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Cited by 260Industrial Vision Systems and Defect DetectionAdvanced Neural Network ApplicationsNon-Destructive Testing Techniques

Artificial intelligence-based smart quality inspection for manufacturing

Open access
Sundaram, Sarvesh, Zeid, Abe·2023·Micromachines
DOI 10.3390/mi14030570PMC PMC10058274
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Abstract

In today’s era, monitoring the health of the manufacturing environment has become essential in order to prevent unforeseen repairs, shutdowns, and to be able to detect defective products that could incur big losses. Data-driven techniques and advancements in sensor technology with Internet of the Things (IoT) have made real-time tracking of systems a reality. The health of a product can also be continuously assessed throughout the manufacturing lifecycle by using Quality Control (QC) measures. Quality inspection is one of the critical processes in which the product is evaluated and deemed acceptable or rejected. The visual inspection or final inspection process involves a human operator sensorily examining the product to ascertain its status. However, there are several factors that impact the visual inspection process resulting in an overall inspection accuracy of around 80% in the industry. With the goal of 100% inspection in advanced manufacturing systems, manual visual inspection is both time-consuming and costly. Computer Vision (CV) based algorithms have helped in automating parts of the visual inspection process, but there are still unaddressed challenges. This paper presents an Artificial Intelligence (AI) based approach to the visual inspection process by using Deep Learning (DL). The approach includes a custom Convolutional Neural Network (CNN) for inspection and a computer application that can be deployed on the shop floor to make the inspection process user-friendly. The inspection accuracy for the proposed model is 99.86% on image data of casting products.

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Cited by

Sorted by citations

Authors
Sundaram, S., & Zeid, A.
Affiliations
Northeastern University (US)
Corresponding author
Zeid, Abe
ORCID
Venue
Micromachines
Publisher
MDPI AG
Published
2023-02-27
Volume
14
Issue
3
Pages
570
ISSN
2072-666X (ISSN-L)
DOI
10.3390/mi14030570
PMC
PMC10058274
OpenAlex
W4322619914
Cited by
260
References
1
Citations by year
2026: 88 · 2025: 101 · 2024: 54 · 2023: 17
FWCI
31.07
Type
journal article
Language
en
Open access
Licence
Locations
  • Micromachines (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
Keywords
automated visual inspection, quality inspection, convolutional neural networks, defect detection, manufacturing quality control, Internet of Things, real-time monitoring

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