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The quality of a product is dependent on both facilities/equipment and manufacturing processes.Any error or disorder in facilities and processes can cause a catastrophic failure.To avoid such failures,a zero-defect manufacturing(ZDM)system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products.One of the major challenges for ZDM is the analysis of massive raw datasets.This type of analysis needs an automated and self-organized decision making system.Data mining(DM)is an effective methodology for discovering interesting knowledge within a huge datasets.It plays an important role in developing a ZDM system.The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect.This paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.
The quality of a product is dependent on both facilities / equipment and manufacturing processes. Any error or disorder in facilities and processes can cause a catastrophic failure. To avoid such failures, a zero-defect manufacturing (ZDM) system is necessary in order to increase the reliability and safety of manufacturing systems and reach zero-defect quality of products. One of the major challenges for ZDM is the analysis of massive raw datasets. This type of analysis needs an automated and self-organized decision making system. Data mining (DM ) is an effective methodology for discovering interesting knowledge within a huge datasets. It plays an important role in developing a ZDM system. The paper presents a general framework of ZDM and explains how to apply DM approaches to manufacture the products with zero-defect. paper also discusses 3 ongoing projects demonstrating the practice of using DM approaches for reaching the goal of ZDM.