cameras, especially through the part thickness. When QRS sensors are used, the sensor acquisitions should be treated with special consideration. The resin detection time indicated by the sensor does not necessarily indicate the flow arrival to this specific zone of the part, because of the flow disturbance created by the sensor.

## Conclusions

In this study, infusion tests were carried out on thick components using a testing bench equipped with a robust monitoring system. QRS sensors were strategically positioned across the thickness of the part to promptly identify the arrival of the front in the out-of-plane direction. The simulations were cross-referenced against the signals recorded by the QRS sensors for validation purposes. Two simulation models were presented, the difference lies in the number of elements through the part thickness. The finer mesh gave a more precise prediction of the experiment. The three QRS
sensors record significant differences in the arrival times of resin, depending on their location within the thickness of the part. These results shed light on the 3D flow variations induced by the permeability ratio between the flow medium and the fabric. The results of the flow simulations showed considerable discrepancies compared to the experimental findings, of about 2.5 min at sensor 2 and higher discrepancies at sensor 3, due to the flow disturbance created by the QRS sensors which generates race-tracking channels. These results provide valuable insights into the limitations of the sensors and highlight areas for potential improvement. This comprehensive evaluation of the numerical-experimental correlation not only contributes to the validation of the used approach but also guides the refinement of future models for a more accurate representation of the complex resin infusion dynamics within thick components. These findings can help address potential optimizations, challenges, and avenues for future research [15].

## Acknowledgments

This study is a part of the ZEBRA project led by IRT Jules Verne (French Institute of Research and Technology in Advanced Manufacturing). The authors wish to thank the industrial partners of this project: LM Wind Power, Arkema, Owens Corning, ENGIE, Suez and CANOE and Ecole Centrale de Nantes, GeM laboratory.

## References

[1] J. Beauson, A. Laurent, D. P. Rudolph, and J. Pagh Jensen, "The complex end-of-life of wind turbine blades: A review of the European context," *Renewable and Sustainable Energy Reviews*, vol. 155. Elsevier Ltd, Mar. 01, 2022. https://doi.org/10.1016/j.rser.2021.111847

[2] S. van Oosterom, T. Allen, M. Battley, and S. Bickerton, "An objective comparison of common vacuum assisted resin infusion processes," *Compos Part A Appl Sci Manuf*, vol. 125, 2019. https://doi.org/10.1016/j.compositesa.2019.105528

[3] N. Siddig et al., "Infusion and Polymerization of Thick Glass/Elium® Acrylic Thermoplastic Resin Composites," in *ICCM23, Belfast, Ireland*, 2023. Accessed: Jan. 11, 2023. [Online]. Available: https://www.researchgate.net/publication/373049008_Infusion_and_Polymerization_of_Thick_GlassEliumR_Acrylic_Thermoplastic_Resin_Composites

[4] B. Liu, S. Bickerton, and S. G. Advani, "Modelling and simulation of resin transfer moulding (RTM)-gate control, venting and dry spot prediction," *Compos Part A Appl Sci Manuf*, vol. 27, no. 2, pp. 135-141, 1996.

[5] Carlone, P., Rubino, F., Paradiso, V., & Tucci, F. (2018). "Multi-scale modeling and online monitoring of resin flow through dual-scale textiles in liquid composite molding processes". The International Journal of Advanced Manufacturing Technology, 96, 2215-2230.

[6] P. Le Bot et al., "Anomaly detection during thermoplastic composite infusion: Monitoring strategy through thermal sensors," in *Key Engineering Materials, Achievements and Trends in Material Forming*, vol. 926, 2022, pp. 1423-1436. https://doi.org/10.4028/p-n27w97

[7] Y. Denis, N. Siddig, R. Guitton, P. Le Bot, A. De Fongalland, and D. Lecointe, "Thermo-chemical modeling and simulation of glass/Elium® acrylic thermoplastic resin composites," in *ESAFORM, 19-21 April 2023, Kraków, Poland*. https://doi.org/10.21741/9781644902479-34

[8] Aleksendrić, D., Bellini, C., Carlone, P., Ćirović, V., Rubino, F., & Sorrentino, L. (2019). "Neural-fuzzy optimization of thick composites curing process". Materials and Manufacturing Processes, 34(3), 262-273.
sensors record significant differences in the arrival times of resin, depending on their location within the thickness of the part. These results shed light on the 3D flow variations induced by the permeability ratio between the flow medium and the fabric. The results of the flow simulations showed considerable discrepancies compared to the experimental findings, of about 2.5 min at sensor 2 and higher discrepancies at sensor 3, due to the flow disturbance created by the QRS sensors which generates race-tracking channels. These results provide valuable insights into the limitations of the sensors and highlight areas for potential improvement. This comprehensive evaluation of the numerical-experimental correlation not only contributes to the validation of the used approach but also guides the refinement of future models for a more accurate representation of the complex resin infusion dynamics within thick components. These findings can help address potential optimizations, challenges, and avenues for future research [15].

## Acknowledgments

This study is a part of the ZEBRA project led by IRT Jules Verne (French Institute of Research and Technology in Advanced Manufacturing). The authors wish to thank the industrial partners of this project: LM Wind Power, Arkema, Owens Corning, ENGIE, Suez and CANOE and Ecole Centrale de Nantes, GeM laboratory.

## References

[1] J. Beauson, A. Laurent, D. P. Rudolph, and J. Pagh Jensen, "The complex end-of-life of wind turbine blades: A review of the European context," *Renewable and Sustainable Energy Reviews*, vol. 155. Elsevier Ltd, Mar. 01, 2022. https://doi.org/10.1016/j.rser.2021.111847

[2] S. van Oosterom, T. Allen, M. Battley, and S. Bickerton, "An objective comparison of common vacuum assisted resin infusion processes," *Compos Part A Appl Sci Manuf*, vol. 125, 2019. https://doi.org/10.1016/j.compositesa.2019.105528

[3] N. Siddig et al., "Infusion and Polymerization of Thick Glass/Elium® Acrylic Thermoplastic Resin Composites," in *ICCM23, Belfast, Ireland*, 2023. Accessed: Jan. 11, 2023. [Online]. Available: https://www.researchgate.net/publication/373049008_Infusion_and_Polymerization_of_Thick_GlassEliumR_Acrylic_Thermoplastic_Resin_Composites

[4] B. Liu, S. Bickerton, and S. G. Advani, "Modelling and simulation of resin transfer moulding (RTM)-gate control, venting and dry spot prediction," *Compos Part A Appl Sci Manuf*, vol. 27, no. 2, pp. 135-141, 1996.

[5] Carlone, P., Rubino, F., Paradiso, V., & Tucci, F. (2018). "Multi-scale modeling and online monitoring of resin flow through dual-scale textiles in liquid composite molding processes". The International Journal of Advanced Manufacturing Technology, 96, 2215-2230.

[6] P. Le Bot et al., "Anomaly detection during thermoplastic composite infusion: Monitoring strategy through thermal sensors," in *Key Engineering Materials, Achievements and Trends in Material Forming*, vol. 926, 2022, pp. 1423-1436. https://doi.org/10.4028/p-n27w97

[7] Y. Denis, N. Siddig, R. Guitton, P. Le Bot, A. De Fongalland, and D. Lecointe, "Thermo-chemical modeling and simulation of glass/Elium® acrylic thermoplastic resin composites," in *ESAFORM, 19-21 April 2023, Kraków, Poland*. https://doi.org/10.21741/9781644902479-34

[8] Aleksendrić, D., Bellini, C., Carlone, P., Ćirović, V., Rubino, F., & Sorrentino, L. (2019). "Neural-fuzzy optimization of thick composites curing process". Materials and Manufacturing Processes, 34(3), 262-273.
[9] N. Siddig et al., "Modeling and simulation of the fabrication of glass/elium® acrylic thermoplastic resin composites by the infusion process," SAMPE Europe, Hamburg, Germany, 2022.

[10] S. F. Gayot, C. Bailly, T. Pardoen, P. Gérard, and F. Van Loock, "Processing maps based on polymerization modelling of thick methacrylic laminates," Mater Des, vol. 196, p. 109170, 2020. https://doi.org/10.1016/j.matdes.2020.109170

[11] A. Lemartinel, M. Castro, O. Fouché, J. C. De Luca, and J. F. Feller, "Strain mapping and damage tracking in carbon fiber reinforced epoxy composites during dynamic bending until fracture with quantum resistive sensors in array," Journal of Composites Science, vol. 5, no. 2, 2021. https://doi.org/10.3390/jcs5020060

[12] C. H. Park, "Numerical simulation of flow processes in composites manufacturing," in Advances in Composites Manufacturing and Process Design, Woodhead Publishing, 2015, pp. 317-378. https://doi.org/10.1016/B978-1-78242-307-2.00015-4

[13] M. Deléglise, C. Binétruy, and P. Krawczak, "Solution to filling time prediction issues for constant pressure driven injection in RTM," Compos Part A Appl Sci Manuf, vol. 36, no. 3, pp. 339-344, 2005. https://doi.org/10.1016/j.compositesa.2004.07.001

[14] L. Shi, "Heat Transfer in the Thick Thermoset Composites, PhD Thesis, Delft University of Technology," Delft University of Technology, 2016.

[15] Boisse, P., Akkerman, R., Carlone, P., Kärger, L., Lomov, S. V., & Sherwood, J. A. (2022). "Advances in composite forming through 25 years of ESAFORM". International Journal of Material Forming, 15(3), 39.
[9] N. Siddig et al., "Modeling and simulation of the fabrication of glass/elium® acrylic thermoplastic resin composites by the infusion process," SAMPE Europe, Hamburg, Germany, 2022.

[10] S. F. Gayot, C. Bailly, T. Pardoen, P. Gérard, and F. Van Loock, "Processing maps based on polymerization modelling of thick methacrylic laminates," Mater Des, vol. 196, p. 109170, 2020. https://doi.org/10.1016/j.matdes.2020.109170

[11] A. Lemartinel, M. Castro, O. Fouché, J. C. De Luca, and J. F. Feller, "Strain mapping and damage tracking in carbon fiber reinforced epoxy composites during dynamic bending until fracture with quantum resistive sensors in array," Journal of Composites Science, vol. 5, no. 2, 2021. https://doi.org/10.3390/jcs5020060

[12] C. H. Park, "Numerical simulation of flow processes in composites manufacturing," in Advances in Composites Manufacturing and Process Design, Woodhead Publishing, 2015, pp. 317-378. https://doi.org/10.1016/B978-1-78242-307-2.00015-4

[13] M. Deléglise, C. Binétruy, and P. Krawczak, "Solution to filling time prediction issues for constant pressure driven injection in RTM," Compos Part A Appl Sci Manuf, vol. 36, no. 3, pp. 339-344, 2005. https://doi.org/10.1016/j.compositesa.2004.07.001

[14] L. Shi, "Heat Transfer in the Thick Thermoset Composites, PhD Thesis, Delft University of Technology," Delft University of Technology, 2016.

[15] Boisse, P., Akkerman, R., Carlone, P., Kärger, L., Lomov, S. V., & Sherwood, J. A. (2022). "Advances in composite forming through 25 years of ESAFORM". International Journal of Material Forming, 15(3), 39.
# Transverse squeeze flow of fibre reinforced thermoplastic composites

BIELEMAN Gerben¹,²,a,*, ROTINK Gijs¹, GROUVE Wouter J.B.¹,², KLOMPEN Edwin T.J.² and AKKERMAN Remko¹,²

¹Faculty of Engineering Technology, Chair of Production Technology, University of Twente, Enschede, the Netherlands

²ThermoPlastic composites Research Center (TPRC), Enschede, the Netherlands

a.g.bieleman@utwente.nl

**Keywords:** Squeeze Flow, Transverse Viscosity, Thermoplastic Composites

**Abstract.** Transverse squeeze flow is one of the deformation mechanisms that govern the forming of molten fiber reinforced thermoplastic composites. It is typically described by a transverse bulk viscosity, dictating the resistance against the flow of the combined constituents. In this work, the squeeze flow method was used to characterize the transverse viscosity of carbon fiber reinforced low-melting PAEK at three different compression rates. The experiments were recorded with a camera and the video images were analyzed to obtain the flow fields. A power law fluid model was fitted to the logged data, but found to be unable to describe the material behavior at all compression rates. Moreover, the video analyses indicated discrepancies between the observed specimen deformations and those predicted by the model. Future studies need to focus on the description of the squeeze flow behavior of UD C/LM-PAEK by different models (viscous, viscoelastic), using video-captured deformations for numerical fitting of the models.

**Introduction**

In the last decade, thermoplastic composites (TPCs) have seen increased application in parts and structures for the automotive and aerospace industries. TPCs possess high specific mechanical properties and provide advantages in terms of automation and assembly compared to their thermoset counterparts [1]. Notably, the melt-processability of the thermoplastic matrix allows for short processing times, via for example press-forming, and for assembly by means of fusion bonding. The design of production and assembly processes for TPCs can be facilitated by predictive process design tools that allow for virtual tool and process optimizations [2] to achieve first-time-right manufacturing. These tools require an accurate description of the material behavior and deformation mechanisms during processing, including for example intraply shear, bending and friction [3,4,5]. Transverse squeeze flow is another deformation mechanism, that takes place during processing, especially in the case of a mismatch between tool cavity and blank thickness where it can lead to changes in the local microstructure, affecting the mechanical performance of the manufactured part. The inclusion of transverse squeeze flow in predictive simulation software requires reliable constitutive models and the associated characterization methods, which are not yet readily available. The literature describes a selection of methods with which the transverse flow behavior of unidirectional (UD) TPCs can be characterized [6]. This work will employ the so-called squeeze flow method[7,8] to characterize the transverse viscosity of UD C/LM-PAEK material.

**Background**

The squeeze flow method typically involves the compression of molten thermoplastic material between two heated compression platens. In this particular case, UD fiber reinforced TPC material will be considered. The experiments are conducted by compressing specimens at a fixed rate to a certain percentage of their original thickness whilst logging the required force and displacement,

<figure><img src="image_555.png" /></figure>
# Transverse squeeze flow of fibre reinforced thermoplastic composites

BIELEMAN Gerben¹,²,a,*, ROTINK Gijs¹, GROUVE Wouter J.B.¹,², KLOMPEN Edwin T.J.² and AKKERMAN Remko¹,²

¹Faculty of Engineering Technology, Chair of Production Technology, University of Twente, Enschede, the Netherlands

²ThermoPlastic composites Research Center (TPRC), Enschede, the Netherlands

a.g.bieleman@utwente.nl

**Keywords:** Squeeze Flow, Transverse Viscosity, Thermoplastic Composites

**Abstract.** Transverse squeeze flow is one of the deformation mechanisms that govern the forming of molten fiber reinforced thermoplastic composites. It is typically described by a transverse bulk viscosity, dictating the resistance against the flow of the combined constituents. In this work, the squeeze flow method was used to characterize the transverse viscosity of carbon fiber reinforced low-melting PAEK at three different compression rates. The experiments were recorded with a camera and the video images were analyzed to obtain the flow fields. A power law fluid model was fitted to the logged data, but found to be unable to describe the material behavior at all compression rates. Moreover, the video analyses indicated discrepancies between the observed specimen deformations and those predicted by the model. Future studies need to focus on the description of the squeeze flow behavior of UD C/LM-PAEK by different models (viscous, viscoelastic), using video-captured deformations for numerical fitting of the models.

**Introduction**

In the last decade, thermoplastic composites (TPCs) have seen increased application in parts and structures for the automotive and aerospace industries. TPCs possess high specific mechanical properties and provide advantages in terms of automation and assembly compared to their thermoset counterparts [1]. Notably, the melt-processability of the thermoplastic matrix allows for short processing times, via for example press-forming, and for assembly by means of fusion bonding. The design of production and assembly processes for TPCs can be facilitated by predictive process design tools that allow for virtual tool and process optimizations [2] to achieve first-time-right manufacturing. These tools require an accurate description of the material behavior and deformation mechanisms during processing, including for example intraply shear, bending and friction [3,4,5]. Transverse squeeze flow is another deformation mechanism, that takes place during processing, especially in the case of a mismatch between tool cavity and blank thickness where it can lead to changes in the local microstructure, affecting the mechanical performance of the manufactured part. The inclusion of transverse squeeze flow in predictive simulation software requires reliable constitutive models and the associated characterization methods, which are not yet readily available. The literature describes a selection of methods with which the transverse flow behavior of unidirectional (UD) TPCs can be characterized [6]. This work will employ the so-called squeeze flow method[7,8] to characterize the transverse viscosity of UD C/LM-PAEK material.

**Background**

The squeeze flow method typically involves the compression of molten thermoplastic material between two heated compression platens. In this particular case, UD fiber reinforced TPC material will be considered. The experiments are conducted by compressing specimens at a fixed rate to a certain percentage of their original thickness whilst logging the required force and displacement,

<figure><img src="image_566.png" /></figure>

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schematically shown in Figure 1. The flow between the platens can take different velocity profiles based on the boundary conditions between the specimen and compression platen. These conditions are shown in Figure 2, with Figure 2a showing the so-called plug flow which occurs when there is full slip between the specimen and the compression platen and Figure 2b describing shear flow with zero slip at the boundary. Figure 2c describes a combined profile of plug flow and shear flow, which may occur when there is a stick-slip response at the boundary. Viscous or viscoelastic models can be fitted based on the logged force and displacement, taking into account the boundary conditions during the experiment.

<figure><img src="image_556.png" /><figcaption>Figure 1: Squeeze flow experiment</figcaption></figure>

<figure><img src="image_557.png" /><figcaption>Figure 2: Flow types between parallel plates</figcaption></figure>

## Problem statement

Currently, the squeeze flow of high fiber-filled UD carbon fiber polyaryletherketones (C/PAEKs) cannot be characterized accurately. Slange indicates in his PhD thesis that the literature does not contain a suitable model for the transverse viscosity of C/PEEK in the temperature window of 340 °C to 400 °C [9]. Moreover, the literature review by Wang et al. shows a wide range of transverse viscosity values for high fiber-filled C/PAEKs [6], with differences in the characterization of C/PEEK up to an order of magnitude. Furthermore, these transverse viscosity characterizations were obtained with various methods. It can be concluded that there is no established methodology and model to describe the transverse squeeze flow of C/PAEK composites. Moreover, up to the author's best knowledge, the transverse squeeze flow behavior of carbon fiber low-melting polyaryletherketone (C/LM-PAEK) has not been characterized yet in the literature. This study aims to characterize the transverse viscosity of UD C/LM-PAEK composites using the squeeze flow method. A power law fluid model is used to fit the experimental data.

## Material

The material used in this study was C/LM-PAEK TC1225, by Toray Advanced Composites [10]. The material was consolidated according to the manufacturer's specifications into UD laminates with a thickness of 64 plies (9.2 mm) on a Pinette press in a 12 by 12 in² picture frame mold. Specimens were milled from the UD laminates, with in-plane dimensions of 50 by 50 mm². Markings were applied to one of the sides with the exposed fiber ends, to visualize the deformation during testing, displayed in Figure 3. Lastly, 0.05mm thick M-Tech F metal foils by Martin [11], were cut to 150 by 75 mm², cleaned with IPA, and placed above and underneath the specimens during the experiments for sample removal between measurements.
schematically shown in Figure 1. The flow between the platens can take different velocity profiles based on the boundary conditions between the specimen and compression platen. These conditions are shown in Figure 2, with Figure 2a showing the so-called plug flow which occurs when there is full slip between the specimen and the compression platen and Figure 2b describing shear flow with zero slip at the boundary. Figure 2c describes a combined profile of plug flow and shear flow, which may occur when there is a stick-slip response at the boundary. Viscous or viscoelastic models can be fitted based on the logged force and displacement, taking into account the boundary conditions during the experiment.

<figure><img src="image_567.png" /><figcaption>Figure 1: Squeeze flow experiment</figcaption></figure>

<figure><img src="image_568.png" /><figcaption>Figure 2: Flow types between parallel plates</figcaption></figure>

## Problem statement

Currently, the squeeze flow of high fiber-filled UD carbon fiber polyaryletherketones (C/PAEKs) cannot be characterized accurately. Slange indicates in his PhD thesis that the literature does not contain a suitable model for the transverse viscosity of C/PEEK in the temperature window of 340 °C to 400 °C [9]. Moreover, the literature review by Wang et al. shows a wide range of transverse viscosity values for high fiber-filled C/PAEKs [6], with differences in the characterization of C/PEEK up to an order of magnitude. Furthermore, these transverse viscosity characterizations were obtained with various methods. It can be concluded that there is no established methodology and model to describe the transverse squeeze flow of C/PAEK composites. Moreover, up to the author's best knowledge, the transverse squeeze flow behavior of carbon fiber low-melting polyaryletherketone (C/LM-PAEK) has not been characterized yet in the literature. This study aims to characterize the transverse viscosity of UD C/LM-PAEK composites using the squeeze flow method. A power law fluid model is used to fit the experimental data.

## Material

The material used in this study was C/LM-PAEK TC1225, by Toray Advanced Composites [10]. The material was consolidated according to the manufacturer's specifications into UD laminates with a thickness of 64 plies (9.2 mm) on a Pinette press in a 12 by 12 in² picture frame mold. Specimens were milled from the UD laminates, with in-plane dimensions of 50 by 50 mm². Markings were applied to one of the sides with the exposed fiber ends, to visualize the deformation during testing, displayed in Figure 3. Lastly, 0.05mm thick M-Tech F metal foils by Martin [11], were cut to 150 by 75 mm², cleaned with IPA, and placed above and underneath the specimens during the experiments for sample removal between measurements.
<figure><img src="image_558.png" /><figcaption>Figure 3: Markings for visualization of the flow front of squeeze flow specimens</figcaption></figure>

<figure><img src="image_559.png" /><figcaption>Figure 3: Specimen before squeeze flow experiment</figcaption></figure>

## Methodology

Squeeze flow tests were performed by compressing the specimens between two heated compression plates while logging force and displacement. The experiments were performed on a compression paten fixture, which was mounted in an INSTRON universal testing machine, shown in Figure 5. The setup contains two heated and temperature-controlled compression platens. Furthermore, the setup makes use of two linear variable differential sensors (LVDT) to measure the relative displacement between both compression platens.

<figure><img src="image_560.png" /><figcaption>Figure 4: Experimental setup for squeeze flow characterization</figcaption></figure>

All squeeze flow experiments were carried out according to the same experimental procedure. The specimens were first taken out of a vacuum oven where they were stored after manufacturing to ensure that they were dry. The specimens were placed between two metal foils in the center of the bottom compression fixture. The metal foils facilitate specimen removal and assure a no-slip boundary condition between the specimens, which was verified by video analyses of the markings on the specimens. The top compression platen was placed 3 mm above the specimen. Subsequently, the specimen was heated to a temperature of 360°C in 7 minutes ensuring a uniform temperature throughout the specimen. The experimental procedure was started by moving the top compression fixture downwards until a force of 20 N was registered, after which the set compression rate in the form of a constant displacement rate of the top compression platen was prescribed. Three displacement rates were considered, namely 0.01, 0.1 and 1.0 mm/s. The experiment consists of compressing the specimen down to 40% of the original thickness (60%
<figure><img src="image_569.png" /><figcaption>Figure 3: Markings for visualization of the flow front of squeeze flow specimens</figcaption></figure>

<figure><img src="image_570.png" /><figcaption>Figure 3: Specimen before squeeze flow experiment</figcaption></figure>

## Methodology

Squeeze flow tests were performed by compressing the specimens between two heated compression plates while logging force and displacement. The experiments were performed on a compression paten fixture, which was mounted in an INSTRON universal testing machine, shown in Figure 5. The setup contains two heated and temperature-controlled compression platens. Furthermore, the setup makes use of two linear variable differential sensors (LVDT) to measure the relative displacement between both compression platens.

<figure><img src="image_571.png" /><figcaption>Figure 4: Experimental setup for squeeze flow characterization</figcaption></figure>

All squeeze flow experiments were carried out according to the same experimental procedure. The specimens were first taken out of a vacuum oven where they were stored after manufacturing to ensure that they were dry. The specimens were placed between two metal foils in the center of the bottom compression fixture. The metal foils facilitate specimen removal and assure a no-slip boundary condition between the specimens, which was verified by video analyses of the markings on the specimens. The top compression platen was placed 3 mm above the specimen. Subsequently, the specimen was heated to a temperature of 360°C in 7 minutes ensuring a uniform temperature throughout the specimen. The experimental procedure was started by moving the top compression fixture downwards until a force of 20 N was registered, after which the set compression rate in the form of a constant displacement rate of the top compression platen was prescribed. Three displacement rates were considered, namely 0.01, 0.1 and 1.0 mm/s. The experiment consists of compressing the specimen down to 40% of the original thickness (60%
compression ratio). All experiments were performed in triplets, using a new specimen for each test.

An analytical plane strain model for incompressible creeping shear flow with a no-slip condition at the wall was used to describe the transverse squeeze flow of the C/LM-PAEK samples for this experimental setup [12]. The flow was assumed to take place transverse to the fiber direction. In this model, the bulk viscosity of the molten TPC was described as a power law fluid, with a viscosity parameter k and a shear rate dependence parameter n. The model provides an equation for the compression force Fc, described by Equation 1.

$$F_c = \left( \frac{\dot{h}^n}{h^{2n+1}} \right) \left( \frac{2n+1}{n} \right)^n \left( \frac{k_s L w^{(n+2)}}{n+2} \right) \quad (1)$$

in which $\dot{h}$ describes the compression rate, h de current specimen thickness, L the length of the specimen in fiber direction and w the current width of the specimen. The width of the specimen w was calculated based on the assumption of constant volume.

The deformation of the specimens during the experiments was captured in real time using a camera mounted on a tripod stand. The velocity field was extracted from the footage with the use of MATLAB code by tracking the marked lines. So-called regions of interest were manually selected in the video file on one of the lines, as shown in Figure 6. Around these selected regions of interest, boxes were drawn in which points with steep greyscale gradients were selected [13]. These points were tracked throughout the video by means of a KLT algorithm [14,15]. The location of the tracked points was marked and stored. The stored locations, combined with the time between frames, were used to determine the velocity of these points. A comparison between the obtained velocity in the points and the velocity profile assumed by the analytical fitted power law fluid model could be made, to give insight into the models' accuracy. The velocity assumed by the fitted power law model can be described by Equation 2.

$$V_x(x, z) = -\dot{h} \left(\frac{1+2*n}{1+n}\right) \left(\frac{1}{h}\right)^{\frac{1+2n}{n}} 2^{\left(\frac{1}{n}+1\right)} x \left[ z^{\frac{1}{n}+1} - \left(\left(\frac{h}{2}\right)^{\frac{1}{n}+1}\right) \right] \quad (2)$$

<figure><img src="image_561.png" /><figcaption>Figure 5: Points traced (yellow stars) on a line to obtain the local velocity during the squeeze flow of a specimen</figcaption></figure>

## Results

The averaged results of the force versus the compression ratio for the three different compression rates are shown by the solid lines in Figure 7. The results show a start-up phase between 0% and 15% compression. The plot shows that an increase in compression rate yields an increase in force response. The data across the three measured samples per compression rate is depicted by the colored areas around the solid lines and shows little deviation between measurements. The fitted power law fluid model is shown by the dashed lines. The data was fitted between the compression range of 15% to 30% and was extrapolated across the full plotted compression range. The model was fitted to the measured data for all compression rates, obtaining fitting parameters k =1.89e4 Pas and n =0.663.
compression ratio). All experiments were performed in triplets, using a new specimen for each test.

An analytical plane strain model for incompressible creeping shear flow with a no-slip condition at the wall was used to describe the transverse squeeze flow of the C/LM-PAEK samples for this experimental setup [12]. The flow was assumed to take place transverse to the fiber direction. In this model, the bulk viscosity of the molten TPC was described as a power law fluid, with a viscosity parameter k and a shear rate dependence parameter n. The model provides an equation for the compression force Fc, described by Equation 1.

$$F_c = \left( \frac{\dot{h}^n}{h^{2n+1}} \right) \left( \frac{2n+1}{n} \right)^n \left( \frac{k_s L w^{(n+2)}}{n+2} \right) \quad (1)$$

in which $\dot{h}$ describes the compression rate, h de current specimen thickness, L the length of the specimen in fiber direction and w the current width of the specimen. The width of the specimen w was calculated based on the assumption of constant volume.

The deformation of the specimens during the experiments was captured in real time using a camera mounted on a tripod stand. The velocity field was extracted from the footage with the use of MATLAB code by tracking the marked lines. So-called regions of interest were manually selected in the video file on one of the lines, as shown in Figure 6. Around these selected regions of interest, boxes were drawn in which points with steep greyscale gradients were selected [13]. These points were tracked throughout the video by means of a KLT algorithm [14,15]. The location of the tracked points was marked and stored. The stored locations, combined with the time between frames, were used to determine the velocity of these points. A comparison between the obtained velocity in the points and the velocity profile assumed by the analytical fitted power law fluid model could be made, to give insight into the models' accuracy. The velocity assumed by the fitted power law model can be described by Equation 2.

$$V_x(x, z) = -\dot{h} \left(\frac{1+2*n}{1+n}\right) \left(\frac{1}{h}\right)^{\frac{1+2n}{n}} 2^{\left(\frac{1}{n}+1\right)} x \left[ z^{\frac{1}{n}+1} - \left(\left(\frac{h}{2}\right)^{\frac{1}{n}+1}\right) \right] \quad (2)$$

<figure><img src="image_572.png" /><figcaption>Figure 5: Points traced (yellow stars) on a line to obtain the local velocity during the squeeze flow of a specimen</figcaption></figure>

## Results

The averaged results of the force versus the compression ratio for the three different compression rates are shown by the solid lines in Figure 7. The results show a start-up phase between 0% and 15% compression. The plot shows that an increase in compression rate yields an increase in force response. The data across the three measured samples per compression rate is depicted by the colored areas around the solid lines and shows little deviation between measurements. The fitted power law fluid model is shown by the dashed lines. The data was fitted between the compression range of 15% to 30% and was extrapolated across the full plotted compression range. The model was fitted to the measured data for all compression rates, obtaining fitting parameters k =1.89e4 Pas and n =0.663.
<figure><img src="image_562.png" /><figcaption>Figure 6: Force versus compression ratio of raw squeeze flow data, averaged data and fitted model</figcaption></figure>

The velocity of the line with the yellow stars in Figure 6 was analyzed for the three different compression rates and evaluated at 15% and 30% compression. Figure 8a shows the velocity in the tracked points on the line in Figure 6 (circles) at a compression of 15%, as well as the line which describes the velocity assumed by the fitted power law model. Figure 8b shows the zoomed-in plot of Figure 8a, to clearly show the results at the lower compression rates. Similarly, Figures 8c and 8d show the velocity profile of the line at 30% compression.

<figure><img src="image_563.png" /><figcaption>(a) Velocity profiles 15% compression</figcaption></figure>

<figure><img src="image_564.png" /><figcaption>(b) Zoomed in velocity profiles 15% compression</figcaption></figure>
<figure><img src="image_573.png" /><figcaption>Figure 6: Force versus compression ratio of raw squeeze flow data, averaged data and fitted model</figcaption></figure>

The velocity of the line with the yellow stars in Figure 6 was analyzed for the three different compression rates and evaluated at 15% and 30% compression. Figure 8a shows the velocity in the tracked points on the line in Figure 6 (circles) at a compression of 15%, as well as the line which describes the velocity assumed by the fitted power law model. Figure 8b shows the zoomed-in plot of Figure 8a, to clearly show the results at the lower compression rates. Similarly, Figures 8c and 8d show the velocity profile of the line at 30% compression.

<figure><img src="image_574.png" /><figcaption>(a) Velocity profiles 15% compression</figcaption></figure>

<figure><img src="image_575.png" /><figcaption>(b) Zoomed in velocity profiles 15% compression</figcaption></figure>
<figure><img src="image_565.png" /><figcaption>Figure 7: Velocity profiles of the tracked line from Figure 6</figcaption></figure>

Discussion

Start-up effects. The start-up effects shown in Figure 7 take place between 0% to 15% compression. These effects were excluded when fitting the power law fluid model. However, this start-up phase contains useful information, as in practical applications squeeze flow mostly takes place in scenarios with limited deformation. Henceforth, to describe press-forming or fusion bonding processes there is the need to understand what the contributions to the start-up phase are. Rasheed mentioned in his PhD thesis that this start-up phase contains the development of the shear flow front at the edges of the specimen, a condition assumed for the power law fluid model [12]. Further observations from the video footage show the deconsolidation of the specimens during heating of the specimens. The specimens rapidly increase in thickness during heating and start to form cracks as shown in Figure 9, with a specimen before heating on the left and the deconsolidated specimen on the right after heating. The cause of deconsolidation is likely due to elastic stresses stored in the fiber bed during manufacturing [16]. The effects of moisture could be excluded, as the specimens were stored in a vacuum oven. The cracks were observed to be partially reconsolidated during the initial compression phase. Given the observed deconsolidation, it is likely that part of the start-up response is due to reconsolidation.

<figure><img src="image_566.png" /><figcaption>Figure 8: Deconsolidation of a specimen during heating, left image before heating, right image after heating with deconsolidation (cracks) at the red arrows</figcaption></figure>

Boundary condition changes. The fitting of the power law fluid model could only be performed up to 30% compression, due to changes occurring in the fluid velocity field. The model used the assumption of shear flow within the specimen. However, at 30% compression, the flow field starts to change near the edges of the specimen from a shear flow profile to a so-called fountain flow profile. Figure 10 shows a micrograph of the edge of one of the specimens. The red arrows indicate the fountain flow originating from the center of the specimen and flowing towards the top and bottom surfaces. Figure 11 shows the onset of this fountain flow on the right side of the specimen
<figure><img src="image_576.png" /><figcaption>Figure 7: Velocity profiles of the tracked line from Figure 6</figcaption></figure>

Discussion

Start-up effects. The start-up effects shown in Figure 7 take place between 0% to 15% compression. These effects were excluded when fitting the power law fluid model. However, this start-up phase contains useful information, as in practical applications squeeze flow mostly takes place in scenarios with limited deformation. Henceforth, to describe press-forming or fusion bonding processes there is the need to understand what the contributions to the start-up phase are. Rasheed mentioned in his PhD thesis that this start-up phase contains the development of the shear flow front at the edges of the specimen, a condition assumed for the power law fluid model [12]. Further observations from the video footage show the deconsolidation of the specimens during heating of the specimens. The specimens rapidly increase in thickness during heating and start to form cracks as shown in Figure 9, with a specimen before heating on the left and the deconsolidated specimen on the right after heating. The cause of deconsolidation is likely due to elastic stresses stored in the fiber bed during manufacturing [16]. The effects of moisture could be excluded, as the specimens were stored in a vacuum oven. The cracks were observed to be partially reconsolidated during the initial compression phase. Given the observed deconsolidation, it is likely that part of the start-up response is due to reconsolidation.

<figure><img src="image_577.png" /><figcaption>Figure 8: Deconsolidation of a specimen during heating, left image before heating, right image after heating with deconsolidation (cracks) at the red arrows</figcaption></figure>

Boundary condition changes. The fitting of the power law fluid model could only be performed up to 30% compression, due to changes occurring in the fluid velocity field. The model used the assumption of shear flow within the specimen. However, at 30% compression, the flow field starts to change near the edges of the specimen from a shear flow profile to a so-called fountain flow profile. Figure 10 shows a micrograph of the edge of one of the specimens. The red arrows indicate the fountain flow originating from the center of the specimen and flowing towards the top and bottom surfaces. Figure 11 shows the onset of this fountain flow on the right side of the specimen
where the outer edges start to split up from the center. In some of the footage taken during the squeeze flow experiments, this splitting near the center of the specimen's edge seemed to originate from pre-existing cracks that formed during the heating as a result of deconsolidation.

<figure><img src="image_567.png" /><figcaption>Figure 9: Fountain flow, indicated with red arrows from the center of the sample to top and bottom surface</figcaption></figure>

<figure><img src="image_568.png" /><figcaption>Figure 10: Onset of fountain flow resulting from a crack at the specimen edge (red arrow)</figcaption></figure>

Power law fluid model. The power law fluid model fit displayed in Figure 7 shows an inability to describe the squeeze flow behavior of C/LM-PAEK. There are discrepancies between the model and measured force versus compression ratio at all compressoin rates. There could be a few causes for this inability to describe the squeeze flow of C/LM-PAEK. The first could be the type of fluid model selected. The choice for a power law fluid was made as it can be analytically fitted to the data. The material may show different viscous behavior, Moreover, it is well established that a polymer melt exhibits viscoelastic material behavior. Lastly, the assumption was made that no interactions between the solid fibers of the TPC melt would contribute to the deformation behavior during the squeeze. This assumption could be an oversimplification and possibly needs further exploration.

Velocity profiles. The velocity profile of the fitted power law fluid model and the measured velocity in the tracked points in Figure 8 show discrepancies, with the velocity assumed by the model overpredicting the observed values. In some instances, this overprediction was only a negligible amount, such as the velocity for the compression rate of 0.1 mm/s (green) in Figure 8b. However, in most cases, the velocity is significantly overpredicted by the model, up to as much as 30%. Moreover, the shape of the solid lines may differ from that of the imaginary line drawn through the measured points. The shape of the fitted line is based on the power law fluid model with the fitted parameter *n*. As stated earlier, the power law fluid model was not able to describe the flow behavior and will therefore also inaccurately describe the velocity field in the sample. In addition, the local differences in observed velocity field could be partly due to localized effects which seem to be the result of local slip between plies occurring during compression. This slip between plies is shown in Figure 11, yielding the jagged-like parabolic lines. It has to be noted that the validity of these results is limited to one of the single lines on the specimen, for better insight into the deformation of the specimen all lines need to be traced. Furthermore, lines are not ideal for the video tracking of a velocity in a single point. The identification of the regions of interest around these lines only has a grayscale gradient in one direction, where preferably it should be in both in-plane directions. The accuracy of the tracked points in z-direction was deemed limited, due to the previously mentioned reason. A grid of points would be a better choice to capture the specimens' deformations under squeeze flow conditions as a point possesses a greyscale gradient in both directions. More accurate measurements of the velocity field could in turn aid in the numerical fitting of different viscous or viscoelastic models to the squeeze flow response of UD C/LM-PAEK.
where the outer edges start to split up from the center. In some of the footage taken during the squeeze flow experiments, this splitting near the center of the specimen's edge seemed to originate from pre-existing cracks that formed during the heating as a result of deconsolidation.

<figure><img src="image_578.png" /><figcaption>Figure 9: Fountain flow, indicated with red arrows from the center of the sample to top and bottom surface</figcaption></figure>

<figure><img src="image_579.png" /><figcaption>Figure 10: Onset of fountain flow resulting from a crack at the specimen edge (red arrow)</figcaption></figure>

Power law fluid model. The power law fluid model fit displayed in Figure 7 shows an inability to describe the squeeze flow behavior of C/LM-PAEK. There are discrepancies between the model and measured force versus compression ratio at all compressoin rates. There could be a few causes for this inability to describe the squeeze flow of C/LM-PAEK. The first could be the type of fluid model selected. The choice for a power law fluid was made as it can be analytically fitted to the data. The material may show different viscous behavior, Moreover, it is well established that a polymer melt exhibits viscoelastic material behavior. Lastly, the assumption was made that no interactions between the solid fibers of the TPC melt would contribute to the deformation behavior during the squeeze. This assumption could be an oversimplification and possibly needs further exploration.

Velocity profiles. The velocity profile of the fitted power law fluid model and the measured velocity in the tracked points in Figure 8 show discrepancies, with the velocity assumed by the model overpredicting the observed values. In some instances, this overprediction was only a negligible amount, such as the velocity for the compression rate of 0.1 mm/s (green) in Figure 8b. However, in most cases, the velocity is significantly overpredicted by the model, up to as much as 30%. Moreover, the shape of the solid lines may differ from that of the imaginary line drawn through the measured points. The shape of the fitted line is based on the power law fluid model with the fitted parameter *n*. As stated earlier, the power law fluid model was not able to describe the flow behavior and will therefore also inaccurately describe the velocity field in the sample. In addition, the local differences in observed velocity field could be partly due to localized effects which seem to be the result of local slip between plies occurring during compression. This slip between plies is shown in Figure 11, yielding the jagged-like parabolic lines. It has to be noted that the validity of these results is limited to one of the single lines on the specimen, for better insight into the deformation of the specimen all lines need to be traced. Furthermore, lines are not ideal for the video tracking of a velocity in a single point. The identification of the regions of interest around these lines only has a grayscale gradient in one direction, where preferably it should be in both in-plane directions. The accuracy of the tracked points in z-direction was deemed limited, due to the previously mentioned reason. A grid of points would be a better choice to capture the specimens' deformations under squeeze flow conditions as a point possesses a greyscale gradient in both directions. More accurate measurements of the velocity field could in turn aid in the numerical fitting of different viscous or viscoelastic models to the squeeze flow response of UD C/LM-PAEK.
**Summary**

This work aimed to characterize the transverse squeeze flow of UD C/PAEK composites, based on the squeeze flow characterization methodology. To this extent, squeeze flow experiments were performed at three compression rates to obtain the force versus compression ratio response of UD C/LM-PAEK specimens. An analytical power law fluid model with a transverse bulk viscosity was fitted to the data. The implemented model was not able to describe the material behavior of C/LM-PAEK for the experimental conditions. Analysis of the flow, by means of the velocity of a marking on the specimens' side showed discrepancies between the velocity obtained from the video images and the velocity based on the fitted power law model. Future research should focus on the description of the squeeze flow of C/PAEK, by means of a different viscous or viscoelastic model, with numerical fitting based on the captured specimen deformation during the squeeze flow experiments.

**Acknowledgments**

This work was performed as part of the Perspectief research program Enlighten, which is financed by the Dutch Research Council (NWO). The authors also gratefully acknowledge the in-kind and technical support from the industrial and academic members of the ThermoPlastic composites Research Center (TPRC)

**References**

[1] A.R. Offringa, "Thermoplastic composites - Rapid processing applications," Composites Part A: Applied Science and Manufacturing, vol. 27, no. 4 PART A, pp. 329-336, 1996.

[2] A.C. Long, Composites forming technologies. Elsevier. 2014

[3] U. Sachs, Friction and bending in thermoplastic composites forming processes p. 101. 2014. Enschede, Netherlands: University of Twente.

[4] D. Brands, L.G. di Genova, E.R. Pierik, W.J.B. Grouve, S. Wijskamp, R. Akkerman, Formability experiments for unidirectional thermoplastics composites, in: 25th International Conference on Material Forming, Braga, Portugal: ESAFORM, 2022, pp. 1358-1371

[5] J.S.U. Schell, J. Guilleminot, C. Binetruy, and P. Krawczak, "Computational and experimental analysis of fusion bonding in thermoplastic composites: Influence of process parameters," Journal of Materials Processing Technology, vol. 209, no. 11, pp. 5211-5219, Jun. 2009, doi: https://doi.org/10.1016/j.jmatprotec.2009.03.008

[6] J. Wang et al., "A review of experimental methods for characterising composite viscosities of continuous fibre-reinforced polymer composites," Korea-australia Rheology Journal, vol. 35, no. 2, pp. 57-68, Feb. 2023, doi: https://doi.org/10.1007/s13367-023-00053-2

[7] S.F. Shuler and S.G. Advani, "Transverse squeeze flow of concentrated aligned fibers in viscous fluids," Journal of Non-Newtonian Fluid Mechanics, vol. 65, no. 1, pp. 47-74, Jul. 1996, doi: https://doi.org/10.1016/0377-0257(96)01440-1

[8] T.G. Rogers, "Squeezing flow of fibre-reinforced viscous fluids," Journal of Engineering Mathematics, vol. 23, no. 1, pp. 81-89, Mar. 1989, doi: https://doi.org/10.1007/bf00058434

[9] T.K. Slange, "Rapid Manufacturing of Tailored Thermoplastic Composites by Automated Lay-up and Stamp Forming: A Study on the Consolidation Mechanisms," research.utwente.nl, Mar. 2019, doi: https://doi.org/10.3990/1.9789036547284

[10] "Toray Cetex® TC1225 - Toray Advanced Composites," Advanced Composites. https://www.toraytac.com/product-explorer/products/gXuK/Toray-Cetex-TC1225
**Summary**

This work aimed to characterize the transverse squeeze flow of UD C/PAEK composites, based on the squeeze flow characterization methodology. To this extent, squeeze flow experiments were performed at three compression rates to obtain the force versus compression ratio response of UD C/LM-PAEK specimens. An analytical power law fluid model with a transverse bulk viscosity was fitted to the data. The implemented model was not able to describe the material behavior of C/LM-PAEK for the experimental conditions. Analysis of the flow, by means of the velocity of a marking on the specimens' side showed discrepancies between the velocity obtained from the video images and the velocity based on the fitted power law model. Future research should focus on the description of the squeeze flow of C/PAEK, by means of a different viscous or viscoelastic model, with numerical fitting based on the captured specimen deformation during the squeeze flow experiments.

**Acknowledgments**

This work was performed as part of the Perspectief research program Enlighten, which is financed by the Dutch Research Council (NWO). The authors also gratefully acknowledge the in-kind and technical support from the industrial and academic members of the ThermoPlastic composites Research Center (TPRC)

**References**

[1] A.R. Offringa, "Thermoplastic composites - Rapid processing applications," Composites Part A: Applied Science and Manufacturing, vol. 27, no. 4 PART A, pp. 329-336, 1996.

[2] A.C. Long, Composites forming technologies. Elsevier. 2014

[3] U. Sachs, Friction and bending in thermoplastic composites forming processes p. 101. 2014. Enschede, Netherlands,: University of Twente.

[4] D. Brands, L.G. di Genova, E.R. Pierik, W.J.B. Grouve, S. Wijskamp, R. Akkerman, Formability experiments for unidirectional thermoplastics composites, in: 25th International Conference on Material Forming, Braga, Portugal: ESAFORM, 2022, pp. 1358-1371

[5] J.S.U. Schell, J. Guilleminot, C. Binetruy, and P. Krawczak, "Computational and experimental analysis of fusion bonding in thermoplastic composites: Influence of process parameters," Journal of Materials Processing Technology, vol. 209, no. 11, pp. 5211-5219, Jun. 2009, doi: https://doi.org/10.1016/j.jmatprotec.2009.03.008

[6] J. Wang et al., "A review of experimental methods for characterising composite viscosities of continuous fibre-reinforced polymer composites," Korea-australia Rheology Journal, vol. 35, no. 2, pp. 57-68, Feb. 2023, doi: https://doi.org/10.1007/s13367-023-00053-2

[7] S.F. Shuler and S.G. Advani, "Transverse squeeze flow of concentrated aligned fibers in viscous fluids," Journal of Non-Newtonian Fluid Mechanics, vol. 65, no. 1, pp. 47-74, Jul. 1996, doi: https://doi.org/10.1016/0377-0257(96)01440-1

[8] T.G. Rogers, "Squeezing flow of fibre-reinforced viscous fluids," Journal of Engineering Mathematics, vol. 23, no. 1, pp. 81-89, Mar. 1989, doi: https://doi.org/10.1007/bf00058434

[9] T.K. Slange, "Rapid Manufacturing of Tailored Thermoplastic Composites by Automated Lay-up and Stamp Forming: A Study on the Consolidation Mechanisms," research.utwente.nl, Mar. 2019, doi: https://doi.org/10.3990/1.9789036547284

[10] "Toray Cetex® TC1225 - Toray Advanced Composites," Advanced Composites. https://www.toraytac.com/product-explorer/products/gXuK/Toray-Cetex-TC1225
[11] “Liefersortiment M-Tech ® F -Metallfolienbänder,” 2011. https://georgmartin.com/wp-content/uploads/2022/02/Liefersortiment_M-Tech-F.Boxen.pdf

[12] M. Iqbal, “Compression molding of chopped woven thermoplastic composite flakes: a study on processing and performance,” May 2016, doi: https://doi.org/10.3990/1.9789036541510

[13] L.D. Bruce and T. Kanade. “An Iterative Image Registration Technique with an Application to Stereo Vision,” Proceedings of the 7th International Joint Conference on Artificial Intelligence, April, 1981, pp. 674–679.

[14] J. Shi and C. Tomasi, "Good Features to Track," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 1994, pp. 593–600.

[15] C. Tomasi and T. Kanade, Detection and Tracking of Point Features, Computer Science Department, Carnegie Mellon University, April, 1991.

[16] L. Amedewovo, L. Orgéas, B. de Parscau du Plessix, N. Lefèvre, A. Lévy, and S. Le Corre, “Deconsolidation of carbon fiber-reinforced PEKK laminates: 3D real-time in situ observation with synchrotron X-ray microtomography,” Composites Part A: Applied Science and Manufacturing, vol. 177, pp. 107917–107917, Feb. 2024, doi: https://doi.org/10.1016/j.compositesa.2023.107917
[11] “Liefersortiment M-Tech ® F -Metallfolienbänder,” 2011. https://georgmartin.com/wp-content/uploads/2022/02/Liefersortiment_M-Tech-F.Boxen.pdf

[12] M. Iqbal, “Compression molding of chopped woven thermoplastic composite flakes: a study on processing and performance,” May 2016, doi: https://doi.org/10.3990/1.9789036541510

[13] L.D. Bruce and T. Kanade. “An Iterative Image Registration Technique with an Application to Stereo Vision,” Proceedings of the 7th International Joint Conference on Artificial Intelligence, April, 1981, pp. 674–679.

[14] J. Shi and C. Tomasi, "Good Features to Track," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 1994, pp. 593–600.

[15] C. Tomasi and T. Kanade, Detection and Tracking of Point Features, Computer Science Department, Carnegie Mellon University, April, 1991.

[16] L. Amedewovo, L. Orgéas, B. de Parscau du Plessix, N. Lefèvre, A. Lévy, and S. Le Corre, “Deconsolidation of carbon fiber-reinforced PEKK laminates: 3D real-time in situ observation with synchrotron X-ray microtomography,” Composites Part A: Applied Science and Manufacturing, vol. 177, pp. 107917–107917, Feb. 2024, doi: https://doi.org/10.1016/j.compositesa.2023.107917
# Investigating TP-AFP process parameters through a mechanical testing approach

IANNONE Vincenzo¹,ᵃ*, DE NICOLA Felice¹,ᵇ, COSTANTINI Mario¹,ᶜ, GIUSTO Giovangiuseppe¹,ᵈ and CARLONE Pierpaolo²,ᵉ

¹PRMC Lab, CIRA – Italian Aerospace Research Center, Via Maiorise, 1, 81043 Capua (CE), Italy

²Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy

ᵃv.iannone@cira.it, ᵇf.denicola@cira.it, ᶜm costantini@cira.it, ᵈg.giusto@cira.it, ᵉpcarlone@unisa.it

**Keywords:** TP-AFP, Advanced Manufacturing, Process Optimization, Mechanical Testing, ILSS, Composite, Forming

**Abstract.** The present work investigates a non-standard approach for tuning TP-AFP process parameters, employing an in-house-designed setup for CF/PEEK ring coupons production and mechanical testing to assess interlaminar shear strength (ILSS). The TP-AFP setup features a laying head based on hot gas torch heating source, controlled pressure on a compaction roller, IR thermal monitoring, all operated by a six-degrees-of-freedom (DOF) robotic anthropomorphic arm. Through a non-standard split-disk setup, *ILSS* is thus evaluated, with its relation with key process parameters such as laydown speed, pressure, and temperature investigated. Although there is a high scatter level of the mechanical characterization parameters of the specimens, the analysis highlights how there is a direct dependence between the AFP process parameters - especially the temperature - and the *ILSS* values calculated through the split-disk testing carried out.

**Introduction**

The growing demands of the Industry 4.0 era, coupled with the need for both enhanced manufacturing efficiency and production flexibility, have led to a surge in automation within the fiber-reinforced polymer composite industry. The quest for solutions that balance process quality, versatility, and efficiency has driven the adoption of robotic automated processes and three-dimensional printing of continuous-fiber reinforcements. Among these, automated fiber placement (AFP) which involves the deposition, the compaction and the consolidation in-situ of pre-impregnated narrow bands to form complex multilayer laminates, stands out as a prominent technique [1]. The AFP is a versatile process consisting of full six degrees of freedom and is capable of laying tows on a mold or mandrel simultaneously cutting, stopping, and restarting individual tow. One of the most attractive aspects of such process is its control of individual tows (also termed as “tow steering”) which makes AFP capable of laying material on curved and complex shapes [2]. Furthermore in recent times the developing of thermoplastic composites has seen a notable increase driven by their peculiar characteristics of enhanced damage tolerance, high impact resistance, resistance to chemicals and cryogenic temperatures, extended shelf life, low storage costs, weldability, and recyclability [3,4].

The in-situ consolidation process - or fusion bonding - can be subdivided in four main phases, listed below in Fig. 1. In the first two stages the tows undergo heating and then are brought into contact under pressure. As the interfaces meet, polymer chains diffuse and entangle across the microscopic level through the so called autohesion process once interlaminar voids have been suppressed. Upon solidification, the polymer chains become fixed, leading to the development of

<figure><img src="image_569.png" /></figure>
# Investigating TP-AFP process parameters through a mechanical testing approach

IANNONE Vincenzo¹,ᵃ*, DE NICOLA Felice¹,ᵇ, COSTANTINI Mario¹,ᶜ,
GIUSTO Giovangiuseppe¹,ᵈ and CARLONE Pierpaolo²,ᵉ

¹PRMC Lab, CIRA – Italian Aerospace Research Center, Via Maiorise, 1, 81043 Capua (CE), Italy

²Department of Industrial Engineering, University of Salerno, Via Giovanni Paolo II, 132, 84084 Fisciano (SA), Italy

ᵃv.iannone@cira.it, ᵇf.denicola@cira.it, ᶜm costantini@cira.it, ᵈg.giusto@cira.it,
ᶜpcarlone@unisa.it

**Keywords:** TP-AFP, Advanced Manufacturing, Process Optimization, Mechanical Testing, ILSS, Composite, Forming

**Abstract.** The present work investigates a non-standard approach for tuning TP-AFP process parameters, employing an in-house-designed setup for CF/PEEK ring coupons production and mechanical testing to assess interlaminar shear strength (ILSS). The TP-AFP setup features a laying head based on hot gas torch heating source, controlled pressure on a compaction roller, IR thermal monitoring, all operated by a six-degrees-of-freedom (DOF) robotic anthropomorphic arm. Through a non-standard split-disk setup, *ILSS* is thus evaluated, with its relation with key process parameters such as laydown speed, pressure, and temperature investigated. Although there is a high scatter level of the mechanical characterization parameters of the specimens, the analysis highlights how there is a direct dependence between the AFP process parameters - especially the temperature - and the *ILSS* values calculated through the split-disk testing carried out.

**Introduction**

The growing demands of the Industry 4.0 era, coupled with the need for both enhanced manufacturing efficiency and production flexibility, have led to a surge in automation within the fiber-reinforced polymer composite industry. The quest for solutions that balance process quality, versatility, and efficiency has driven the adoption of robotic automated processes and three-dimensional printing of continuous-fiber reinforcements. Among these, automated fiber placement (AFP) which involves the deposition, the compaction and the consolidation in-situ of pre-impregnated narrow bands to form complex multilayer laminates, stands out as a prominent technique [1]. The AFP is a versatile process consisting of full six degrees of freedom and is capable of laying tows on a mold or mandrel simultaneously cutting, stopping, and restarting individual tow. One of the most attractive aspects of such process is its control of individual tows (also termed as “tow steering”) which makes AFP capable of laying material on curved and complex shapes [2]. Furthermore in recent times the developing of thermoplastic composites has seen a notable increase driven by their peculiar characteristics of enhanced damage tolerance, high impact resistance, resistance to chemicals and cryogenic temperatures, extended shelf life, low storage costs, weldability, and recyclability [3,4].

The in-situ consolidation process - or fusion bonding - can be subdivided in four main phases, listed below in Fig. 1. In the first two stages the tows undergo heating and then are brought into contact under pressure. As the interfaces meet, polymer chains diffuse and entangle across the microscopic level through the so called autohesion process once interlaminar voids have been suppressed. Upon solidification, the polymer chains become fixed, leading to the development of

<figure><img src="image_580.png" /></figure>
mechanical strength at the interface. For engineering polymers such as PEEK with moderately high molecular weight, the diffusion of polymer chains is likely constrained during in-situ consolidation due to the brief transient time at elevated temperatures. Nevertheless, effective interfacial strength can be achieved with a sufficient number of polymer chain entanglements [5].

<figure><img src="image_570.png" /><figcaption>Figure 1: Main phases of the in-situ consolidation of thermoplastic composites process [5]</figcaption></figure>

The stages of the AFP process are presented below in Fig. 2. A very short portion of the towpreg and substrate is heated rapidly to the polymer matrix melting temperature. The molten surfaces are then pressed together by a compaction roller. The heated thermoplastic towpreg and substrate finally cool and solidify.

<figure><img src="image_571.png" /><figcaption>Figure 2: In-situ consolidation process of thermoplastic towpreg placement [5]</figcaption></figure>

This approach offers the advantage of rapid in-situ processing without the need of autoclave curing but despite the potential benefits, the TP-AFP ISC process still faces several challenges such as lower mechanical properties that must be addressed.

Interlaminar shear strength, together with void content, crystallinity and fracture toughness, is a representative indicator of manufacturing quality. A summary of optimised processing parameters based on evaluation of single lap shear testing (SLS) on various thermoplastic composites is presented below in Table 1. It is also important to notice that the “optimised” processing parameters are strictly dependent on the machine setup and material system. In the cited studies [6-9] samples were manufactured usually by planar geometries while in the present work the study has been carried out manufacturing ring shaped SLS coupons.
mechanical strength at the interface. For engineering polymers such as PEEK with moderately high molecular weight, the diffusion of polymer chains is likely constrained during in-situ consolidation due to the brief transient time at elevated temperatures. Nevertheless, effective interfacial strength can be achieved with a sufficient number of polymer chain entanglements [5].

<figure><img src="image_581.png" /><figcaption>Figure 1: Main phases of the in-situ consolidation of thermoplastic composites process [5]</figcaption></figure>

The stages of the AFP process are presented below in Fig. 2. A very short portion of the towpreg and substrate is heated rapidly to the polymer matrix melting temperature. The molten surfaces are then pressed together by a compaction roller. The heated thermoplastic towpreg and substrate finally cool and solidify.

<figure><img src="image_582.png" /><figcaption>Figure 2: In-situ consolidation process of thermoplastic towpreg placement [5]</figcaption></figure>

This approach offers the advantage of rapid in-situ processing without the need of autoclave curing but despite the potential benefits, the TP-AFP ISC process still faces several challenges such as lower mechanical properties that must be addressed.

Interlaminar shear strength, together with void content, crystallinity and fracture toughness, is a representative indicator of manufacturing quality. A summary of optimised processing parameters based on evaluation of single lap shear testing (SLS) on various thermoplastic composites is presented below in Table 1. It is also important to notice that the “optimised” processing parameters are strictly dependent on the machine setup and material system. In the cited studies [6-9] samples were manufactured usually by planar geometries while in the present work the study has been carried out manufacturing ring shaped SLS coupons.
<table><caption>Table 1: Summary of thermoplastic polymer matrix optimization studies featuring SLS</caption><thead><tr><th>Material</th><th>Heat source</th><th>Laydown speed</th><th>Compaction force or pressure</th><th>Tool temperature</th><th>Ref.</th></tr></thead><tbody><tr><td>CF/PEEK</td><td>Laser</td><td>200 – 800 mm/s</td><td>1025 N</td><td>Unheated</td><td>Di Francesco et al. [6]</td></tr><tr><td>CF/PEEK</td><td>HGT</td><td>57 – 95 mm/s</td><td>333 – 556 N</td><td>20 – 150 °C</td><td>Qureshi et al. [7]</td></tr><tr><td>CF/LM-PAEK</td><td>Laser</td><td>125 – 250 mm/s</td><td>0.6 MPa</td><td>20 and 200 °C</td><td>Schiel et al. [8]</td></tr><tr><td>CF/PEI</td><td>HGT</td><td>0.5 – 3.5 mm/s</td><td>75 – 275 N</td><td>100 – 140 °C</td><td>Sabido [9]</td></tr></tbody></table>

Di Francesco et al. [6] indicates the increase in material laydown velocity reduces the degree of crystallinity in in-situ consolidated CF/PEEK. On the other hand, Schiel et al. [8] reported that the deposition rate did not affect crystallinity of in-situ consolidated CF/LM-PAEK.

HGT based heating is only able to achieve approximately 60% of the interlaminar strength of autoclaved parts and it decreases with deposition velocity according to Qureshi et al. [7]. Lower heating efficiency by convection lead to insufficient time to melt the PEEK polymer matrix for adequate autohesion.

## Materials and Methods

The material used in this investigation is a unidirectional thermoplastic prepreg tape consisting of Poly-ether-ether-ketone (PEEK) reinforced with standard modulus carbon fibers (whose trade name used by Cytec is APC-2).

PEEK is by far the most commonly used thermoplastic in tape placement processes. The main properties of CF/PEEK have been listed in Table 2.

<table><caption>Table 2: Material properties of unidirectional CF/PEEK</caption><thead><tr><th>Property</th><th>CFRTP</th></tr></thead><tbody><tr><td>Prepreg</td><td>APC-2</td></tr><tr><td>Fiber</td><td>AS4</td></tr><tr><td>Fiber volume fraction V<sub>f</sub></td><td>60%</td></tr><tr><td>Tape width</td><td>6.35 mm</td></tr><tr><td>Melt temperature T<sub>m</sub></td><td>343 °C</td></tr><tr><td>Glass transition temperature T<sub>g</sub></td><td>143 °C</td></tr><tr><td>Crystallization enthalpy H<sub>ref</sub></td><td>122.5 Jg<sup>-1</sup></td></tr></tbody></table>

The CFRTP AFP setup consists of a laying head based on hot gas torch (HGT) heating source, controlled pressure on a stainless steel compaction roller (30 mm diameter, 30 mm width) and IR thermal monitoring. The head is moved by a six DOF anthropomorphic arm (KUKA KR240 R2900-2). Tapes are laid down on a steel cylindrical mandrel with 300 mm diameter (see Fig. 3a). The end effector in Fig. 3b enables the placement of a single 0.25" unidirectional prepreg tow.

The heat source plays a crucial role in the key manufacturing parameter of in-situ consolidation process. Currently radiant heating using near-infrared (NIR) lasers are more commonly used in AFP. However, despite having lower thermal efficiency, HGT heating is still being used for in-situ consolidation due to its lower capital costs and ease of implementation. With HGT there is also a reduced risk of damaging the matrix, compared to methods involving CO₂ and NIR laser, which can affect the interfacial adhesion through matrix burning. As can also be seen in Table 1, usually the laydown speeds of AFP with laser heating source are higher than those equipped with HGT systems.