精實生產流程於模具製程改善之應用

本研究目的以實際精實六標準差專案，透過整合精實生產與六標準差手法，解決模具表面粗糙度之問題。研究中透過由價值溪流圖定義流程與現況績效衡量後，研究中將六面體加工、成形加工、尺寸加工，三個工作站進行合併，使其生產流程時間平準化。同時，針對平準化改善時於實務上相關問題解決，則利用田口方法，來提升產品製程能力，以達成改善目標。同時為符合實務上針對不同客戶與不同產品之多樣表面粗度需求，則提出運用類神經網路架構預測模式，免去重覆啟動田口實驗演算所需耗費的大量時間與成本。

關鍵字

銑削； 3C產品；六標準差；田口方法；倒傳遞類神經網路

並列摘要

The purpose of this study is to integrate lean production and the six sigma technique into actual lean six sigma projects to solve the problem of mold surface roughness. This process is defined by Value Stream Mapping, current performance is measured, and hexahedron processing, forming, and size processing workstations are merged, in order to stabilize production flow time. Meanwhile, to solve the practical problems of stabilization improvement, the Taguchi method is used to enhance product process capability, and thus, attain the goal of improvement. To meet the multiple surface roughness requirements of different customers for different products in practice, the Artificial Neural Network prediction model is used to save the time and costs of repeatedly starting Taguchi experiment calculations.

並列關鍵字

Milling ； 3C Product ； Six Sigma ； Taguchi Method ； Back-propagation Neural Network

參考文獻

4. Coronado, R. B., & Antony, J. (2002). Critical success factors for the successful implementation of six sigma projects in organisations. The TQM magazine, 14(2), 92-99.

5. Hornik, K., Stinchcombe, M., & White, H. (1989). Multilayer feedforward networks are universal approximators. Neural networks, 2(5), 359-366.

6. Kwak, Y. H., & Anbari, F. T. (2006). Benefits, obstacles, and future of six sigma approach. Technovation, 26(5), 708-715.

7. Lo, S. P. (2003). An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of Materials Processing Technology, 142(3), 665-675.

8. Pfeifer, T., Reissiger, W., & Canales, C. (2004). Integrating six sigma with quality management systems. The TQM Magazine, 16(4), 241-249.

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精實生產流程於模具製程改善之應用

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