A Novel Method of Fault Diagnosis for Injection Molding Systems Based on Improved VGG16 and Machine Vision

Zhicheng Hu, Zhengjie Yin, Ling Qin () and Fengxiang Xu
Additional contact information
Zhicheng Hu: School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan 430074, China
Zhengjie Yin: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Ling Qin: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China
Fengxiang Xu: School of Automotive Engineering, Wuhan University of Technology, Wuhan 430070, China

Sustainability, 2022, vol. 14, issue 21, 1-26

Abstract: Artificial intelligence technology has enabled the manufacturing industry and actively guided its transformation and promotion for the past few decades. Injection molding technology is a crucial procedure in mechanical engineering and manufacturing due to its adaptability and dimensional stability. An essential step in the injection molding process is quality inspection and manual visual inspection is still used in conventional quality control, but this open-loop working method has issues with subjectivity and real-time monitoring capacity. This paper proposes an integrated “processing–matching–classification–diagnosis” concept based on machine vision and deep learning that allows for efficient and intelligent diagnosis of injection molding in complex scenarios. Based on eight categories of failure images of plastic components, this paper summarizes the theoretical method of processing fault categorization and identifies the various causes of defects from injection machines and molds. A template matching mechanism based on a new concept—arbitration function J ψ i j —provided in this paper, matches the edge features to achieve the initial classification of plastic components images. A conventional VGG16 network is innovatively upgraded in this work in order to further classify the unqualified plastic components. The classification accuracy of this improved VGG16 reaches 96.67%, which is better than the 53.33% of the traditional network. The accuracy, responsiveness, and resilience of the quality inspection are all improved in this paper. This work enhances production safety while promoting automation and intelligence of fault diagnosis in injection molding systems. Similar technical routes can be generalized to other industrial scenarios for quality inspection problems.

Keywords: deep learning; machine vision; fault diagnosis; improved VGG16; injection molding (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/21/14280/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/21/14280/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:21:p:14280-:d:960253

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().