谁有688(I)的英文版??????????????????

koenig 2001-09-23 11:53:41
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wilddragon 2001-09-24
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up
wilddragon 2001-09-24
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up
koenig 2001-09-23
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no. I have found nothing:(
koenig 2001-09-23
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thank you so much:)
unsounder 2001-09-23
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Check it with Computer Gaming World's website
koenig 2001-09-23
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官方攻略要19。95美圆,我不知道在哪里能买到,或是下栽
unsounder 2001-09-23
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对,说明书有删节.没关系,删除的部分并不是很难.我不就这么玩下来了嘛.
koenig 2001-09-23
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说明书是英文版吗????????????????

中文的有删节,我要英文的,你能帮助我吗????????????????
unsounder 2001-09-23
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我有!是正版
书的目录 Contents Website viii Acknowledgments ix Notation xiii 1 Introduction 1 1.1 Who Should Read This Book? . . . . . . . . . . . . . . . . . . . . 8 1.2 Historical Trends in Deep Learning . . . . . . . . . . . . . . . . . 12 I Applied Math and Machine Learning Basics 27 2 Linear Algebra 29 2.1 Scalars, Vectors, Matrices and Tensors . . . . . . . . . . . . . . . 29 2.2 Multiplying Matrices and Vectors . . . . . . . . . . . . . . . . . . 32 2.3 Identity and Inverse Matrices . . . . . . . . . . . . . . . . . . . . 34 2.4 Linear Dependence and Span . . . . . . . . . . . . . . . . . . . . 35 2.5 Norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 2.6 Special Kinds of Matrices and Vectors . . . . . . . . . . . . . . . 38 2.7 Eigendecomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 40 2.8 Singular Value Decomposition . . . . . . . . . . . . . . . . . . . . 42 2.9 The Moore-Penrose Pseudoinverse . . . . . . . . . . . . . . . . . . 43 2.10 The Trace Operator . . . . . . . . . . . . . . . . . . . . . . . . . 44 2.11 The Determinant . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 2.12 Example: Principal Components Analysis . . . . . . . . . . . . . 45 3 Probability and Information Theory 51 3.1 Why Probability? . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 i CONTENTS 3.2 Random Variables . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.3 Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . 54 3.4 Marginal Probability . . . . . . . . . . . . . . . . . . . . . . . . . 56 3.5 Conditional Probability . . . . . . . . . . . . . . . . . . . . . . . 57 3.6 The Chain Rule of Conditional Probabilities . . . . . . . . . . . . 57 3.7 Independence and Conditional Independence . . . . . . . . . . . . 58 3.8 Expectation, Variance and Covariance . . . . . . . . . . . . . . . 58 3.9 Common Probability Distributions . . . . . . . . . . . . . . . . . 60 3.10 Useful Properties of Common Functions . . . . . . . . . . . . . . 65 3.11 Bayes’ Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 3.12 Technical Details of Continuous Variables . . . . . . . . . . . . . 69 3.13 Information Theory . . . . . . . . . . . . . . . . . . . . . . . . . . 71 3.14 Structured Probabilistic Models . . . . . . . . . . . . . . . . . . . 73 4 Numerical Computation 78 4.1 Overflow and Underflow . . . . . . . . . . . . . . . . . . . . . . . 78 4.2 Poor Conditioning . . . . . . . . . . . . . . . . . . . . . . . . . . 80 4.3 Gradient-Based Optimization . . . . . . . . . . . . . . . . . . . . 80 4.4 Constrained Optimization . . . . . . . . . . . . . . . . . . . . . . 91 4.5 Example: Linear Least Squares . . . . . . . . . . . . . . . . . . . 94 5 Machine Learning Basics 96 5.1 Learning Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 97 5.2 Capacity, Overfitting and Underfitting . . . . . . . . . . . . . . . 108 5.3 Hyperparameters and Validation Sets . . . . . . . . . . . . . . . . 118 5.4 Estimators, Bias and Variance . . . . . . . . . . . . . . . . . . . . 120 5.5 Maximum Likelihood Estimation . . . . . . . . . . . . . . . . . . 129 5.6 Bayesian Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . 133 5.7 Supervised Learning Algorithms . . . . . . . . . . . . . . . . . . . 137 5.8 Unsupervised Learning Algorithms . . . . . . . . . . . . . . . . . 142 5.9 Stochastic Gradient Descent . . . . . . . . . . . . . . . . . . . . . 149 5.10 Building a Machine Learning Algorithm . . . . . . . . . . . . . . 151 5.11 Challenges Motivating Deep Learning . . . . . . . . . . . . . . . . 152 II Deep Networks: Modern Practices 162 6 Deep Feedforward Networks 164 6.1 Example: Learning XOR . . . . . . . . . . . . . . . . . . . . . . . 167 6.2 Gradient-Based Learning . . . . . . . . . . . . . . . . . . . . . . . 172 ii CONTENTS 6.3 Hidden Units . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 6.4 Architecture Design . . . . . . . . . . . . . . . . . . . . . . . . . . 193 6.5 Back-Propagation and Other Differentiation Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 6.6 Historical Notes . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220 7 Regularization for Deep Learning 224 7.1 Parameter Norm Penalties . . . . . . . . . . . . . . . . . . . . . . 226 7.2 Norm Penalties as Constrained Optimization . . . . . . . . . . . . 233 7.3 Regularization and Under-Constrained Problems . . . . . . . . . 235 7.4 Dataset Augmentation . . . . . . . . . . . . . . . . . . . . . . . . 236 7.5 Noise Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 7.6 Semi-Supervised Learning . . . . . . . . . . . . . . . . . . . . . . 240 7.7 Multitask Learning . . . . . . . . . . . . . . . . . . . . . . . . . . 241 7.8 Early Stopping . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 7.9 Parameter Tying and Parameter Sharing . . . . . . . . . . . . . . 249 7.10 Sparse Representations . . . . . . . . . . . . . . . . . . . . . . . . 251 7.11 Bagging and Other Ensemble Methods . . . . . . . . . . . . . . . 253 7.12 Dropout . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255 7.13 Adversarial Training . . . . . . . . . . . . . . . . . . . . . . . . . 265 7.14 Tangent Distance, Tangent Prop and Manifold Tangent Classifier . . . . . . . . . . . . . . . . . . . . . . . . . . . 267 8 Optimization for Training Deep Models 271 8.1 How Learning Differs from Pure Optimization . . . . . . . . . . . 272 8.2 Challenges in Neural Network Optimization . . . . . . . . . . . . 279 8.3 Basic Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . 290 8.4 Parameter Initialization Strategies . . . . . . . . . . . . . . . . . 296 8.5 Algorithms with Adaptive Learning Rates . . . . . . . . . . . . . 302 8.6 Approximate Second-Order Methods . . . . . . . . . . . . . . . . 307 8.7 Optimization Strategies and Meta-Algorithms . . . . . . . . . . . 313 9 Convolutional Networks 326 9.1 The Convolution Operation . . . . . . . . . . . . . . . . . . . . . 327 9.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 329 9.3 Pooling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335 9.4 Convolution and Pooling as an Infinitely Strong Prior . . . . . . . 339 9.5 Variants of the Basic Convolution Function . . . . . . . . . . . . 342 9.6 Structured Outputs . . . . . . . . . . . . . . . . . . . . . . . . . . 352 9.7 Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354 iii CONTENTS 9.8 Efficient Convolution Algorithms . . . . . . . . . . . . . . . . . . 356 9.9 Random or Unsupervised Features . . . . . . . . . . . . . . . . . 356 9.10 The Neuroscientific Basis for Convolutional Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 358 9.11 Convolutional Networks and the History of Deep Learning . . . . 365 10 Sequence Modeling: Recurrent and Recursive Nets 367 10.1 Unfolding Computational Graphs . . . . . . . . . . . . . . . . . . 369 10.2 Recurrent Neural Networks . . . . . . . . . . . . . . . . . . . . . 372 10.3 Bidirectional RNNs . . . . . . . . . . . . . . . . . . . . . . . . . . 388 10.4 Encoder-Decoder Sequence-to-Sequence Architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 390 10.5 Deep Recurrent Networks . . . . . . . . . . . . . . . . . . . . . . 392 10.6 Recursive Neural Networks . . . . . . . . . . . . . . . . . . . . . . 394 10.7 The Challenge of Long-Term Dependencies . . . . . . . . . . . . . 396 10.8 Echo State Networks . . . . . . . . . . . . . . . . . . . . . . . . . 399 10.9 Leaky Units and Other Strategies for Multiple Time Scales . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 402 10.10 The Long Short-Term Memory and Other Gated RNNs . . . . . . 404 10.11 Optimization for Long-Term Dependencies . . . . . . . . . . . . . 408 10.12 Explicit Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . 412 11 Practical Methodology 416 11.1 Performance Metrics . . . . . . . . . . . . . . . . . . . . . . . . . 417 11.2 Default Baseline Models . . . . . . . . . . . . . . . . . . . . . . . 420 11.3 Determining Whether to Gather More Data . . . . . . . . . . . . 421 11.4 Selecting Hyperparameters . . . . . . . . . . . . . . . . . . . . . . 422 11.5 Debugging Strategies . . . . . . . . . . . . . . . . . . . . . . . . . 431 11.6 Example: Multi-Digit Number Recognition . . . . . . . . . . . . . 435 12 Applications 438 12.1 Large-Scale Deep Learning . . . . . . . . . . . . . . . . . . . . . . 438 12.2 Computer Vision . . . . . . . . . . . . . . . . . . . . . . . . . . . 447 12.3 Speech Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . 453 12.4 Natural Language Processing . . . . . . . . . . . . . . . . . . . . 456 12.5 Other Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 473 iv CONTENTS III Deep Learning Research 482 13 Linear Factor Models 485 13.1 Probabilistic PCA and Factor Analysis . . . . . . . . . . . . . . . 486 13.2 Independent Component Analysis (ICA) . . . . . . . . . . . . . . 487 13.3 Slow Feature Analysis . . . . . . . . . . . . . . . . . . . . . . . . 489 13.4 Sparse Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 492 13.5 Manifold Interpretation of PCA . . . . . . . . . . . . . . . . . . . 496 14 Autoencoders 499 14.1 Undercomplete Autoencoders . . . . . . . . . . . . . . . . . . . . 500 14.2 Regularized Autoencoders . . . . . . . . . . . . . . . . . . . . . . 501 14.3 Representational Power, Layer Size and Depth . . . . . . . . . . . 505 14.4 Stochastic Encoders and Decoders . . . . . . . . . . . . . . . . . . 506 14.5 Denoising Autoencoders . . . . . . . . . . . . . . . . . . . . . . . 507 14.6 Learning Manifolds with Autoencoders . . . . . . . . . . . . . . . 513 14.7 Contractive Autoencoders . . . . . . . . . . . . . . . . . . . . . . 518 14.8 Predictive Sparse Decomposition . . . . . . . . . . . . . . . . . . 521 14.9 Applications of Autoencoders . . . . . . . . . . . . . . . . . . . . 522 15 Representation Learning 524 15.1 Greedy Layer-Wise Unsupervised Pretraining . . . . . . . . . . . 526 15.2 Transfer Learning and Domain Adaptation . . . . . . . . . . . . . 534 15.3 Semi-Supervised Disentangling of Causal Factors . . . . . . . . . 539 15.4 Distributed Representation . . . . . . . . . . . . . . . . . . . . . . 544 15.5 Exponential Gains from Depth . . . . . . . . . . . . . . . . . . . 550 15.6 Providing Clues to Discover Underlying Causes . . . . . . . . . . 552 16 Structured Probabilistic Models for Deep Learning 555 16.1 The Challenge of Unstructured Modeling . . . . . . . . . . . . . . 556 16.2 Using Graphs to Describe Model Structure . . . . . . . . . . . . . 560 16.3 Sampling from Graphical Models . . . . . . . . . . . . . . . . . . 577 16.4 Advantages of Structured Modeling . . . . . . . . . . . . . . . . . 579 16.5 Learning about Dependencies . . . . . . . . . . . . . . . . . . . . 579 16.6 Inference and Approximate Inference . . . . . . . . . . . . . . . . 580 16.7 The Deep Learning Approach to Structured Probabilistic Models . . . . . . . . . . . . . . . . . . . . . . . . . 581 17 Monte Carlo Methods 587 17.1 Sampling and Monte Carlo Methods . . . . . . . . . . . . . . . . 587 v CONTENTS 17.2 Importance Sampling . . . . . . . . . . . . . . . . . . . . . . . . . 589 17.3 Markov Chain Monte Carlo Methods . . . . . . . . . . . . . . . . 592 17.4 Gibbs Sampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . 596 17.5 The Challenge of Mixing between Separated Modes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 597 18 Confronting the Partition Function 603 18.1 The Log-Likelihood Gradient . . . . . . . . . . . . . . . . . . . . 604 18.2 Stochastic Maximum Likelihood and Contrastive Divergence . . . 605 18.3 Pseudolikelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . 613 18.4 Score Matching and Ratio Matching . . . . . . . . . . . . . . . . 615 18.5 Denoising Score Matching . . . . . . . . . . . . . . . . . . . . . . 617 18.6 Noise-Contrastive Estimation . . . . . . . . . . . . . . . . . . . . 618 18.7 Estimating the Partition Function . . . . . . . . . . . . . . . . . . 621 19 Approximate Inference 629 19.1 Inference as Optimization . . . . . . . . . . . . . . . . . . . . . . 631 19.2 Expectation Maximization . . . . . . . . . . . . . . . . . . . . . . 632 19.3 MAP Inference and Sparse Coding . . . . . . . . . . . . . . . . . 633 19.4 Variational Inference and Learning . . . . . . . . . . . . . . . . . 636 19.5 Learned Approximate Inference . . . . . . . . . . . . . . . . . . . 648 20 Deep Generative Models 651 20.1 Boltzmann Machines . . . . . . . . . . . . . . . . . . . . . . . . . 651 20.2 Restricted Boltzmann Machines . . . . . . . . . . . . . . . . . . . 653 20.3 Deep Belief Networks . . . . . . . . . . . . . . . . . . . . . . . . . 657 20.4 Deep Boltzmann Machines . . . . . . . . . . . . . . . . . . . . . . 660 20.5 Boltzmann Machines for Real-Valued Data . . . . . . . . . . . . . 673 20.6 Convolutional Boltzmann Machines . . . . . . . . . . . . . . . . . 679 20.7 Boltzmann Machines for Structured or Sequential Outputs . . . . 681 20.8 Other Boltzmann Machines . . . . . . . . . . . . . . . . . . . . . 683 20.9 Back-Propagation through Random Operations . . . . . . . . . . 684 20.10 Directed Generative Nets . . . . . . . . . . . . . . . . . . . . . . . 688 20.11 Drawing Samples from Autoencoders . . . . . . . . . . . . . . . . 707 20.12 Generative Stochastic Networks . . . . . . . . . . . . . . . . . . . 710 20.13 Other Generation Schemes . . . . . . . . . . . . . . . . . . . . . . 712 20.14 Evaluating Generative Models . . . . . . . . . . . . . . . . . . . . 713 20.15 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 716 Bibliography 717 vi CONTENTS Index 774
Chapter 1 Materials: Structure, Properties, and Performance 1 1.1 Introduction 1 1.2 Monolithic, Composite, and Hierarchical Materials 3 1.3 Structure of Materials 15 1.3.1 Crystal Structures 16 1.3.2 Metals 19 1.3.3 Ceramics 25 1.3.4 Glasses 30 1.3.5 Polymers 31 1.3.6 Liquid Crystals 39 1.3.7 Biological Materials and Biomaterials 40 1.3.8 Porous and Cellular Materials 44 1.3.9 Nano- and Microstructure of Biological Materials 45 1.3.10 The Sponge Spicule: An Example of a Biological Material 56 1.3.11 Active (or Smart) Materials 57 1.3.12 Electronic Materials 58 1.3.13 Nanotechnology 60 1.4 Strength of Real Materials 61 Suggested Reading 64 Exercises 65 Chapter 2 Elasticity and Viscoelasticity 71 2.1 Introduction 71 2.2 Longitudinal Stress and Strain 72 2.3 Strain Energy (or Deformation Energy) Density 77 2.4 Shear Stress and Strain 80 2.5 Poisson’s Ratio 83 2.6 More Complex States of Stress 85 2.7 Graphical Solution of a Biaxial State of Stress: the Mohr Circle 89 2.8 Pure Shear: Relationship between G and E 95 2.9 Anisotropic Effects 96 2.10 Elastic Properties of Polycrystals 107 2.11 Elastic Properties of Materials 110 2.11.1 Elastic Properties of Metals 111 2.11.2 Elastic Properties of Ceramics 111 2.11.3 Elastic Properties of Polymers 116 2.11.4 Elastic Constants of Unidirectional Fiber Reinforced Composite 117 2.12 Viscoelasticity 120 2.12.1 Storage and Loss Moduli 124 2.13 Rubber Elasticity 126 2.14 Mooney--Rivlin Equation 131 2.15 Elastic Properties of Biological Materials 134 2.15.1 Blood Vessels 134 2.15.2 Articular Cartilage 137 2.15.3 Mechanical Properties at the Nanometer Level 140 2.16 Elastic Properties of Electronic Materials 143 2.17 Elastic Constants and Bonding 145 Suggested Reading 155 Exercises 155 Chapter 3 Plasticity 161 3.1 Introduction 161 3.2 Plastic Deformation in Tension 163 3.2.1 Tensile Curve Parameters 171 3.2.2 Necking 172 3.2.3 Strain Rate Effects 176 3.3 Plastic Deformation in Compression Testing 183 3.4 The Bauschunger Effect 187 3.5 Plastic Deformation of Polymers 188 3.5.1 Stress--Strain Curves 188 3.5.2 Glassy Polymers 189 3.5.3 Semicrystalline Polymers 190 3.5.4 Viscous Flow 191 3.5.5 Adiabatic Heating 192 3.6 Plastic Deformation of Glasses 193 3.6.1 Microscopic Deformation Mechanism 195 3.6.2 Temperature Dependence and Viscosity 197 3.7 Flow, Yield, and Failure Criteria 199 3.7.1 Maximum-Stress Criterion (Rankine) 200 3.7.2 Maximum-Shear-Stress Criterion (Tresca) 200 3.7.3 Maximum-Distortion-Energy Criterion (von Mises) 201 3.7.4 Graphical Representation and Experimental Verification of Rankine, Tresca, and von Mises Criteria 201 3.7.5 Failure Criteria for Brittle Materials 205 3.7.6 Yield Criteria for Ductile Polymers 209 3.7.7 Failure Criteria for Composite Materials 211 3.7.8 Yield and Failure Criteria for Other Anisotropic Materials 213 3.8 Hardness 214 3.8.1 Macroindentation Tests 216 3.8.2 Microindentation Tests 221 3.8.3 Nanoindentation 225 3.9 Formability: Important Parameters 229 3.9.1 Plastic Anisotropy 231 3.9.2 Punch--Stretch Tests and Forming-Limit Curves (or Keeler--Goodwin Diagrams) 232 3.10 Muscle Force 237 3.11 Mechanical Properties of Some Biological Materials 241 Suggested Reading 245 Exercises 246 Chapter 4 Imperfections: Point and Line Defects 251 4.1 Introduction 251 4.2 Theoretical Shear Strength 252 4.3 Atomic or Electronic Point Defects 254 4.3.1 Equilibrium Concentration of Point Defects 256 4.3.2 Production of Point Defects 259 4.3.3 Effect of Point Defects on Mechanical Properties 260 4.3.4 Radiation Damage 261 4.3.5 Ion Implantation 265 4.4 Line Defects 266 4.4.1 Experimental Observation of Dislocations 270 4.4.2 Behavior of Dislocations 273 4.4.3 Stress Field Around Dislocations 275 4.4.4 Energy of Dislocations 278 4.4.5 Force Required to Bow a Dislocation 282 4.4.6 Dislocations in Various Structures 284 4.4.7 Dislocations in Ceramics 293 4.4.8 Sources of Dislocations 298 4.4.9 Dislocation Pileups 302 4.4.10 Intersection of Dislocations 304 4.4.11 Deformation Produced by Motion of Dislocations (Orowan’s Equation) 306 4.4.12 The Peierls--Nabarro Stress 309 4.4.13 The Movement of Dislocations: Temperature and Strain Rate Effects 310 4.4.14 Dislocations in Electronic Materials 313 Suggested Reading 316 Exercises 317 Chapter 5 Imperfections: Interfacial and Volumetric Defects 321 5.1 Introduction 321 5.2 Grain Boundaries 321 5.2.1 Tilt and Twist Boundaries 326 5.2.2 Energy of a Grain Boundary 328 5.2.3 Variation of Grain-Boundary Energy with Misorientation 330 5.2.4 Coincidence Site Lattice (CSL) Boundaries 332 5.2.5 Grain-Boundary Triple Junctions 334 5.2.6 Grain-Boundary Dislocations and Ledges 334 5.2.7 Grain Boundaries as a Packing of Polyhedral Units 336 5.3 Twinning and Twin Boundaries 336 5.3.1 Crystallography and Morphology 337 5.3.2 Mechanical Effects 341 5.4 Grain Boundaries in Plastic Deformation (Grain-size Strengthening) 345 5.4.1 Hall--Petch Theory 348 5.4.2 Cottrell’s Theory 349 5.4.3 Li’s Theory 350 5.4.4 Meyers--Ashworth Theory 351 5.5 Other Internal Obstacles 353 5.6 Nanocrystalline Materials 355 5.7 Volumetric or Tridimensional Defects 358 5.8 Imperfections in Polymers 361 Suggested Reading 364 Exercises 364 Chapter 6 Geometry of Deformation and Work-Hardening 369 6.1 Introduction 369 6.2 Geometry of Deformation 373 6.2.1 Stereographic Projections 373 6.2.2 Stress Required for Slip 374 6.2.3 Shear Deformation 380 6.2.4 Slip in Systems and Work-Hardening 381 6.2.5 Independent Slip Systems in Polycrystals 384 6.3 Work-Hardening in Polycrystals 384 6.3.1 Taylor’s Theory 386 6.3.2 Seeger’s Theory 388 6.3.3 Kuhlmann--Wilsdorf’s Theory 388 6.4 Softening Mechanisms 392 6.5 Texture Strengthening 395 Suggested Reading 399 Exercises 399 Chapter 7 Fracture: Macroscopic Aspects 404 7.1 Introduction 404 7.2 Theorectical Tensile Strength 406 7.3 Stress Concentration and Griffith Criterion of Fracture 409 7.3.1 Stress Concentrations 409 7.3.2 Stress Concentration Factor 409 7.4 Griffith Criterion 416 7.5 Crack Propagation with Plasticity 419 7.6 Linear Elastic Fracture Mechanics 421 7.6.1 Fracture Toughness 422 7.6.2 Hypotheses of LEFM 423 7.6.3 Crack-Tip Separation Modes 423 7.6.4 Stress Field in an Isotropic Material in the Vicinity of a Crack Tip 424 7.6.5 Details of the Crack-Tip Stress Field in Mode I 425 7.6.6 Plastic-Zone Size Correction 428 7.6.7 Variation in Fracture Toughness with Thickness 431 7.7 Fracture Toughness Parameters 434 7.7.1 Crack Extension Force G 434 7.7.2 Crack Opening Displacement 437 7.7.3 J Integral 440 7.7.4 R Curve 443 7.7.5 Relationships among Different Fracture Toughness Parameters 444 7.8 Importance of K I c in Practice 445 7.9 Post-Yield Fracture Mechanics 448 7.10 Statistical Analysis of Failure Strength 449 Appendix: Stress Singularity at Crack Tip 458 Suggested Reading 460 Exercises 460 Chapter 8 Fracture: Microscopic Aspects 466 8.1 Introduction 466 8.2 Facture in Metals 468 8.2.1 Crack Nucleation 468 8.2.2 Ductile Fracture 469 8.2.3 Brittle, or Cleavage, Fracture 480 8.3 Facture in Ceramics 487 8.3.1 Microstructural Aspects 487 8.3.2 Effect of Grain Size on Strength of Ceramics 494 8.3.3 Fracture of Ceramics in Tension 496 8.3.4 Fracture in Ceramics Under Compression 499 8.3.5 Thermally Induced Fracture in Ceramics 504 8.4 Fracture in Polymers 507 8.4.1 Brittle Fracture 507 8.4.2 Crazing and Shear Yielding 508 8.4.3 Fracture in Semicrystalline and Crystalline Polymers 512 8.4.4 Toughness of Polymers 513 8.5 Fracture and Toughness of Biological Materials 517 8.6 Facture Mechanism Maps 521 Suggested Reading 521 Exercises 521 Chapter 9 Fracture Testing 525 9.1 Introduction 525 9.2 Impact Testing 525 9.2.1 Charpy Impact Test 526 9.2.2 Drop-Weight Test 529 9.2.3 Instrumented Charpy Impact Test 531 9.3 Plane-Strain Fracture Toughness Test 532 9.4 Crack Opening Displacement Testing 537 9.5 J-Integral Testing 538 9.6 Flexure Test 540 9.6.1 Three-Point Bend Test 541 9.6.2 Four-Point Bending 542 9.6.3 Interlaminar Shear Strength Test 543 9.7 Fracture Toughness Testing of Brittle Materials 545 9.7.1 Chevron Notch Test 547 9.7.2 Indentation Methods for Determining Toughness 549 9.8 Adhesion of Thin Films to Substrates 552 Suggested Reading 553 Exercises 553 Chapter 10 Solid Solution, Precipitation, and Dispersion Strengthening 558 10.1 Introduction 558 10.2 Solid-Solution Strengthening 559 10.2.1 Elastic Interaction 560 10.2.2 Other Interactions 564 10.3 Mechanical Effects Associated with Solid Solutions 564 10.3.1 Well-Defined Yield Point in the Stress--Strain Curves 565 10.3.2 Plateau in the Stress--Strain Curve and L¨uders Band 566 10.3.3 Strain Aging 567 10.3.4 Serrated Stress--Strain Curve 568 10.3.5 Snoek Effect 569 10.3.6 Blue Brittleness 570 10.4 Precipitation- and Dispersion-Hardening 571 10.5 Dislocation--Precipitate Interaction 579 10.6 Precipitation in Microalloyed Steels 585 10.7 Dual-Phase Steels 590 Suggested Reading 590 Exercises 591 Chapter 11 Martensitic Transformation 594 11.1 Introduction 594 11.2 Structures and Morphologies of Martensite 594 11.3 Strength of Martensite 600 11.4 Mechanical Effects 603 11.5 Shape-Memory Effect 608 11.5.1 Shape-Memory Effect in Polymers 614 11.6 Martensitic Transformation in Ceramics 614 Suggested Reading 618 Exercises 619 Chapter 12 Special Materials: Intermetallics and Foams 621 12.1 Introduction 621 12.2 Silicides 621 12.3 Ordered Intermetallics 622 12.3.1 Dislocation Structures in Ordered Intermetallics 624 12.3.2 Effect of Ordering on Mechanical Properties 628 12.3.3 Ductility of Intermetallics 634 12.4 Cellular Materials 639 12.4.1 Structure 639 12.4.2 Modeling of the Mechanical Response 639 12.4.3 Comparison of Predictions and Experimental Results 645 12.4.4 Syntactic Foam 645 12.4.5 Plastic Behavior of Porous Materials 646 Suggested Reading 650 Exercises 650 Chapter 13 Creep and Superplasticity 653 13.1 Introduction 653 13.2 Correlation and Extrapolation Methods 659 13.3 Fundamental Mechanisms Responsible for Creep 665 13.4 Diffusion Creep 666 13.5 Dislocation (or Power Law) Creep 670 13.6 Dislocation Glide 673 13.7 Grain-Boundary Sliding 675 13.8 Deformation-Mechanism (Weertman--Ashby) Maps 676 13.9 Creep-Induced Fracture 678 13.10 Heat-Resistant Materials 681 13.11 Creep in Polymers 688 13.12 Diffusion-Related Phenomena in Electronic Materials 695 13.13 Superplasticity 697 Suggested Reading 705 Exercises 705 Chapter 14 Fatigue 713 14.1 Introduction 713 14.2 Fatigue Parameters and S--N (W¨ohler) Curves 714 14.3 Fatigue Strength or Fatigue Life 716 14.4 Effect of Mean Stress on Fatigue Life 719 14.5 Effect of Frequency 721 14.6 Cumulative Damage and Life Exhaustion 721 14.7 Mechanisms of Fatigue 725 14.7.1 Fatigue Crack Nucleation 725 14.7.2 Fatigue Crack Propagation 730 14.8 Linear Elastic Fracture Mechanics Applied to Fatigue 735 14.8.1 Fatigue of Biomaterials 744 14.9 Hysteretic Heating in Fatigue 746 14.10 Environmental Effects in Fatigue 748 14.11 Fatigue Crack Closure 748 14.12 The Two-Parameter Approach 749 14.13 The Short-Crack Problem in Fatigue 750 14.14 Fatigue Testing 751 14.14.1 Conventional Fatigue Tests 751 14.14.2 Rotating Bending Machine 751 14.14.3 Statistical Analysis of S--N Curves 753 14.14.4 Nonconventional Fatigue Testing 753 14.14.5 Servohydraulic Machines 755 14.14.6 Low-Cycle Fatigue Tests 756 14.14.7 Fatigue Crack Propagation Testing 757 Suggested Reading 758 Exercises 759 Chapter 15 Composite Materials 765 15.1 Introduction 765 15.2 Types of Composites 765 15.3 Important Reinforcements and Matrix Materials 767 15.3.1 Microstructural Aspects and Importance of the Matrix 769 15.4 Interfaces in Composites 770 15.4.1 Crystallographic Nature of the Fiber--Matrix Interface 771 15.4.2 Interfacial Bonding in Composites 772 15.4.3 Interfacial Interactions 773 15.5 Properties of Composites 774 15.5.1 Density and Heat Capacity 775 15.5.2 Elastic Moduli 775 15.5.3 Strength 780 15.5.4 Anisotropic Nature of Fiber Reinforced Composites 783 15.5.5 Aging Response of Matrix in MMCs 785 15.5.6 Toughness 785 15.6 Load Transfer from Matrix to Fiber 788 15.6.1 Fiber and Matrix Elastic 789 15.6.2 Fiber Elastic and Matrix Plastic 792 15.7 Fracture in Composites 794 15.7.1 Single and Multiple Fracture 795 15.7.2 Failure Modes in Composites 796 15.8 Some Fundamental Characteristics of Composites 799 15.8.1 Heterogeneity 799 15.8.2 Anisotropy 799 15.8.3 Shear Coupling 801 15.8.4 Statistical Variation in Strength 802 15.9 Functionally Graded Materials 803 15.10 Applications 803 15.10.1 Aerospace Applications 803 15.10.2 Nonaerospace Applications 804 15.11 Laminated Composites 806 Suggested Reading 809 Exercises 810 Chapter 16 Environmental Effects 815 16.1 Introduction 815 16.2 Electrochemical Nature of Corrosion in Metals 815 16.2.1 Galvanic Corrosion 816 16.2.2 Uniform Corrosion 817 16.2.3 Crevice corrosion 817 16.2.4 Pitting Corrosion 818 16.2.5 Intergranular Corrosion 818 16.2.6 Selective leaching 819 16.2.7 Erosion-Corrosion 819 16.2.8 Radiation Damage 819 16.2.9 Stress Corrosion 819 16.3 Oxidation of metals 819 16.4 Environmentally Assisted Fracture in Metals 820 16.4.1 Stress Corrosion Cracking (SCC) 820 16.4.2 Hydrogen Damage in Metals 824 16.4.3 Liquid and Solid Metal Embrittlement 830 16.5 Environmental Effects in Polymers 831 16.5.1 Chemical or Solvent Attack 832 16.5.2 Swelling 832 16.5.3 Oxidation 833 16.5.4 Radiation Damage 834 16.5.5 Environmental Crazing 835 16.5.6 Alleviating the Environmental Damage in Polymers 836 16.6 Environmental Effects in Ceramics 836 16.6.1 Oxidation of Ceramics 839 Suggested Reading 840 Exercises 840 Appendixes 843 Index 851
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