| 研究生: |
張振弘 Chang, Chen-Hung |
|---|---|
| 論文名稱: |
運用六標準差與實驗設計法進行車用零件表面粗糙度改善之研究 The Study of Applying Six Sigma and Design of Experiment Method for Improving Surface Roughness of Automotive Components |
| 指導教授: |
胡政宏
Hu, Cheng-Hung |
| 學位類別: |
碩士 Master |
| 系所名稱: |
管理學院 - 工業與資訊管理學系碩士在職專班 Department of Industrial and Information Management (on the job class) |
| 論文出版年: | 2023 |
| 畢業學年度: | 111 |
| 語文別: | 中文 |
| 論文頁數: | 70 |
| 中文關鍵詞: | 六標準差方法 、實驗設計 、統計製程管制 |
| 外文關鍵詞: | Six Sigma Method, Design of Experiments, Statistical Process Control |
| 相關次數: | 點閱:89 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
車輛工業屬於資本與技術密集的綜合性產業,整體車輛工業產業鏈相當廣泛,汽機車本身零件大致有3萬多種,相關產業包含鋼鐵、電子、石化、塑膠等,一輛汽車的生產製造會連同帶動這些相關產業的發展。而車用零件的製作,又以外徑研磨是最攸關整個零件產品的品質優劣,是最直接攸關汽機車壽命與行車安全的重要技術。考慮到汽車零件外徑研磨的技術攸關整個汽車的使用壽命,故本篇研究著重利用六標準差和實驗設計的方法找出影響金屬表面粗糙度的關鍵因子。
為了改善金屬表面粗糙度,研究採用了實驗設計法來探討影響表面粗糙度的關鍵因子,同時運用六標差技術作為架構,包含問題界定、衡量階段、分析階段、改善階段與控制階段,來提升表面粗糙度的品質。其中在改善階段,將以全因子實驗確認主效應是否有顯著影響並獲得迴歸方程式,並透過反應曲面法得到最佳解,最後以製程能力指數驗證對於最終解的信心度和嚴謹度。
研究結果顯示,砂輪轉速、修砂輪速度和細磨占比是影響金屬表面粗糙度的關鍵因子。透過對這三項關鍵因子的控制,並制定砂輪轉速為1850、修砂輪速度為70、細磨占比為0.6的參數設定,成功降低了表面粗糙度值至2.915。同時,製程能力指數(Cpk)也從原本的0.984提升至1.876,顯示產品品質得到了明顯的改善。
The automotive industry is a capital- and technology-intensive comprehensive industry. The overall automotive industry chain is quite extensive, with over 30,000 types of components for automobiles and motorcycles. Related industries include steel, electronics, petrochemicals, plastics, and more. The production of a vehicle not only drives the development of these related industries but also relies on the quality of automotive parts, with outer diameter grinding being a crucial technique that directly impacts the quality of the entire component and the lifespan and safety of automobiles and motorcycles. Recognizing the significance of outer diameter grinding in the lifespan of automotive parts, this study focuses on utilizing the Six Sigma and experimental design methods to identify key factors influencing the surface roughness of metal.
To improve the surface roughness of metal, the study adopts an experimental design method to investigate the key factors influencing surface roughness. The Six Sigma methodology is used as the framework, which includes definition, measurement, analysis, improvement, and control, aiming to enhance the quality of surface roughness. In the improvement phase, a full factorial experiment is conducted to determine if the main effects have a significant impact and to obtain a regression equation. The optimal solution is then obtained through response surface methodology, the confidence and rigor of the optimal parameters are verified by the process capability (CPK).
The research results indicate that the key factors affecting the surface roughness of metal are the wheel rotation speed, the speed of wheel dressing, and the proportion of fine grinding. By controlling these three key factors and setting the wheel rotation speed at 1850, the wheel dressing speed at 70, and the proportion of fine grinding at 0.6, the surface roughness value was successfully reduced to 2.915. Furthermore, the process capability index (Cpk) increased from the original 0.984 to 1.876, indicating a significant improvement in product quality.
AIAG, “Measurement system analysis”, Automotive Industry Action Group, Detroit, New York, 2010.
Aouici, H., Bouchelaghem, H., Yallese, M. A., Elbah, M., & Fnides, B. (2014). Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology. The International Journal of Advanced Manufacturing Technology, 73(9), 1775-1788.
Brue, G., & Launsby, R. G. (2003), Design for Six Sigma (Briefcase Books Series),
McGraw-Hill, New York.
Chowdhury, B., & Deb, S. K. (2021). Optimizing Thermoforming of Refrigerator Liners to Reduce Liner Rejection Rate—A Case Study Using Fractional Factorial Design of Experiments. In Advances in Mechanical Engineering (pp. 225-238). Springer, Singapore.
Djokić, M., Djuriš, J., Solomun, L., Kachrimanis, K., Djurić, Z., & Ibrić, S. (2014). The influence of spiral jet-milling on the physicochemical properties of carbamazepine form III crystals: quality by design approach. Chemical Engineering Research and Design, 92(3), 500-508.
Erameh, A. A., Raji, N. A., Durojaye, R. O., & Yussouff, A. A. (2016). Process capability analysis of a centre lathe turning process. Engineering, 8(03), 79.
Ganguly, K. (2012). Improvement process for rolling mill through the DMAIC six sigma approach. International Journal for quality research, 6(3), 221-231.
Garza-Reyes, J. A., Al-Balushi, M., Antony, J., & Kumar, V. (2016). A Lean Six Sigma framework for the reduction of ship loading commercial time in the iron ore pelletising industry. Production Planning & Control, 27(13), 1092-1111.
Gijo, E. V., Bhat, S., & Jnanesh, N. A. (2014). Application of Six Sigma methodology in a small-scale foundry industry. International Journal of Lean Six Sigma.
Gijo, E. V., Scaria, J., & Antony, J. (2011). Application of Six Sigma methodology to reduce defects of a grinding process. Quality and reliability engineering international, 27(8), 1221-1234.
Guleria, P., Pathania, A., Sharma, S., & Sá, J. C. (2022). Lean six-sigma implementation in an automobile axle manufacturing industry: A case study. Materials Today: Proceedings, 50, 1739-1746.
Hakimi, S., Zahraee, S. M., & Rohani, J. M. (2018). Application of Six Sigma DMAIC methodology in plain yogurt production process. International Journal of Lean Six Sigma.
Hanief, M., Wani, M. F., & Charoo, M. S. (2017). Modeling and prediction of cutting forces during the turning of red brass (C23000) using ANN and regression analysis. Engineering science and technology, an international journal, 20(3), 1220-1226.
Kumar, P., & Chauhan, S. R. (2015). Machinability study on finish turning of AISI H13 hot working die tool steel with cubic boron nitride (CBN) cutting tool inserts using response surface methodology (RSM). Arabian Journal for Science and Engineering, 40(5), 1471-1485.
Kechagias, J. D., Aslani, K. E., Fountas, N. A., Vaxevanidis, N. M., & Manolakos, D. E. (2020). A comparative investigation of Taguchi and full factorial design for machinability prediction in turning of a titanium alloy. Measurement, 151, 107213.
Loizou, J., Tian, W., Robertson, J., & Camelio, J. (2015). Automated wear characterization for broaching tools based on machine vision systems. Journal of Manufacturing Systems, 37, 558-563.
Minh, L. D., Ni, V. T. H., & Hien, D. N. (2019). Continuous improvement of productivity and quality with lean Six-Sigma: a case study. In Applied Mechanics and Materials (Vol. 889, pp. 557-566). Trans Tech Publications Ltd.
Montgomery, D. C. (2012). Design and analysis of experiments. John wiley & sons.
Muhammad, N., Manurung, Y. H., Jaafar, R., Abas, S. K., Tham, G., & Haruman, E. (2013). Model development for quality features of resistance spot welding using multi-objective Taguchi method and response surface methodology. Journal of Intelligent Manufacturing, 24(6), 1175-1183.
Noori, B., & Latifi, M. (2018). Development of Six Sigma methodology to improve grinding processes: a change management approach. International journal of lean six sigma.
Pai, D., Rao, S., Shetty, R., & Nayak, R. (2010). Application of response surface methodology on surface roughness in grinding of aerospace materials.
Park SH (2002). Six Sigma for productivity improvement: Korean business corporations. Productivity 43(2), 173–183
Prabakaran, M., Viswabharathy, P., Prakash, V., Rajarathinam, N., Rajagopal, P., & Sivarajan, M. Experimental Investigation on Process Capability & Process Capability Index in Grinding Machine.
Raja, S. K., & Jaiganesh, V. (2021). Productivity Improvement Through Cycle Time Reduction In A Gear Manufacturing Industry. Int. J. of Aquatic Science, 12(2), 4386-4404.
Saha, A., & Majumder, H. (2018). Performance analysis and optimization in turning of ASTM A36 through process capability index. Journal of King Saud University-Engineering Sciences, 30(4), 377-383.
Sanjeevi, R., Kumar, G. A., & Krishnan, B. R. (2021). Optimization of machining parameters in plane surface grinding process by response surface methodology. Materials Today: Proceedings, 37, 85-87.
Shao, C., Wang, H., Suriano-Puchala, S., & Hu, S. J. (2019). Engineering fusion spatial modeling to enable areal measurement system analysis for optical surface metrology. Measurement, 136, 163-172.
Snee Ronald D. (2010), “Lean Six Sigma – getting better all the time”, International Journal of Lean Six Sigma, 1(1), 9-29
Suresh, R., Basavarajappa, S., Gaitonde, V. N., & Samuel, G. L. (2012). Machinability investigations on hardened AISI 4340 steel using coated carbide insert. International Journal of Refractory Metals and Hard Materials, 33, 75-86.
Tribot, A., Delattre, C., Badel, E., Dussap, C. G., Michaud, P., & de Baynast, H. (2018). Design of experiments for bio-based composites with lignosulfonates matrix and corn cob fibers. Industrial Crops and Products, 123, 539-545.
Ucurum, M., Malgir, E., Deligezen, H., Karaer, N., & Avsar, M. (2016). Applicability of statistical process control for surface modification plant and properties of coated calcite. Physicochemical Problems of Mineral Processing, 52.
Vännman, K. (1995). A unified approach to capability indices. Statistica Sinica 5:805–820.
校內:2028-06-29公開