Machine Learning for Object Recognition in Manufacturing Applications

被引:0
|
作者
Huitaek Yun
Eunseob Kim
Dong Min Kim
Hyung Wook Park
Martin Byung-Guk Jun
机构
[1] Purdue University,Indiana Manufacturing Competitiveness Center (IN
[2] Purdue University,MaC)
[3] Korea Institute of Industrial Technology,School of Mechanical Engineering
[4] Ulsan National Institute of Science and Technology,Dongnam Regional Division
关键词
Machine learning (ML); Manufacturability; Automated feature recognition (AFR); Object recognition;
D O I
暂无
中图分类号
学科分类号
摘要
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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页码:683 / 712
页数:29
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