Title: Autoencoder-based defect detection in PVC profile manufacturing

Authors: Ahmet Zahit Aslan; Sinan Onal

Addresses: Department of Industrial Engineering, Southern Illinois University, Edwardsville, 61 Harping Dr. Box 1805, Edwardsville, IL 62026, USA ' Department of Industrial Engineering, Southern Illinois University, Edwardsville, 61 Harping Dr. Box 1805, Edwardsville, IL 62026, USA

Abstract: This study develops an automatic defect detection system for polyvinyl chloride (PVC) profile manufacturing, addressing inefficiencies in manual inspection. It compares the proposed autoencoder model with other well-known unsupervised deep-learning methods, including GANomaly, f-AnoGAN, and the student-teacher network, for defect detection during extrusion. Utilising a defective PVC profile dataset, the study generates anomaly heat maps through reconstruction errors and assesses model performance using the area under the receiver operating characteristic (ROC) curve. The proposed autoencoder model is found to be optimal for this dataset, offering a balance between efficiency and accuracy. These findings have significant implications for enhancing quality control and reducing defects in PVC manufacturing, with potential applicability in other industrial settings. [Submitted 18 December 2023; Accepted 22 May 2024]

Keywords: automated defect detection; polyvinyl chloride; PVC; profiles; unsupervised deep learning models; autoencoder; quality control in manufacturing.

DOI: 10.1504/IJMR.2024.140291

International Journal of Manufacturing Research, 2024 Vol.19 No.2, pp.119 - 144

Received: 18 Dec 2023
Accepted: 22 May 2024
Published online: 01 Aug 2024 *

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