Artificial Intelligence-Based Smart Quality Inspection for Manufacturing
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 Th...
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
|
| Series: | Micromachines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-666X/14/3/570 |
| _version_ | 1827748561594351616 |
|---|---|
| author | Sarvesh Sundaram Abe Zeid |
| author_facet | Sarvesh Sundaram Abe Zeid |
| author_sort | Sarvesh Sundaram |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T06:10:15Z |
| format | Article |
| id | doaj.art-6cac68ff763f43e5969c48ad704633cf |
| institution | Directory Open Access Journal |
| issn | 2072-666X |
| language | English |
| last_indexed | 2024-03-11T06:10:15Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Micromachines |
| spelling | doaj.art-6cac68ff763f43e5969c48ad704633cf2023-11-17T12:42:40ZengMDPI AGMicromachines2072-666X2023-02-0114357010.3390/mi14030570Artificial Intelligence-Based Smart Quality Inspection for ManufacturingSarvesh Sundaram0Abe Zeid1College of Engineering, Northeastern University, Boston, MA 02135, USACollege of Engineering, Northeastern University, Boston, MA 02135, USAIn 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.https://www.mdpi.com/2072-666X/14/3/570artificial intelligencedeep learningquality controlvisual inspectionindustry 4.0smart manufacturing |
| spellingShingle | Sarvesh Sundaram Abe Zeid Artificial Intelligence-Based Smart Quality Inspection for Manufacturing Micromachines artificial intelligence deep learning quality control visual inspection industry 4.0 smart manufacturing |
| title | Artificial Intelligence-Based Smart Quality Inspection for Manufacturing |
| title_full | Artificial Intelligence-Based Smart Quality Inspection for Manufacturing |
| title_fullStr | Artificial Intelligence-Based Smart Quality Inspection for Manufacturing |
| title_full_unstemmed | Artificial Intelligence-Based Smart Quality Inspection for Manufacturing |
| title_short | Artificial Intelligence-Based Smart Quality Inspection for Manufacturing |
| title_sort | artificial intelligence based smart quality inspection for manufacturing |
| topic | artificial intelligence deep learning quality control visual inspection industry 4.0 smart manufacturing |
| url | https://www.mdpi.com/2072-666X/14/3/570 |
| work_keys_str_mv | AT sarveshsundaram artificialintelligencebasedsmartqualityinspectionformanufacturing AT abezeid artificialintelligencebasedsmartqualityinspectionformanufacturing |