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عدد المساهمات : 19001 التقييم : 35505 تاريخ التسجيل : 01/07/2009 الدولة : مصر العمل : مدير منتدى هندسة الإنتاج والتصميم الميكانيكى
| موضوع: كتاب Smart Machining Systems - Modelling, Monitoring and Informatics الجمعة 13 مايو 2022, 4:17 pm | |
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أخواني في الله أحضرت لكم كتاب Smart Machining Systems - Modelling, Monitoring and Informatics Kunpeng Zhu
و المحتوى كما يلي :
Contents 1 Introduction to the Smart Machining System 1 1.1 The Development of Modern Manufacturing System 1 1.2 Modern Machining Technology 4 1.2.1 High Precision Machining . 4 1.2.2 High Speed Machining 5 1.2.3 Green Machining . 6 1.2.4 Smart Machining . 7 1.3 The Smart Machining System 7 1.3.1 Intelligent Process Planning 9 1.3.2 The Process Simulation and Optimization 9 1.3.3 The Machining Process Monitoring . 11 1.3.4 The Intelligent Control 12 1.3.5 The Database and Big Data Analytics 13 1.3.6 Smart Machine Tool . 13 1.4 The Trends of Smart Machining System . 15 References 16 2 Modeling of the Machining Process . 19 2.1 The Machining Process Modeling Methods 19 2.1.1 Modeling Based on Cutting Mechanics 20 2.1.2 Modeling Based on Machine Tool Vibration 20 2.1.3 Modeling Based on Numerical Simulation 20 2.1.4 Modeling Based on Measurement Information 21 2.1.5 Modeling Based on Artificial Intelligence (AI) 21 2.1.6 Modeling Method Combining Data and Cutting Mechanics . 22 2.2 Principles of Chip Formation . 22 2.2.1 Chip Formation . 22 2.2.2 Mechanical Model of Chip Formation . 22 2.2.3 Divisions of Deformation Zones 25 2.3 Cutting Forces . 27 xixii Contents 2.3.1 Sources of Cutting Forces 27 2.3.2 Joint and Component Cutting Forces and Cutting Powers 28 2.3.3 Empirical Models of Cutting Forces . 29 2.3.4 Affecting Factors of Cutting Forces 33 2.4 Cutting Heat and Temperatures . 36 2.4.1 Generation and Transfer of Cutting Heat . 36 2.4.2 Cutting Temperatures and Their Distributions . 38 2.4.3 Modeling of Temperature Fields 39 2.5 Milling Process Modeling and Control 41 2.5.1 Types of Milling Cutters . 41 2.5.2 Milling Types 43 2.5.3 Milling Parameters and Cutting Layer Parameters . 45 2.5.4 Milling Forces 49 2.5.5 The Milling System Dynamics 51 2.6 High-Speed Machining 56 2.6.1 Introduction to High-Speed Machining . 56 2.6.2 Advantages of High-Speed Machining . 58 2.6.3 Modeling of the Three-Dimensional Instantaneous Milling Force 59 2.7 Control of Machining Process 63 References 67 3 Tool Wear and Modeling . 71 3.1 Types of Tool Wear . 71 3.1.1 Crater Wear 72 3.1.2 Flank Wear 72 3.1.3 Boundary Wear . 73 3.1.4 Tool Wear Criteria 74 3.2 The Formation of Tool Wear . 75 3.2.1 Mechanical Wear . 76 3.2.2 Adhesive Wear . 76 3.2.3 Diffusion Wear . 77 3.2.4 Chemical Wear . 78 3.2.5 Thermoelectric Wear 78 3.3 Tool Usability and Its Relationship with Cutting Parameters 79 3.3.1 Tool Life 79 3.3.2 Tool Life Equation 79 3.3.3 Tool Breakage 83 3.4 Modeling of Tool Wear 83 3.4.1 Abrasive Wear Rate Model . 84 3.4.2 Adhesive Wear Rate Model 85 3.4.3 Diffusion Wear Rate Model 86 3.4.4 Comprehensive Wear Rate Model . 87 3.4.5 Intelligent Tool Wear Model 88Contents xiii 3.5 Tool Wear Modeling in High-Speed Milling 89 3.5.1 Tool Flank Wear Conditions 89 3.5.2 Modeling of Tool Flank Wear . 90 3.5.3 Generalization of the Tool Wear Model 92 3.5.4 Analysis of Tool Wear Model . 95 References 100 4 Mathematical Foundations of Machining System Monitoring 103 4.1 Machining System Monitoring . 103 4.1.1 The Content of Machining System Monitoring 103 4.1.2 The System of Machining Process Monitoring 104 4.2 The Content of the Machining Process Monitoring System . 107 4.2.1 Signal Detection 107 4.2.2 Feature Extraction 107 4.2.3 State Recognition . 108 4.2.4 Decision-Making and Control 108 4.3 The Methods of Machining Process Monitoring . 109 4.3.1 Introduction 109 4.3.2 Stochastic Process Based Methods 110 4.4 Parameter Estimation Methods . 112 4.4.1 Least Square Estimation . 113 4.4.2 Yule-Walker Estimation . 114 4.4.3 Maximum Likelihood Estimate . 115 4.5 Time Series Analysis in Condition Monitoring 116 4.5.1 The Auto-Regression Model AR(N) . 116 4.5.2 The Auto Regression Moving Average Model ARMA(n, m) 117 4.6 The Machining State Description . 119 4.6.1 Typical Anomaly State of the Machining Process 120 4.6.2 Process Model Based State Feature Extraction 121 4.7 Identification of Machining Process . 123 4.7.1 Overview of Process Modeling . 123 4.7.2 Model of Machining Process and Identification Method 124 4.7.3 The Time Series Identification of the Machining State 127 4.7.4 Identification of the Cutting Force . 129 4.7.5 Neural Network Identification of Machining Process 130 4.8 The Common Measurement Methods and Characteristics 132 References 136xiv Contents 5 The Smart Machining System Monitoring from Machine Learning View 139 5.1 The Condition Monitoring Methods . 139 5.1.1 Empirical Analysis 139 5.1.2 Statistical Method 140 5.1.3 Intelligent Method 143 5.2 Smart Machining System Monitoring (MSM) as a Machine Learning Problem 144 5.2.1 Feature 145 5.2.2 State 145 5.2.3 Classifier 146 5.3 The MSM System Content . 146 5.3.1 Signal Preprocessing 146 5.3.2 Feature Extraction and Selection 148 5.3.3 State Classification 150 5.4 Feature Selection Method . 150 5.4.1 Effective Criteria for Monitoring Features 151 5.4.2 Optimal Monitoring Feature Group Selection . 154 5.4.3 The Bidirectional Search Algorithm for Feature Selection 156 5.5 Machine Learning Method . 157 5.5.1 Bayesian Classifier 157 5.5.2 Fisher Linear Discriminant . 158 5.5.3 Principal Components Analysis . 159 5.5.4 Kernel Principal Components Analysis 159 5.5.5 Support Vector Machines 161 5.5.6 Artificial Neural Network (ANN) . 163 5.5.7 K-Nearest Neighbor (KNN) 164 5.5.8 Case Study: MSM with Self-Organizing Map (SOM) 165 5.6 Deep Learning . 168 5.6.1 Introduction to Deep Learning 168 5.6.2 Sparse Autoencoder (AE) 170 5.6.3 Deep Belief Neural Network (DBN) . 174 5.6.4 Convolution Neural Network (CNN) . 178 5.6.5 Recurrent Neural Network (RNN) . 181 5.6.6 Challenges of Deep Learning Approaches in MSM Process Monitoring . 187 References 188 6 Signal Processing for Machining Process Modeling and Condition Monitoring . 191 6.1 Signal Processing in Condition Monitoring . 191 6.1.1 Overview of Condition Monitoring 191 6.1.2 Signal Processing Issues in Condition Monitoring . 192Contents xv 6.2 Signal Space, Linear System, and Fourier Transform 193 6.2.1 Signal Spaces and Inner Product 193 6.2.2 Fourier Transform 195 6.2.3 Linear System, Sampling Theorem, and Convolution 195 6.3 Spectrum Analysis of Machining Signals 197 6.3.1 The Spectrum of Machining Signals . 197 6.3.2 Spectrum Characteristics of Stochastic Signals 199 6.4 Correlation Analysis 202 6.4.1 Autocorrelation Function 202 6.4.2 Cross-Correlation Function . 203 6.5 Common Signal Features in Time and Frequency Domain 204 6.5.1 Feature Parameters in the Time Domain 204 6.5.2 Feature Parameters in the Frequency Domain . 207 6.6 Wavelet Analysis . 209 6.6.1 Limitation of Fourier Methods 209 6.6.2 Continuous Wavelet Analysis (CWT) and Its Time–Frequency Properties 211 6.6.3 Discrete Wavelet Transform and Its Implementation . 214 6.6.4 Wavelet Basis Function 217 6.6.5 Wavelet Packets Decomposition 221 6.6.6 Some Remarks on Wavelet Transform . 222 6.7 Sparse Decomposition of Signals . 226 6.7.1 Compressive Sensing 226 6.7.2 Sparse Decomposition Over Pre-defined Dictionaries 227 6.7.3 Greedy Algorithms 229 6.7.4 Dictionary Learning for Redundant Representation 232 References 233 7 Tool Condition Monitoring with Sparse Decomposition . 235 7.1 Introduction . 235 7.2 Sparse Coding for Denoising (Heavy Non-Gaussian Noise Separation) 237 7.2.1 Introduction 237 7.2.2 Noise Properties in Micro-milling . 238 7.2.3 Sparse Representation in the Time–Frequency Domain . 240 7.2.4 Sparse Representation as a Convex Optimization Problem . 241 7.2.5 Case Studies . 243 7.3 Sparse Representation for Tool State Estimation 249 7.3.1 Sparse Coding of Wavelet Packet Decomposition Coefficients 250xvi Contents 7.3.2 The Discriminant Dictionary Learning . 252 7.3.3 Fast Tool State Estimation Without Signal Reconstruction . 254 7.3.4 Experimental Validation . 255 7.3.5 Results and Discussions . 257 References 264 8 Machine Vision Based Smart Machining System Monitoring 267 8.1 Machine Vision System for Machining Process Monitoring . 267 8.1.1 Introduction 267 8.1.2 The State-of-the-Art . 268 8.2 Digital Image Acquisition and Representation 271 8.2.1 Image Acquisition of the Monitored Objects 271 8.2.2 CCD Sensor . 272 8.2.3 CMOS Sensor 273 8.2.4 Representation of Digital Images 273 8.2.5 Digital Image Processing 275 8.3 Machine Vision System for Micro Milling Tool Condition Monitoring 277 8.3.1 The Micro Milling Tool Condition Monitoring 277 8.3.2 Tool Wear Inspection System . 279 8.3.3 Tool Wear Inspection Method 282 8.3.4 Experimental Verification 288 8.3.5 Conclusions 292 References 293 9 Tool Wear Monitoring with Hidden Markov Models 297 9.1 Introduction . 297 9.2 HMM Based Methods 299 9.2.1 Hidden Markov Models 299 9.2.2 Three Problems of Hidden Markov Models . 300 9.3 Hidden Markov Models Based Tool Condition Monitoring . 301 9.3.1 HMM Description of Tool Wear Process and Monitoring . 301 9.3.2 The Framework of HMMs for TCM . 303 9.3.3 Hidden Markov Model Selection: Continuous Left–Right HMMs 303 9.3.4 Selection of the Number of Gaussian Mixture Components . 306 9.3.5 On the Number of Hidden States in Each HMM . 307 9.3.6 Estimation of the HMM Parameters for Tool Wear Classification 308 9.3.7 Tool State Estimation with HMMs 310 9.4 Experimental Verifications . 311 9.4.1 Experiment Setup . 311 9.4.2 HMM Training for TCM . 312Contents xvii 9.4.3 HMM for Tool Wear State Estimation 312 9.4.4 Moving Average for Tool Wear State Estimation Smoothing . 314 9.4.5 On the Generalization of the HMM-Based Algorithm for TCM . 315 9.5 Diagnosis and Prognosis of Tool Life with Hidden Semi-Markov Model 317 9.5.1 Hidden Semi-Markov Model for Degradation Process Modeling . 318 9.5.2 On-Line Health Monitoring via HSMM 320 9.6 Experimental Validation . 326 9.6.1 Case Study 326 9.6.2 Feature Extraction and Quantization . 327 9.6.3 Training of HSMM for Tool Wear Monitoring 328 9.6.4 Diagnosis and Prognosis Results 331 References 335 10 Sensor Fusion in Machining System Monitoring 339 10.1 Multi-sensor Information Fusion Principle . 339 10.2 Multi-sensor Information Fusion with Neural Networks 340 10.3 Sensor Fusion with Deep Learning 344 10.3.1 Problem Formulation 346 10.3.2 The Unit of Pyramid LSTM Auto-encoder 347 10.3.3 The Structure of the Pyramid LSTM Auto-encoder 350 10.3.4 The Training Method 351 10.3.5 Computational Efficiency 352 10.3.6 Experimental Validation . 353 10.3.7 Conclusion 359 References 359 11 Big Data Oriented Smart Tool Condition Monitoring System 361 11.1 The Big Data Issues in Manufacturing . 361 11.2 The Big Data Analytics in Smart Machining System . 362 11.2.1 The Big Data Challenges and Motivation . 362 11.2.2 Related Works 363 11.3 The Framework of Big Data Oriented Smart Machining Monitoring System . 365 11.3.1 The Monitoring System Architecture 365 11.3.2 The Big Data-Oriented Formulation of TCM 366 11.4 The Functional Modules and Case Study . 366 11.4.1 Sparse Coding Based Data Pre-processing 367 11.4.2 In-process Workpiece Integrity Monitoring . 369 11.4.3 Heterogeneous Data Fusion and Deep Learning . 370 11.4.4 Intelligent Tool Monitoring and Wear Compensation 372 11.5 Case Study 375xviii Contents 11.6 Summary . 379 References 379 12 The Cyber-Physical Production System of Smart Machining System . 383 12.1 Introduction . 383 12.2 The Cyber-Physical System in Manufacturing 383 12.2.1 The Definition 383 12.2.2 The CPS Features . 384 12.3 The CPS of Machine Tool and Machining Process 386 12.3.1 The State-of-the-Art . 386 12.3.2 The CPS of Machine Tool 388 12.3.3 The CPS of Machining Process . 389 12.4 A CPPS Framework of Smart Machining Monitoring System . 393 12.4.1 Induction 393 12.4.2 The Smart CNC Machining Monitoring CPPS Structure 395 12.4.3 Case Studies . 398 12.5 Summary . 404 References . 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