Machine Learning for Object Recognition in Manufacturing Applications
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 remai...
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| Published in | International journal of precision engineering and manufacturing Vol. 24; no. 4; pp. 683 - 712 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Seoul
Korean Society for Precision Engineering
01.04.2023
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 2234-7593 2005-4602 |
| DOI | 10.1007/s12541-022-00764-6 |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Park, Hyung Wook Kim, Dong Min Jun, Martin Byung-Guk Yun, Huitaek Kim, Eunseob |
| Author_xml | – sequence: 1 givenname: Huitaek surname: Yun fullname: Yun, Huitaek organization: Indiana Manufacturing Competitiveness Center (IN-MaC), Purdue University – sequence: 2 givenname: Eunseob surname: Kim fullname: Kim, Eunseob organization: School of Mechanical Engineering, Purdue University – sequence: 3 givenname: Dong Min orcidid: 0000-0001-9303-7731 surname: Kim fullname: Kim, Dong Min email: dkim0707@kitech.re.kr organization: Dongnam Regional Division, Korea Institute of Industrial Technology – sequence: 4 givenname: Hyung Wook surname: Park fullname: Park, Hyung Wook organization: Department of Mechanical Engineering, Ulsan National Institute of Science and Technology – sequence: 5 givenname: Martin Byung-Guk surname: Jun fullname: Jun, Martin Byung-Guk email: mbgjun@purdue.edu organization: Indiana Manufacturing Competitiveness Center (IN-MaC), Purdue University, School of Mechanical Engineering, Purdue University |
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| SubjectTerms | 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 |
| Title | Machine Learning for Object Recognition in Manufacturing Applications |
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