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Smart defect detection using transfer learning in injection molding: a comparative exploration study of deep learning architectures

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Abstract

In the dynamic landscape of Industry 4.0, optimizing industrial processes like injection molding for enhanced efficiency and quality is paramount. This study endeavors to address this imperative of developing an automatic defect detection system, crucial for achieving intelligent process control and meeting evolving market demands while ensuring cost-effectiveness and sustainability. Utilizing transfer learning, this paper aims to explore and to verify the efficacy of three deep learning architectures—InceptionV2, ResNet50, and Inception-Resnet—unexplored in previous research on defect detection in the field of injection molding. The methodology adopted includes two essential steps to achieve the desired objective. The first step consists of training and testing the three RCNN architectures retained on a small data set after having determined the best values of three hyperparameters considered—learning rate, momentum, and number of iterations—allowing the obtaining of a better detection accuracy. The second step consists of improving the architecture of the best model obtained—here Inception v2—by using its last version v3, to consider and tune the values of additional hyperparameters—Solver and Bach size—and to use a training large dataset after adding other parts and proceeding with the different data augmentation techniques. Compared to the results obtained with the pre-trained models explored in previous studies such as MobileNet-SSD, VGG16, and YOLO, the results obtained during this study and particularly after the improvements undertaken are considered very satisfactory with an accuracy of 92%, a recall of 100%, and an F1 score of 90%. The results obtained during the test in the real world of production in the presence of external factors were very promising and revealed the importance of the luminosity factor as an influence on the precision and speed of detection.

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Data availability

The data used in this article is available on request (image dataset, python code, detection trained model, etc.).

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Author information

Authors and Affiliations

  1. Laboratory of Structural Engineering, Processes, Intelligent Systems and Informatique in ENSAM, Hassan 2 University, Casablanca, Morocco

    Mohamed EL Ghadoui & Ahmed Mouchtachi

  2. Laboratory of Complex Cyber Physical Systems in ENSAM, Hassan 2 University, Casablanca, Morocco

    Radouane Majdoul

Authors
  1. Mohamed EL Ghadoui
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  2. Ahmed Mouchtachi
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  3. Radouane Majdoul
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Contributions

This paper is mainly the work of the author Mr. EL Ghadoui Mohamed under the supervision of two other people who are the thesis director Mr. Mouchtachi Ahmed and his co-director Mr. Majdoul Radouane who participated in the supervision, orientation, discussion and revision of the work.

Corresponding author

Correspondence to Mohamed EL Ghadoui.

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The authors declare no competing interests.

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Appendix A1

Appendix A1

 

Main characteristics of the faster R-CNN architecture

Inception v2

1. Architecture type: faster R-CNN

2. Backbone feature extractor: Inception v2

3. Number of classes: 4 (indicating the number of classes the model is trained to detect)

4. Input image resizing: Resizes input images to keep aspect ratio with a minimum dimension of 600 pixels and a maximum dimension of 800 pixels

5. Anchor generation: Generates anchor boxes using grid anchor generator with specified scales and aspect ratios

6. Localization and objectness loss weights: Specifies weights for the localization and objectness loss in the first stage

7. NMS (non-maximum suppression) thresholds: Sets score and IoU thresholds for NMS during inference

8. Box predictor: Utilizes mask R-CNN box predictor with optional dropout and fully connected layers

9. Post-processing: Applies batch NMS and softmax score conversion for post-processing

10. Optimizer: Uses Adam optimizer with manual step learning rate scheduling and momentum optimization

11. Data augmentation: Includes random horizontal flip as a data augmentation option

12. Maximum number of boxes: Limits the maximum number of predicted boxes to 50 per image during training

13. Evaluation metrics: Uses COCO detection metrics for evaluation with 53 examples in the evaluation dataset

ResNet-50

1. Architecture type: Faster R-CNN

2. Backbone feature extractor: ResNet-50 (v1)

3. Number of classes: 4 (indicating the number of classes the model is trained to detect)

4. Input image resizing: Resizes input images to keep aspect ratio with a minimum dimension of 600 pixels and a maximum dimension of 800 pixels

5. Anchor generation: Generates anchor boxes using grid anchor generator with specified scales and aspect ratios

6. Localization and objectness loss weights: Specifies weights for the localization and objectness loss in the first stage

7. NMS (non-maximum suppression) thresholds: Sets score and IoU thresholds for NMS during inference

8. Box predictor: Utilizes mask R-CNN box predictor with optional dropout and fully connected layers

9. Post-processing: applies batch NMS and softmax score conversion for post-processing

10. Optimizer: Uses momentum optimizer with manual step learning rate scheduling and momentum optimization

11. Data augmentation: Includes random horizontal flip as a data augmentation option

12. Maximum number of boxes: Limits the maximum number of predicted boxes to 50 per image during training

13. Evaluation metrics: Uses COCO detection metrics for evaluation with 53 examples in the evaluation dataset

Inception ResNet v2

1. Architecture type: Faster R-CNN

2. Backbone feature extractor: Inception ResNet v2

3. Number of classes: 4 (indicating the number of classes the model is trained to detect)

4. Input image resizing: Resizes input images to keep aspect ratio with a minimum dimension of 400 pixels and a maximum dimension of 800 pixels

5. Anchor generation: Generates anchor boxes using grid anchor generator with specified scales and aspect ratios

6. Atrous rate: Specifies the atrous rate used in the first stage

7. Localization and objectness loss weights: Specifies weights for the localization and objectness loss in the first stage

8. NMS (non-maximum suppression) thresholds: Sets score and IoU thresholds for NMS during inference

9. Box predictor: Utilizes Mask R-CNN box predictor with optional dropout and fully connected layers

10. Post-processing: Applies batch NMS and softmax score conversion for post-processing

11. Optimizer: Uses momentum optimizer with manual step learning rate scheduling and momentum optimization

12. Data augmentation: Includes random horizontal flip as a data augmentation option

13. Maximum number of boxes: Limits the maximum number of predicted boxes to 50 per image during inference

14. Evaluation metrics: Uses COCO detection metrics for evaluation with 53 examples in the evaluation dataset

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EL Ghadoui, M., Mouchtachi, A. & Majdoul, R. Smart defect detection using transfer learning in injection molding: a comparative exploration study of deep learning architectures. Int J Adv Manuf Technol (2024). https://doi.org/10.1007/s00170-024-13768-5

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