A Systematic Study of the Prediction Model for Operator-Induced Assembly Defects Based on Assembly Complexity Factors
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Su-2010-A Systematic Study o.pdf
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1.75 MB
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Checksum (MD5)
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Author(s)
Lei, Liu
Qiang, Su
Whitney, Daniel E.
Date Issued
December 2009
Journal
IEEE Transactions on Systems, Man and Cybernetics, Part A: Systems and Humans
Publisher
Institute of Electrical and Electronics Engineers
Citation
Qiang Su, Lei Liu, and Daniel E. Whitney. “A Systematic Study of the Prediction Model for Operator-Induced Assembly Defects Based on Assembly Complexity Factors.” Systems, Man and Cybernetics, Part A: Systems and Humans, IEEE Transactions on 40.1 (2010): 107-120. © 2009 Institute of Electrical and Electronics Engineers.
Version
Final published version
Abstract
It is a common view that the assembly process heavily affects a product's final quality and cost. The continuously shortening product life cycle requires a faster response speed as well as a lower defect rate in assembly production. In this situation, assembly quality control is becoming one of the most demanding problems in the modern manufacturing environment. The main causes of assembly defects can be classified into four categories, i.e., improper design, defective part, variance in assembly system, and operator error. The first three categories have been studied for many decades. However, elements of the operator error have not been fully explored. In this paper, using a copier assembly as an example, the problem of assembly defects caused by mistakes of operators is investigated systematically. A novel defect-rate prediction model is derived from the study of two complexity factors, namely, the design-based assembly complexity factor and the process-based assembly complexity factor, which are defined according to the structure and production characteristics of the copier. Several case studies consistently demonstrate that the new prediction model is accurate and stable for evaluating the copier assembly quality. Moreover, another case study offered in this paper demonstrates that the prediction model can provide effective assistance in the improvement of assembly quality.
Subjects
quality control
design for assembly (DFA)
assembly
MIT Department
Massachusetts Institute of Technology. Center for Technology, Policy, and Industrial Development
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
http://dx.doi.org/10.1109/tsmca.2009.2033030
