DocumentCode
2030077
Title
Neural and machine learning to the surface defect investigation in sheet metal forming
Author
Wu, Xiaodan ; Wang, Jianwen ; Flitman, Andrew ; Thomson, Peter
Author_Institution
Sch. of Bus. Syst., Monash Univ., Clayton, Vic., Australia
Volume
3
fYear
1999
fDate
1999
Firstpage
1088
Abstract
Surface defects such as wrinkling and buckling are a serious quality problem in the sheet metal-forming industry. This paper presents using information processing techniques (artificial neural networks and machine learning approaches) to study the geometrical influence on the formation of wrinkling for automobile components
Keywords
automobile industry; buckling; forming processes; learning (artificial intelligence); mechanical engineering computing; metallurgical industries; neural nets; production engineering computing; surface phenomena; artificial neural networks; automobile components; buckling; geometrical influence; information processing techniques; machine learning; quality; sheet metal forming; surface defects; wrinkling; Artificial neural networks; Automobile manufacture; Data engineering; Inorganic materials; Machine learning; Manufacturing industries; Metals industry; Sheet materials; Solid modeling; Springs;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on
Conference_Location
Perth, WA
Print_ISBN
0-7803-5871-6
Type
conf
DOI
10.1109/ICONIP.1999.844687
Filename
844687
Link To Document