Quality Classification of Defective Parts from Injection Moulding

2020/07/08 by Adithya Venkatadri Hulagadri, Hulagadri, Adithya Venkatadri
Computer Science · Engineering · #Advanced Neural Network Applications #FOS: Computer and information sciences #Industrial Vision Systems and Defect Detection #Injection Molding Process and Properties #Other Computer Science (cs.OH) #cs.OH

paper · pdf · doi:10.48550/arxiv.2008.02872

4 pages, 11 figures

arxiv created 2020/07/08 · openalex publication_date 2020/07/08 · arxiv updated 2020/08/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This report examines machine learning algorithms for detecting short forming and weaving in plastic parts produced by injection moulding. Transfer learning was implemented by using pretrained models and finetuning them on our dataset of 494 samples of 150 by 150 pixels images. The models tested were Xception, InceptionV3 and Resnet-50. Xception showed the highest overall accuracy (86.66%), followed by InceptionV3 (82.47%) and Resnet-50 (80.41%). Short forming was the easiest fault to identify, with the highest F1 score for each model.

Citations

• ImageNet Large Scale Visual Recognition Challenge

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