# Intelligent Warping Detection for Fused Filament Fabrication of a Metal-Polymer Composite Filament

Jungyoon Moon, Kijung Park, Sangin Park

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Jungyoon Moon, Kijung Park, Sangin Park. Intelligent Warping Detection for Fused Filament Fabrication of a Metal-Polymer Composite Filament. IFIP International Conference on Advances in Production Management Systems (APMS), Sep 2022, Gyeongju, South Korea. pp.267-273, 10.1007/978-3-031-16407-1_32. hal-05386063

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Intelligent Warping Detection for Fused
Filament Fabrication of a Metal-Polymer
Composite Filament

Jungyoon Moon<fnref n="1" /> ID, Kijung Park<fnref n="1" />(✉) ID, and Sangin Park<fnref n="2" /> ID

**Abstract.** Fused Filament Fabrication (FFF) for a metal-polymer composite filament is receiving attention as an effective means to manufacture complex metal parts. However, warping deformation is one of the main problems that reduce part quality in the FFF of a metal-polymer composite filament. In this regard, an appropriate quality monitoring system for the warping deformation of FFF is essential to effectively manage the quality of metal-polymer composite 3D printing outputs. This study presents an intelligent warping detection process for FFF using a metal-polymer composite filament through vision monitoring and automatic object detection. First, five FFF samples are manufactured using the Ultrafuse-316L metal-polymer composite filament. Each fabrication process is recorded by a camera to construct an image dataset for warping deformation. Then, the YOLO v5 algorithm is applied to a training set (80%) of the image dataset to learn labeled FFF output and warping deformation areas. The constructed object detection model for warping deformation is tested using a validation set (20%) of the original image dataset. This study provides a basis to develop an intelligent quality management system for the FFF of a metal-polymer composite filament.

**Keywords:** Object detection · Fused Filament Fabrication · Metal-polymer composite · Warping · Image mining

# 1 Introduction

Additive manufacturing (AM) has received increasing attention as a potential tool for cost-effectiveness and sustainable manufacturing [1]. The Fused Filament Fabrication (FFF) method is an AM technique to extrude a polymer filament at a high temperature and to deposit the molten material layer by layer [2]. FFF is one of the popular AM methods due to its compatibility with various materials [2]. In particular, the recent advance of FFF to use metal-polymer composite filaments provides a new opportunity to fabricate

<footnote n="1">¹ Department of Industrial and Management Engineering, Incheon National University,
Incheon, Republic of Korea
{mjy,kjpark}@inu.ac.kr</footnote>

<footnote n="2">² Department of Mechatronics Engineering, Incheon National University, Incheon,
Republic of Korea
sangin.park@inu.ac.kr</footnote>
complex metal parts with affordable manufacturing cost [3]. Nowadays various metal applications such as metal tooling parts and prototypes are easily manufactured by FFF for metal-polymer composite materials [4].

Warping deformation is a common error type for FFF that occurs due to material shrinkage during an FFF process and results in bending distortion of a fabricated output [5]. Since the warping deformation of metal-polymer composite outputs significantly reduces part quality after post-processing (i.e., sintering process) for the outputs, detecting warping deformation during an early stage of FFF is essential to avoid unnecessary material consumption and printing time taken for final outputs with warping defects. Previous studies relevant to reducing warping deformation during FFF mainly focused on the impact of FFF process parameters that cause warping deformation [6–8]. On the other hand, recent studies relevant to the warping deformation of FFF have attempted to develop an intelligent process through data or image mining to detect warping deformation of FFF outputs. For example, Li et al. [9] proposed a back-propagation neural network (BPNN) model to diagnose warping deformation based on the vibration data of an FFF machine. Saluja et al. [10] developed a closed-loop system to identify warping deformation in an FFF process through a convolutional neural network (CNN) model.

Recently, the YOLO (You Look Only Once) object detection algorithm has received a great deal of attention in defect detection and quality monitoring due to its successful detection performance for various application areas [11–13]. Defect detection based on an object detection model can be applied to AM to provide useful information of defects during an AM process such as defect location and size of a being fabricated object. Paying attention to the aforementioned issues, this study employs the YOLO v5 object detection algorithm [14] to identify warping deformation during FFF using a metal-polymer composite filament.

# 2 Methods

## 2.1 Experimental Settings

Pinter223 [15], an open-source FFF machine, was remodeled to fabricate a metal-polymer composite filament. Ultrafuse-316L [4], which is a popular commercial metal-polymer composite filament for FFF, was used for experiments. For data collection, the GoPro Hero9 [16] camera was positioned at the front side of the FFF machine to record the fabrication process of each experimental sample (see Fig. 1). The part design illustrated in Fig. 2 was adopted to fabricate five samples through FFF.

## 2.2 Data Collection and Labeling

The recoded videos of the whole FFF process for each sample were transformed to an image dataset for a warping detection model. The image dataset was extracted by capturing one image frame per second from the fabrication process video of each sample. As a result, a total of 7,029 warping images were collected for a supervised learning-based warping detection model. Furthermore, the region of interest (ROI) method [17] was employed to resize 2407 × 1520 (pixels) original images to 640 × 640 (pixels) images for model training.
<figure><img src="image_3.png" /><figcaption>Fig. 1. Machine settings to record fabricated outputs during FFF</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Design of a sample and its fabricated output with a brim</figcaption></figure>

Next, data labeling was conducted to train a warping detection model based on YOLO v5 [14]. YOLO v5 requires a label per image in a YOLO format, which consists of the center coordinates of bounding boxes (x, y), width (w), and height (h). To identify the whole fabrication and warping areas, the number of classes for bounding boxes was set to two (i.e., “part” and “warp”) for the YOLO v5 model. Figure 3(b) illustrates these two target detection areas (i.e., the green bounding box for “part” class and the red bounding boxes for the “warp” class). In order to input image data with predefined labels into YOLO v5, this study conducted label annotation for the YOLO format.

## 2.3 Training and Validation of Warping Detection Model

The constructed image dataset was randomly separated into a training set (80% of the dataset) and a validation set (20% of the dataset). The training and validation sets consisted of 5,623 and 1,406 images, respectively. A training model for warping detection was built based on the YOLO v5s model, which shows the fastest data learning process of all available YOLO v5 model variants. Moreover, the transfer learning method [18] was adopted to reduce training time and to improve detection performance. The pre-trained weight obtained by transfer learning was trained by the COCO dataset [19], which is a popular benchmark dataset in the computer vision field. The hyper-parameter of YOLO v5s was set to the default settings in the YOLO v5 library [11], including 640 image sizes in pixels, 64 batch sizes, and 100 epochs.
<figure><img src="image_5.png" /><figcaption>Fig. 3. ROI resizing and labeling process</figcaption></figure>

The following precision, recall, and mean average precision (mAP) [20] measures were employed to evaluate the performance of the derived warping detection model. These measures were calculated based on intersection over union (IoU) [21], which is a criterion of judging true or false, in Eq. (1).

$$IoU = \frac{|Ground\ truth \cap Prediction|}{|Ground\ truth \cup Prediction|}, \qquad (1)$$

where the ground-truth (Ground truth) is an area of the labeled bounding boxes of trained images, and the prediction (Prediction) is an area of the predicted bounding boxes of trained images. Through this IoU values, the true positive (TP), false positive (FP), and false negative (FN) were determined for Precision and Recall [22] in Eqs. (2-3).

$$Precision = \frac{TP}{TP + FP} \qquad (2)$$

In Eq. (2), the precision measure is defined as a ratio between correctly recognized true positives and all predicted positives. A high precision value indicates that the object detection model can precisely predict object locations and areas.

$$Recall = \frac{TP}{TP + FN} \qquad (3)$$

In Eq. (3), the recall measure is defined as a ratio between correctly recognized positive prediction and all ground-truth of positives and negatives through the model. A high recall value means that the object detection model is close to the ground-truth of object locations and areas.

$$mAP = \frac{1}{c} \sum_{i=1}^{c} AP_{i}, \qquad (4)$$

where AP is calculated as a gap between Precision and Recall, and c is the number of label types. The mAP is categorized into two types: mAP<sub>0.5</sub> (IoU criterion at 0.5) and mAP<sub>0.5:0.95</sub> (averaged over IoU criterion in a range from 0.5 to 0.95).
## 3 Results

Figure 4 shows the originally labeled areas and the detected warping areas obtained by the warping detection model using the validation dataset. The performance of the warping detection model is shown in Table 1. The total recall, precision, mAP<sub>0.5</sub> and mAP<sub>0.5:0.95</sub> of the warping detection model in all the classes were achieved at 0.99, 0.989, 0.992, and 0.785, respectively.

The generated warping detection model can detect warping deformation along with a printing part close to labels and bounding boxes in the validation dataset. However, the gap between mAP<sub>0.5</sub> and mAP<sub>0.5:0.95</sub> for “Warp” indicates this gap may be occurred because the bounding boxes of the “Warp” label are too small to detect precisely. Nevertheless, this issue seems not to largely affect to the visualized results of the warping detection model as shown in Fig. 4.

<figure><img src="image_6.png" /><figcaption>Fig. 4. Comparison of ground-truth (a) and prediction (b) in validation dataset</figcaption></figure>

<table><caption>Table 1. Performance of warping detection model</caption><thead><tr><td>Classes</td><td>Recall</td><td>Precision</td><td>mAP<sub>0.5</sub></td><td>mAP<sub>0.5:0.95</sub></td></tr></thead><tbody><tr><td>Warp</td><td>0.98</td><td>0.981</td><td>0.990</td><td>0.627</td></tr><tr><td>Part</td><td>0.99</td><td>0.998</td><td>0.995</td><td>0.942</td></tr><tr><td>Total</td><td>0.99</td><td>0.989</td><td>0.992</td><td>0.785</td></tr></tbody></table>
# 4 Conclusions and Discussion

This study applied an objective detection algorithm to intelligently identify warping deformation during a FFF process using a metal-polymer composite filament. First, five FFF samples using Ultrafuse-316L were printed to record their FFF process. An image dataset was constructed from the recorded fabrication process of each sample. To train a warping detection model, the image dataset was labeled by the YOLO format. Next, the warping detection model based on YOLO v5 was trained to identify a warping area along with a sample part. The derived warping detection model showed the overall good performance in the model evaluation measures.

This study would serve as a basis for an intelligent quality management system for AM. For future work, an extended model of warping detection for FFF will be studied to achieve a dynamic quality management system through automatic vision monitoring. More detailed information of warping deformation during FFF will be detected and displayed by an extended warping detection model to facilitate decision making for AM operations.

**Acknowledgement.** This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. NRF-2022R1C1C1012140) for Kijung Park.

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