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• W4220759116 abstract "This paper proposes data-analytics-based factory operation strategies for the quality enhancement of die casting. We first define the four main problems of die casting that result in lower quality: [P1] gaps between the input and output casting parameter values, [P2] occurrence of preheat shots, [P3] lateness of defect distinction, and [P4] worker-experience-based casting parameter tuning. To address these four problems, we derived seven tasks that should be conducted during factory operation: [T1] implementation of exploratory data analysis (EDA) for investigating the trends and correlations between data, [T2] deduction of the optimal casting parameter output values for the production of fair-quality products, [T3] deduction of the upper and lower control limits for casting parameter input–output gap management, [T4] development of a preheat shot diagnosis algorithm, [T5] development of a defect prediction algorithm, [T6] development of a defect cause diagnosis algorithm, and [T7] development of a casting parameter tuning algorithm. The details of the proposed data-analytics-based factory operation strategies with regard to the casting parameter input and output data, data preprocessing, data analytics method used, and implementation are presented and discussed. Finally, a case study of a die-casting factory in South Korea that has adopted the proposed strategies is introduced." @default.
• W4220759116 created "2022-04-03" @default.
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• W4220759116 date "2022-01-10" @default.
• W4220759116 modified "2023-10-01" @default.
• W4220759116 title "Data-analytics-based factory operation strategies for die-casting quality enhancement" @default.
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• W4220759116 doi "https://doi.org/10.1007/s00170-021-08625-8" @default.
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