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題名：盤元鋼材製程參數最佳化之研究
作者：蔡日新
作者(外文)：Jeh-Hsin Tsai
校院名稱：國立中山大學
系所名稱：企業管理學系研究所
指導教授：盧淵源
學位類別：博士
出版日期：2006
主題關鍵詞：田口方法；類神經網路；基因演算法；參數設計；Neural Network；Taguchi Methods；Parameter Design；Genetic Algorithm
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田口方法或稱品質工程，係以最經濟的成本及最短的時間，從事產品設計(改良)、製程設計(改良)作業，期使產品能夠充分滿足客戶需求的一套系統性方法。其參數設計又叫穩健設計，具有低成本、高效率的優點，能協助企業做好產品品質設計、管理和改善等工作，進而增強企業的競爭能力。因此如何有效應用參數設計，縮短研發時間，促使低成本高品質產品早日上市，俾強化競爭優勢是一值得研究的方向。
然而，參數設計在最佳化應用時，仍有若干困擾：(1)當系統輸入、輸出及參數間存在複雜與非線性關係 (2)參數間具有交互作用 (3)在田口方法二階段最適化程序中，調整因子在實務中未必存在 (4)對於不完整數據，田口方法並無法良好處理之。
類神經網路具學習、容錯及不需要模式等特性，因此在處理多變數之輸入、輸出執行時，是一具競爭性的工具，其成功領域包括診斷、機器人、排程、決策、預測等，同時基因演算法在找尋最佳化之過程中，可以避免陷入局部最佳解，所以它找到全域最佳解的機會頗大。因此本研究運用田口方法/類神經網路/基因演算法/主成份分析法分別找尋兩種盤元鋼材之最佳化製程參數設計。
本研究經由球化理論探討，歸納出要因參數，再分別導入L18及L9直交實驗，結果經由訊號雜因比分析，已發現該兩鋼材之最佳球化熱處理模式，研究結論分述如下:
1. 3130鋼種:因球化率不合再處理量曾高居各鋼種之冠，必須經二次熱處理處理，改善前實績顯示，因球化率不合之再處理量為83公噸，故亟待改善。經設計L18(61×35) 直積實驗，進行球化熱處理模式試驗，已獲得最佳之退火模式為：均溫溫度B2℃，均溫時間A2小時後，以C2℃/hr.冷卻至D1℃，再以E1℃/hr.緩冷至F2℃。經再確任試驗 37爐盤元，皆只需一次熱處理，且熱處理後球化率皆佳，為1~2級，再處理率由改善前的2.63%，降為0%。
2. 6036鋼種:屬中碳低合金鋼冷打材，是所有球化鋼種最難處理者，改善前實績顯示，球化率不合再處理量高達256噸，高居各鋼種之首。為降低該鋼種再處理率，經設計L9(34) 直積實驗，進行球化熱處理模式試驗，已獲得最佳之熱處理模式為：二段均溫時間A3小時、緩冷起始溫度B1℃、低均溫溫度C3℃、緩冷時間D3小時。經再確認試驗6爐結果，球化率1.5~3級，再處理率由改善前的15.92%，降為0%。
Taguchi methods is also called quality engineering. It is a systematic methodology for product design(modify) and process design(improvement) with the most of saving cost and time, in order to satisfy customer requirement. Taguchi’s parameter design is also known as robust design, which has the merits of low cost and high efficiency, and can achieve the activities of product quality design, management and improvement, consequently to reinforce the competitive ability of business. It is a worthy research course to study how to effectively apply parameter design, to shorten time spending on research, early to promote product having low cost and high quality on sale and to reinforce competitive advantage.
However, the parameter design optimization problems are difficult in practical application owing to (1)complex and nonlinear relationships exist among the system’s inputs, outputs and parameters and (2)interactions may occur among parameters. (3)In Taguchi’s two-phase optimization procedure, the adjustment factor cannot be guaranteed to exist in practice. (4)For some reasons, the data may become lost or were never available. For these incomplete data, the Taguchi’s method cannot treat them well.
Neural networks have learning capacity fault tolerance and model-free characteristics. These characteristics support the neural networks as a competitive tool in processing multivariable input-output implementation. The successful field including diagnostics, robotics, scheduling, decision-marking, predicition, etc. In the process of searching optimization, genetic algorithm can avoid local optimization. So that it may enhance the possibility of global optimization.
This study had drawn out the key parameters from the spheroidizing theory, and L18, L9 orthogonal experimental array were applied to determine the optimal operation parameters by Signal/Noise analysis. The conclusions are summarized as follows:
1. The spheroidizing of AISI 3130 used to be the highest unqualified product, and required for the second annealing treatment. The operational record before improvement showed 83 tons of the 3130 steel were required for the second treatment. The optimal operation parameters had been defined by L18(61×35) orthogonal experimental array. The control parameters of the annealing temperature was at B2
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