Machine Learning for Object Recognition in Manufacturing Applications

• Published in: International journal of precision engineering and manufacturing Vol. 24; no. 4; pp. 683 - 712
• Main Authors: Yun, Huitaek, Kim, Eunseob, Kim, Dong Min, Park, Hyung Wook, Jun, Martin Byung-Guk
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society for Precision Engineering 01.04.2023; Springer Nature B.V
• Subjects: Algorithms; Business competition; CAD; Computer aided design; Decision making; Engineering; Feature recognition; Industrial and Production Engineering; Machine learning; Manufacturability; Manufacturing; Materials Science; Object recognition; Process planning; Review; Automated feature recognition (AFR); Machine learning (ML); Manufacturability; Object recognition
• ISSN: 2234-7593; 2005-4602
• DOI: 10.1007/s12541-022-00764-6

Feature recognition and manufacturability analysis from computer-aided design (CAD) models are indispensable technologies for better decision making in manufacturing processes. It is important to transform the knowledge embedded within a CAD model to manufacturing instructions for companies to remain competitive as experienced baby-boomer experts are going to retire. Automatic feature recognition and computer-aided process planning have a long history in research, and recent developments regarding algorithms and computing power are bringing machine learning (ML) capability within reach of manufacturers. Feature recognition using ML has emerged as an alternative to conventional methods. This study reviews ML techniques to recognize objects, features, and construct process plans. It describes the potential for ML in object or feature recognition and offers insight into its implementation in various smart manufacturing applications. The study describes ML methods frequently used in manufacturing, with a brief introduction of underlying principles. After a review of conventional object recognition methods, the study discusses recent studies and outlooks on feature recognition and manufacturability analysis using ML.