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研究生:伍尚宏
研究生(外文):WU, SHANG-HUNG
論文名稱:五軸工具機結構分析與智慧化加工
論文名稱(外文):Structure Analysis of Five-Axis Machine Tools and Intelligent Machining Process
指導教授:詹子奇詹子奇引用關係
指導教授(外文):CHAN, TZU-CHI
口試委員:李炳寅陳進益盧銘詮
口試委員(外文):LEE, BEEN-YINCHEN, JENN-YIHLU, MING-CHYUAN
口試日期:2019-07-19
學位類別:碩士
校院名稱:國立虎尾科技大學
系所名稱:機械與電腦輔助工程系碩士班
學門:工程學門
學類:機械工程學類
論文種類:學術論文
論文出版年:2019
畢業學年度:107
語文別:中文
論文頁數:78
中文關鍵詞:五軸工具機有限元素法模態分析模態測試熱變形智慧預測與診斷系統智慧化加工
外文關鍵詞:Five-axis machine toolFinite element methodModal analysisModal testThermal deformationPrediction diagnosis performance systemIntelligent Machining Process
相關次數:
  • 被引用被引用:11
  • 點閱點閱:625
  • 評分評分:
  • 下載下載:185
  • 收藏至我的研究室書目清單書目收藏:1
工具機不斷的朝向多軸化、高速化、高精度化與智慧化的方向發展,因此為因應工具機高速化與高精度化的性能要求,其結構設計必須有足夠的剛性與最佳化的設計考量。工具機結構是影響加工品質的基礎,良好的工具機結構能提供良好的動態特性,因此可以提升加工品質,反之則會造成加工成品不良,無法滿足顧客需求。

本論文主要研究五軸工具機的結構靜剛性、動剛性、熱變形分析與測試驗證,並進行結構最佳化與智慧化加工實驗。本研究採用有限元素法作為結構模態分析工具,並且搭配模態實驗進行比對,驗證五軸工具機各自然頻率與振型。分析模型配合實際物理現象建立各個零組件的邊界條件與設定參數,使分析結果更能符合實際機台特性,並作為後續優化五軸工具機之結構的基礎,並進一步進行結構改善達到更好的結構靜、動態特性,提升機台之加工精度。此外本研究也運用智慧預測與診斷系統監測機台加工中之切削訊號,並對切削訊號進行特徵選取與分析,切削過程中能預測與監控切削品質,因此有機會進一步達到產業智慧製造的需求。研究結果顯示經由拓樸最佳化的設計,可有效減重、改善最大變形量與提升機台之自然頻率,另經由智慧預測與診斷系統可針對設備健康狀態進行監測,並可達到預測生產的功能,在加工過程中就能即時預測機台加工性能與健康狀態,相信本研究對於工具機產業朝向智慧製造的發展,會有具體的助益與技術上的突破。
The machine tool technology is constantly developing in the direction of multi-axis, high-speed, high-precision and intelligent. Therefore, in order to meet the performance requirements of high-speed and high-precision machine tools, the structural design must be sufficient. Rigidity and optimized design considerations. The structure of the machine tool is the most important factor affecting the processing quality. A good machine tool structure can provide good dynamic characteristics, so it can improve the processing quality, and vice versa will result in poor finished products and can not meet customer needs.

This thesis mainly studies the structural static stiffness, dynamic stiffness and thermal deformation analysis and test verification of the five-axis machine tool, and carries out structural optimization and intelligent machining experiments. In this study, the finite element method is used as a structural modal analysis tool, and it is compared with the modal experiment to verify the natural frequency and vibration mode of the five-axis machine tool. The analysis model is combined with the actual physical phenomena to establish the boundary conditions and setting parameters of each component, so that the analysis results can better conform to the actual machine characteristics, and serve as the basis for the subsequent optimization of the structure of the five-axis machine tool, and further improve the structure to achieve better. The static and dynamic characteristics of the structure enhance the machining accuracy of the machine. In addition, this study also uses the prediction diagnosis performance system to monitor the cutting signals in the machining of the machine, and selects and analyzes the cutting signals. The cutting process can predict and monitor the cutting quality, so it has the opportunity to further meet the needs of industrial smart manufacturing. The research results show that the optimized design can effectively reduce weight, improve the maximum deformation and increase the natural frequency of the machine; Monitor the health of the equipment through the prediction diagnosis performance system, and achieve the predicted production. Function, in the machining process, can immediately predict the processing performance and health status of the machine. I believe that this research will have specific benefits and breakthroughs for the development of the machine tool industry towards smart manufacturing.
摘要…i
Abstract…ii
誌謝…iii
目錄…iv
表目錄…vi
圖目錄…vii
符號說明…ix
第一章 緒論…1
1.1研究背景…1
1.2研究目的…1
1.3文獻回顧…1
1.4論文大綱…4
第二章 相關基礎理論與實驗設備…6
2.1振動與模態基礎理論…6
2.1.1單自由度系統…6
2.1.2模態實驗分析簡介…7
2.1.3模態分析重要名詞…9
2.2有限元素法簡介…10
2.2.1有限元素法平衡方程式…11
2.2.2有限元素種類…13
2.2.3有限元素分析重要名詞…14
2.3實驗設備…16
2.3.1衝擊槌…16
2.3.2加速規…16
2.3.3溫度感測器…18
2.3.4電流感測器…19
2.3.5頻譜分析儀…20
2.3.6 ME’scope…21
2.3.7 ANSYS…22
2.3.8 Hyperworks…23
2.3.9 Olympus STM6 高精度工具顯微鏡…23
2.3.10 Surfcorder SE-4000表面粗糙度量測儀…24
2.3.11智慧預測與診斷系統(PDPS)…25
2.3.12 PMC小型五軸工具機…26
第三章 自然模態實驗與有限元素分析…28
3.1有限元素分析…28
3.1.1有限元素模型…28
3.1.2材料性質…29
3.1.3網格化…30
3.1.4收斂性分析…30
3.1.5有限元素模型邊界條件…31
3.1.6靜剛性分析…32
3.1.7模態分析…34
3.1.8頻譜分析…35
3.1.9暫態分析…35
3.1.10熱變形分析…36
3.2模態實驗…36
3.2.1五軸工具機佈點配置…36
3.2.2敲擊試驗…41
3.2.3結構自然頻率分析…44
3.3 CAD模型驗證…46
第四章 結構優化比較…48
4.1結構優化設計…48
4.1.1拓樸優化…49
4.1.2結構優化再設計…49
4.2設計變更結果比較…50
第五章 智慧化加工…55
5.1加工實驗…55
5.2智慧加工測試與方法…56
5.3實驗量測方法…58
5.3.1刀具磨耗…58
5.3.2工件表面粗糙度…63
5.4切削訊號特徵模型…66
5.4.1刀頻資訊…68
5.4.2合力趨勢…69
5.4.3訊號資料分佈…69
第六章 結論與未來展望…71
6.1結論…71
6.2未來展望…71
參考文獻…72

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