工具機在金屬切削中，低的表面粗糙度改善產品的摩擦學性能、耐腐蝕性和美觀。因此，若能預測產品的表面粗糙度，使產品達到低的表面粗糙度，不僅能使產品品質更加良好，且也降低了加工成本。
本論文提出一個倒傳遞類神經網路(BPNN)預測工具機加工後的工件表面粗糙度，運用工具機台的參數，主軸轉速、進給率、切削深度和銑削間距進行實際切削的實驗，並且量測其工件的表面粗糙度，最後再對各參數進行分析。
根據實驗結果，倒傳遞類神經網絡的均方根誤差（RMSE）小於線性回歸，測定係數也達到0.9995。 表示反向神經網絡具有很好的預測表面粗糙度的能力。在ANOVA異變數分析中，所用的加工參數與表面粗糙度的關係均為顯著。

In the metal cutting of the tool machine, the value of the surface roughness improves the tribological property, corrosion resistance and beauty of the product. Therefore, if the surface roughness of the product can be predicted and the product can achieve a low surface roughness, not only can the product quality be better, but also the processing cost is reduced.
In this paper, an inverse backpropagation neural network (BPNN) is proposed to predict the surface roughness of the machined workpiece. Using the parameters of the machine tool, spindle speed, feed rate, cutting depth and milling distance to carry out the actual cutting experiment, and measure the surface roughness of the workpiece, and finally analyze the parameters.
According to the experimental results, the root mean square error (RMSE) of the backpropagation neural network is smaller than the linear regression, and the measurement coefficient also reaches 0.9995. Represents that the backpropagation neural network has a good ability to predict surface roughness.

摘要..........................................................................i
Abstract......................................................................ii
Acknowledgements..............................................................iii
Contents......................................................................iv
List of Tables................................................................vii
List of Figures...............................................................ix
Chapter 1 Introduction........................................................1
1.1 Research Background.......................................................1
1.2 Research motivation and purpose...........................................2
1.3 Literature Review.........................................................2
1.4 Organization of Dissertation..............................................10
Chapter 2 Surface roughness of workpiece......................................11
2.1 Workpiece detection and monitoring methods................................11
2.1.1 Contact surface rough measurement.......................................11
2.1.2 Non-contact surface roughness...........................................13
2.1.3 Internal signal measurement.............................................15
2.2 Milling pitch.............................................................16
2.3 Principle of surface roughness............................................16
2.3.1 Surface roughness of various machining methods..........................18
2.3.2 Surface roughness parameters............................................20
2.3.3 Surface roughness formula...............................................21
Chapter 3 Measurement of Surface Roughness....................................23
3.1 Experiment apparatus......................................................23
3.1.1 Machine tool specifications.............................................24
3.1.2 Tool specifications.....................................................26
3.1.3 Z–axis setting..........................................................26
3.1.3.1 Model and specifications..............................................26
3.1.3.2 Z-axis setter set zero offset.........................................27
3.1.4 Blum tool setter........................................................29
3.1.5 Workpiece material......................................................30
3.2 Experimental plans and procedures.........................................32
3.3 Surface roughness measurement.............................................35
3.3.1 Introduction of surface roughness instrument............................35
3.3.2 Calibration and measurement of surface roughness instrument.............37
3.4 Experimental parameters...................................................39
Chapter 4 Building of Surface Roughness Prediction Model......................40
4.1 Backpropagation neural network (BPNN).....................................41
4.1.1 Theory of backpropagation neural network................................41
4.1.2 Mathematical derivation of backpropagation neural network...............44
4.2 Linear regression.........................................................47
4.3 Analysis of Variance......................................................49
Chapter 5 Analysis of Experimental Results of Surface Roughness Prediction Model.........................................................................52
5.1 Proposed backpropagation neural network...................................53
5.2 Orthogonal table of experimental parameters...............................54
5.3 Backpropagation neural network prediction.................................56
5.4 Comparison of backpropagation neural network parameters...................60
5.5 ANOVA parametric analysis.................................................70
Chapter 6 Conclusions and Future Works........................................72
6.1 Conclusions...............................................................72
6.2 Future Works..............................................................74
Reference.....................................................................75
Appendix......................................................................82
Abstract......................................................................85
Chapter 1 Introduction.......................................................86
Chapter 2 Surface roughness of workpiece......................................87
Chapter 3 Measurement of Surface Roughness....................................87
Chapter 4 Building of Surface Roughness Prediction Model......................88
Chapter 5 Analysis of Experimental Results of Surface Roughness Prediction Model ..............................................................................89
Chapter 6 Conclusions.........................................................90

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