Ângela Semitela, André Ferreira, António Completo, Nuno Lau, José P Santos
{"title":"对热水器表面的缺陷进行检测和分类:开发自动化系统","authors":"Ângela Semitela, André Ferreira, António Completo, Nuno Lau, José P Santos","doi":"10.1177/09544089241262945","DOIUrl":null,"url":null,"abstract":"Seeking a total automation of the existing industrial processes, manual product quality control systems have been gradually replaced by automated ones, to significantly improve efficiency and speed, and ultimately, increase industrial productivity. In this regard, an automated inspection system was developed in this work to detect and classify defects on the painted surfaces of Bosch Thermotechnology water heaters. This system comprised a deflectometry-based image acquisition module, two light deep learning models built and trained from scratch for defect detection and classification in the painted surfaces and a visual interface. The experimental results confirmed that: (1) deflectometry techniques were crucial for an accurate defect detection; (2) the two lightweight models – for detection and classification – rapidly achieved high accuracies, even in the testing stage, demonstrating their high performance regardless of their small size; (3) the developed system was able to correctly and quickly predict the status of a painted surface, and then successfully send this status information to a user-friendly visual interface, validating its suitability for an industrial setting. Overall, this system demonstrated great potential as a suitable alternative to the existing manual inspection of the painted surfaces of Bosch Thermotechnology water heaters.","PeriodicalId":20552,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering","volume":"36 1","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Detecting and classifying defects on the surface of water heaters: Development of an automated system\",\"authors\":\"Ângela Semitela, André Ferreira, António Completo, Nuno Lau, José P Santos\",\"doi\":\"10.1177/09544089241262945\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Seeking a total automation of the existing industrial processes, manual product quality control systems have been gradually replaced by automated ones, to significantly improve efficiency and speed, and ultimately, increase industrial productivity. In this regard, an automated inspection system was developed in this work to detect and classify defects on the painted surfaces of Bosch Thermotechnology water heaters. This system comprised a deflectometry-based image acquisition module, two light deep learning models built and trained from scratch for defect detection and classification in the painted surfaces and a visual interface. The experimental results confirmed that: (1) deflectometry techniques were crucial for an accurate defect detection; (2) the two lightweight models – for detection and classification – rapidly achieved high accuracies, even in the testing stage, demonstrating their high performance regardless of their small size; (3) the developed system was able to correctly and quickly predict the status of a painted surface, and then successfully send this status information to a user-friendly visual interface, validating its suitability for an industrial setting. Overall, this system demonstrated great potential as a suitable alternative to the existing manual inspection of the painted surfaces of Bosch Thermotechnology water heaters.\",\"PeriodicalId\":20552,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering\",\"volume\":\"36 1\",\"pages\":\"\"},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-08-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/09544089241262945\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09544089241262945","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Detecting and classifying defects on the surface of water heaters: Development of an automated system
Seeking a total automation of the existing industrial processes, manual product quality control systems have been gradually replaced by automated ones, to significantly improve efficiency and speed, and ultimately, increase industrial productivity. In this regard, an automated inspection system was developed in this work to detect and classify defects on the painted surfaces of Bosch Thermotechnology water heaters. This system comprised a deflectometry-based image acquisition module, two light deep learning models built and trained from scratch for defect detection and classification in the painted surfaces and a visual interface. The experimental results confirmed that: (1) deflectometry techniques were crucial for an accurate defect detection; (2) the two lightweight models – for detection and classification – rapidly achieved high accuracies, even in the testing stage, demonstrating their high performance regardless of their small size; (3) the developed system was able to correctly and quickly predict the status of a painted surface, and then successfully send this status information to a user-friendly visual interface, validating its suitability for an industrial setting. Overall, this system demonstrated great potential as a suitable alternative to the existing manual inspection of the painted surfaces of Bosch Thermotechnology water heaters.
期刊介绍:
The Journal of Process Mechanical Engineering publishes high-quality, peer-reviewed papers covering a broad area of mechanical engineering activities associated with the design and operation of process equipment.