Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning
Quality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible d...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-01-01
|
| Series: | Journal of Sensor and Actuator Networks |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2224-2708/10/1/7 |
| _version_ | 1797406507273289728 |
|---|---|
| author | Tajeddine Benbarrad Marouane Salhaoui Soukaina Bakhat Kenitar Mounir Arioua |
| author_facet | Tajeddine Benbarrad Marouane Salhaoui Soukaina Bakhat Kenitar Mounir Arioua |
| author_sort | Tajeddine Benbarrad |
| collection | DOAJ |
| description | Quality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible data by vision equipment will be used to identify and report defective products, understand the causes of deficiencies and allow rapid and efficient intervention in smart factories. From this perspective, the proposed machine vision model in this paper combines the identification of defective products and the continuous improvement of manufacturing processes by predicting the most suitable parameters of production processes to obtain a defect-free item. The suggested model exploits all generated data by various integrated technologies in the manufacturing chain, thus meeting the requirements of quality management in the context of Industry 4.0, based on predictive analysis to identify patterns in data and suggest corrective actions to ensure product quality. In addition, a comparative study between several machine learning algorithms, both for product classification and process improvement models, is performed in order to evaluate the designed system. The results of this study show that the proposed model largely meets the requirements for the proper implementation of these techniques. |
| first_indexed | 2024-03-09T03:26:31Z |
| format | Article |
| id | doaj.art-7ea86597b57d42789a7f270536493fc7 |
| institution | Directory Open Access Journal |
| issn | 2224-2708 |
| language | English |
| last_indexed | 2024-03-09T03:26:31Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Sensor and Actuator Networks |
| spelling | doaj.art-7ea86597b57d42789a7f270536493fc72023-12-03T15:01:20ZengMDPI AGJournal of Sensor and Actuator Networks2224-27082021-01-01101710.3390/jsan10010007Intelligent Machine Vision Model for Defective Product Inspection Based on Machine LearningTajeddine Benbarrad0Marouane Salhaoui1Soukaina Bakhat Kenitar2Mounir Arioua3Laboratory of Information and Communication Technologies (LabTIC), National School of Applied Sciences, Abdelmalek Essaadi University, ENSA of Tangier, Route Ziaten, BP 1818 Tangier, MoroccoLaboratory of Information and Communication Technologies (LabTIC), National School of Applied Sciences, Abdelmalek Essaadi University, ENSA of Tangier, Route Ziaten, BP 1818 Tangier, MoroccoLaboratory of Information and Communication Technologies (LabTIC), National School of Applied Sciences, Abdelmalek Essaadi University, ENSA of Tangier, Route Ziaten, BP 1818 Tangier, MoroccoLaboratory of Information and Communication Technologies (LabTIC), National School of Applied Sciences, Abdelmalek Essaadi University, ENSA of Tangier, Route Ziaten, BP 1818 Tangier, MoroccoQuality control is one of the industrial tasks most susceptible to be improved by implementing technological innovations. As an innovative technology, machine vision enables reliable and fast 24/7 inspections and helps producers to improve the efficiency of manufacturing operations. The accessible data by vision equipment will be used to identify and report defective products, understand the causes of deficiencies and allow rapid and efficient intervention in smart factories. From this perspective, the proposed machine vision model in this paper combines the identification of defective products and the continuous improvement of manufacturing processes by predicting the most suitable parameters of production processes to obtain a defect-free item. The suggested model exploits all generated data by various integrated technologies in the manufacturing chain, thus meeting the requirements of quality management in the context of Industry 4.0, based on predictive analysis to identify patterns in data and suggest corrective actions to ensure product quality. In addition, a comparative study between several machine learning algorithms, both for product classification and process improvement models, is performed in order to evaluate the designed system. The results of this study show that the proposed model largely meets the requirements for the proper implementation of these techniques.https://www.mdpi.com/2224-2708/10/1/7Industry 4.0quality controlIoTartificial intelligencemachine learningmachine vision |
| spellingShingle | Tajeddine Benbarrad Marouane Salhaoui Soukaina Bakhat Kenitar Mounir Arioua Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning Journal of Sensor and Actuator Networks Industry 4.0 quality control IoT artificial intelligence machine learning machine vision |
| title | Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning |
| title_full | Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning |
| title_fullStr | Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning |
| title_full_unstemmed | Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning |
| title_short | Intelligent Machine Vision Model for Defective Product Inspection Based on Machine Learning |
| title_sort | intelligent machine vision model for defective product inspection based on machine learning |
| topic | Industry 4.0 quality control IoT artificial intelligence machine learning machine vision |
| url | https://www.mdpi.com/2224-2708/10/1/7 |
| work_keys_str_mv | AT tajeddinebenbarrad intelligentmachinevisionmodelfordefectiveproductinspectionbasedonmachinelearning AT marouanesalhaoui intelligentmachinevisionmodelfordefectiveproductinspectionbasedonmachinelearning AT soukainabakhatkenitar intelligentmachinevisionmodelfordefectiveproductinspectionbasedonmachinelearning AT mounirarioua intelligentmachinevisionmodelfordefectiveproductinspectionbasedonmachinelearning |