{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/9407"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/9407","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"Quantitative selection of inspection plans for variation risk management","abstract":"Over the last decade, the importance of quality has increased significantly. Quality improvement efforts involve mitigating the impact of manufacturing variation through robust design, statistical process control (SPC), and inspection. This thesis focuses on the last: how to choose an inspection plan to remove the most variation at the lowest cost. The optimal inspection plan balances the cost of inspection and rework against the cost of increased quality. This thesis describes an empirical analysis and prototype software that employs Monte Carlo simulation and simulated annealing to identify the optimal inspection plan. 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