Abstract
In the manufacturing of critical components of a product, it is important to ascertain the performance and behaviour of those components being produced before assembly. Frequently, these part components are subject to stringent acceptance tests in order to confirm their conformance to the required specifications. Such acceptance tests are normally monotonous and tedious. At times, they may be costly to carry out and may affect the cycle time of production. This work proposes an approach that is based on genetic algorithms and rough set theory to uncover the characteristics of the part components in relation to their performance using past acceptance test data, that is, the historical data. Such characteristics are described in terms of decision rules. By examining the characteristics exhibited, it may be possible to relax the rigour of acceptance tests. A case study was used to illustrate the proposed approach. It was found that the cost in conducting the acceptance tests and the production cycle time could be reduced remarkably without compromising the overall specifications of the acceptance tests.KeywordsDecision RuleAcceptance TestInformation TableRule PrunerRedundant AttributeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.