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 |  موضوع: كتاب Intelligent Machining of Complex Aviation Components الجمعة 03 يونيو 2022, 1:32 am | |
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أخواني في الله
أحضرت لكم كتاب
Intelligent Machining of Complex Aviation Components
Dinghua Zhang, Ming Luo, Baohai Wu, Ying Zhang
و المحتوى كما يلي :
Contents
1 Introduction . 1
1.1 Numerical Control Machining Technology . 1
1.1.1 Development of Numerical Control Technology 1
1.1.2 Development Stage of CNC Machining Model . 3
1.2 Intelligent Processing Technology 5
1.2.1 Intelligent Processing Technology 5
1.2.2 Ways to Realize Intelligent Processing 6
1.2.3 Basic Knowledge of Intelligent Processing Technology 7
1.3 The Content of This Book . 9
References . 9
2 Polymorphic Evolution Process Model for Time-Varying
Machining Process . 11
2.1 Description of the Machining Process System 11
2.1.1 The Cuter-Spindle Subsystem Dynamics Model 13
2.1.2 Workpiece-Fixture Subsystem Dynamic Model . 14
2.2 Polymorphic Evolution Model of Machining Process 14
2.2.1 Definition of the Machining Process 14
2.2.2 Time Domain Dispersion of the Machining Process . 15
2.2.3 Evolution of Polymorphic Models 17
2.3 Workpiece Geometric Evolution Model 19
2.3.1 Geometric Deformation Mapping Method . 20
2.3.2 Deformation Mapping Modeling Method for Complex
Machining Features 26
2.4 The Workpiece Dynamic Evolution Model . 32
2.4.1 Dynamic Evolution Analysis of Workpiece Based
on Structural Dynamic Modification Technique . 33
2.4.2 Dynamic Evolution Analysis of Workpieces Based
on the Thin Shell Model 34
viiviii Contents
2.5 Tool Wear Evolution Model 40
2.5.1 Tool Wear During the Machining Process . 40
2.5.2 Evolutionary Modeling of Tool Flank Wear 42
References . 43
3 Machining Process Monitoring and the Data Processing Method 45
3.1 The Detection Method During Cutting Process 46
3.2 Machining Process Detection 46
3.2.1 The Concept of Detection Processing . 48
3.2.2 Implementation Method of Detection Processing . 49
3.3 Milling Force Based Cutting Depth and Width Detection . 52
3.3.1 Average Milling Force 52
3.3.2 Detection and Measurement in the Milling Process . 53
3.3.3 Detection Response Equation 53
3.3.4 Detection and Recognition of Depth and Width of Cut . 54
3.4 Detection and Recognition of Milling Cutter Wear Status . 57
3.4.1 Measurement of Tool Wear 57
3.4.2 Milling Force Model of Worn Tool . 58
3.4.3 Identification Process Analysis . 62
3.4.4 Calculation and Identification of Wear 63
3.5 Identification of Cutting Force Coefficients Based
on Monitored Data . 64
3.5.1 Cutting Force Modeling Considering Cutter Vibrations 64
3.5.2 Cutting Force Coefficients Identification Considering
Vibration . 72
References . 75
4 Learning and Optimization of Process Model . 77
4.1 Learning and Optimization Method of the Machining Process
Model 78
4.2 Time-Position Mapping of Processing Data 79
4.3 Iterative Learning Method of Machining Error Compensation . 84
4.3.1 In-Position Detection Method for Workpiece
Geometry Information 85
4.3.2 Compensation Modeling of Machining Errors
for Thin-Wall Parts . 86
4.3.3 Solution of Error Compensation Model
for Thin-Walled Parts . 88
4.3.4 Learning Control Method for Error Compensation
Coefficient 93
4.3.5 The Application of Error Iterative Compensation
Method in Thin-Walled Blade Machining 95
4.4 Iterative Learning Optimization Method for Deep-Hole
Drilling Depth . 97
4.4.1 Chip Evacuation Force Model for One-Step Drilling 98Contents ix
4.4.2 Chip Evacuation Process in Peck Drilling
for Deep-Hole . 101
4.4.3 Iterative Learning Method for Drilling Depth
Optimization 104
4.5 Process Optimization Method for Multi-hole
Varying-Parameter Drilling 107
4.5.1 Mathematical Model of Drilling Parameter
Optimization 107
4.5.2 Drilling Parameter Optimization Procedure 111
4.6 Cyclic Iterative Optimization Method for Process Parameters . 119
4.6.1 Mathematical Model of Feed Rate Optimization 121
4.6.2 Online Solving for Feed Speed Optimization Problem . 126
4.6.3 Offline Learning and Iterative Optimization
for Process Parameters 133
References . 134
5 Dynamic Response Prediction and Control for Machining
Process 135
5.1 Control Method of Dynamic Response for Machining Process 135
5.2 Alternating Excitation Force During Milling 136
5.2.1 Alternating Excitation Force . 136
5.2.2 Characterization and Decomposition of Alternating
Excitation Force . 137
5.3 Prediction of Milling Dynamic Response 139
5.3.1 Forced Vibration in Milling 139
5.3.2 Prediction of Milling Chatter Stability 142
5.4 Dynamic Response Control of Milling Based on Optimization
of Cutting Parameters . 149
5.5 Response Control Method Based on Variable Pitch Cutters
Optimization Design 153
5.5.1 Stability Limit Calculation of Variable Pitch Cutters 153
5.5.2 Geometrical Relation Between Adjacent Pitch Angles . 155
5.5.3 Design of Variable Pitch Angles 156
5.6 Control Method of Workpiece-Fixture Subsystem Dynamic
Characteristics . 157
5.6.1 Control Method Based on Additional Auxiliary
Support . 157
5.6.2 Control Method Based on Additional Masses 160
5.6.3 Control Method Based on Magnetorheological
Damping Support 161
References . 166
6 Clamping Perception for Residual Stress-Induced Deformation
of Thin-Walled Parts . 167
6.1 Residual Stress in Cutting Process 168
6.2 Residual Stress-Induced Deformation . 169x Contents
6.3 Principles of RSID Perception and Prediction . 172
6.4 RSID Perception Prediction Model 174
6.5 Potential Energy Perception of Residual Stress
and Deformation in Typical Clamping Forms . 176
6.5.1 Surface Constraints in Redundant Constraints 177
6.5.2 Redundant Constraints Are Point Constraints 178
6.6 Solving Residual Stress and Deformation Perception
Prediction Model . 179
6.6.1 Solution Method and Procedure 179
6.6.2 Application Cases in Thin-Walled Parts Machining . 184
6.7 Active Control Method for Residual Stresses Induced
Deformation of Thin-Walled Parts 187
6.7.1 Evolution of Residual Stress in Machining Process 187
6.7.2 The In-Processes Active Control Method 191
6.7.3 Application of Active Control Method for RSID
in Blade Machining 195
References
1. WANG J T. Research on prediction and optomization method of deformation induced by residual
stresses in milling of thin-walled parts [D]. Xi’an; Northwestern Polytechnical University, 2019.
2. WANG J T, ZHANG D H, WU B H, et al. Prediction of distortion induced by machining residual
stresses in thin-walled components [J]. The International Journal of Advanced Manufacturing
Technology, 2018, 95(9): 4153–4162.
3. YANG Y F. Research on machining deformation prediction and control technology of monolithic
structure based on internal stress field [D]. Nanjing; Nanjing University of Aeronautics and
Astronautics, 2010.
4. ZHANG P X. Theoretical structural mechanics of energy [M]. Shanghai: Shanghai Scientific &
Technical Publishers, 2010.
5. ZHANG Z X. The prediction and control methods for the deformation of complicated thinwalled parts that induced by residual stress [D]. Xi’an; Northwestern Polytechnical University,
2021.
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