Fixture Failure Diagnosis for Autobody Assembly Using Pattern Recognition

Author:

Ceglarek D.1,Shi J.1

Affiliation:

1. S. M. Wu Manufacturing Research Center, Department of Mechanical Engineering and Applied Mechanics, The University of Michigan, Ann Arbor, MI 48109-2125

Abstract

In this paper, a fault diagnostic method is proposed for autobody assembly fixtures. This method uses measurement data to detect and isolate dimensional faults of part caused by fixture. The proposed method includes a predetermined variation pattern model and a fault mapping procedure. The variation pattern model is based on CAD information about the fixture geometry and location of the measurement points. This fault mapping procedure combines Principal Component Analysis with pattern recognition approach. Simulations and one case study illustrate the proposed method.

Publisher

ASME International

Subject

General Medicine

Reference23 articles.

1. ABC, 1993, “Variation Reduction for Automotive Body Assembly,” Annual Report for Advanced Technology Program (NIST), Autobody Consortium (ABC) and University of Michigan, Ann Arbor.

2. Asada H. , and ByA., 1985, “Kinematic Analysis of Workpart Fixturing for Flexible Assembly with Automatically Reconfigurable Fixtures,” IEEE Journal of Robotics and Automation, Vol. RA-1, No. 2, pp. 86–94.

3. Ceglarek, D., 1994, “Knowledge-Based Diagnosis for Automotive Body Assembly: Methodology and Implementation,” Ph.D. Dissertation, University of Michigan, Ann Arbor.

4. Ceglarek D. , ShiJ., 1995, “Dimensional Variation Reduction for Automotive Body Assembly,” Manufacturing Review, Vol. 8, No. 2, pp. 139–154.

5. Ceglarek D. , ShiJ., and WuS. M., 1994, “A Knowledge-based Diagnosis Approach for the Launch of the Auto-body Assembly Process,” ASME JOURNAL OF ENGINEERING FOR INDUSTRY, Vol. 116, No. 3, pp. 491–499.

Cited by 176 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3