Autoencoder-based defect detection in PVC profile manufacturing
by Ahmet Zahit Aslan; Sinan Onal
International Journal of Manufacturing Research (IJMR), Vol. 19, No. 2, 2024

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]

Online publication date: Thu, 01-Aug-2024

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.

Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Manufacturing Research (IJMR):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your password?

Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.

If you still need assistance, please email subs@inderscience.com