Advances in modeling and simulation in textile engineering : new concepts, methods, and applications /
Veit, Dieter,editor.
Akankwasa, Nicholus Tayari,editor.
Description based on print version record.
Front Cover -- Advances in Modeling and Simulation in Textile Engineering -- The Textile Institute Book Series -- Recently Published and Upcoming Titles in The Textile Institute Book Series: -- Advances in Modeling and Simulation in Textile EngineeringThe Textile Institute Book SeriesNew Concepts, Methods, and Appli ... -- Copyright -- Contents -- Contributors -- Preface -- Acknowledgements -- 1 - Overview of modeling and simulation in textile engineering -- 1.1 Introduction -- 1.1.1 Modeling -- 1.1.2 Simulation -- 1.2 The state of the art -- 1.2.1 Textile engineering process chain -- 1.3 Modeling and simulation in textile engineering -- 1.3.1 Textile materials/processes -- 1.3.2 Textile machinery -- 1.4 Key steps and solution approach -- 1.4.1 Step 1: numerical versus experimental data -- 1.4.2 Step 2: establish important parameters -- 1.4.3 Solution strategy -- identify the model -- 1.4.4 Simulation and postprocessing -- 1.4.5 Validation and results analysis -- 1.5 Typical challenges and limitations -- 1.6 Further reading and reference -- References -- 2 - Modeling classification of textile engineering problems -- 2.1 Introduction -- 2.2 Mathematical modeling -- 2.3 Analytical modeling -- 2.4 Computational and numerical modeling -- 2.4.1 Theoretical and experimental studies of 3-D air flows -- 2.4.2 Numerical modeling of fiber suspension flows -- 2.5 Characterization of 3-D airflow dynamics in spinning technology -- 2.5.1 Modeling the rotor spinning process -- 2.5.2 Dimensional characteristics -- 2.5.2.1 The rotor diameter -- 2.5.2.2 The rotor slide wall angle -- 2.5.2.3 The transfer channel dimensional characteristics -- 2.5.2.4 Other geometrical dimensional characteristics -- 2.5.3 Flow and spinning condition characteristics of the rotor spinning unit -- 2.5.3.1 Velocity analysis -- 2.5.3.2 Negative pressure.
2.5.3.3 Vortical structures and streamlines -- 2.5.3.4 Rotor speed -- 2.6 Characterization of turbulence and Reynolds number for flow-based problems in textile engineering -- 2.7 Overview of turbulence models -- 2.7.1 Classification of turbulent models -- 2.7.1.1 Reynolds-averaged Navier-Stokes models -- 2.7.1.2 Computation of fluctuating quantities -- 2.8 Turbulence models -- 2.8.1 Three main types of turbulence models -- 2.8.2 Options for k-ϵ model -- 2.8.2.1 Mixture turbulence model -- 2.8.2.2 Per-phase turbulence model -- 2.8.2.3 Dispersed turbulence model -- 2.9 Case-based reasoning in textile engineering -- 2.9.1 Case-based reasoning model development -- References -- 3 - Neural networks in textile engineering -- 3.1 Definition of artificial intelligence -- 3.2 A brief history of artificial intelligence -- 3.3 Biological background -- 3.3.1 Buildup of a neuron -- 3.3.2 Information transmission between neurons -- 3.4 Models of artificial neural networks -- 3.4.1 Perceptron -- 3.4.1.1 Learning rule of a perceptron -- 3.4.1.2 Perceptron convergence theorem -- 3.4.1.3 Problem of linear divisibility -- 3.4.2 Adaline and madaline networks -- 3.4.2.1 Adaline network -- 3.4.2.2 Madaline network -- 3.5 Backpropagation algorithm -- 3.5.1 Structure of the net -- 3.5.2 Learning rule -- 3.5.3 Structure and capacity -- 3.5.4 Learning with the backpropagation algorithm -- 3.5.4.1 Initialization of the weight factors -- 3.5.4.2 Problems during the training -- Flat plateau -- Steep crevices -- Missing a minimum -- 3.5.4.3 Optimizing the training -- Learning rate -- Momentum -- Constant factor added to derivative -- Weight decay -- Data submission -- 3.5.5 Determination of suitable training parameters -- 3.5.5.1 Learning rate σ -- 3.5.5.2 Momentum factor -- 3.5.5.3 Multistage training -- 3.5.5.4 Example-texturing process -- Structure of the neural network.
3.6 Deep learning -- 3.6.1 Feature engineering -- 3.6.2 Network engineering -- 3.6.3 Neural networks with graphic processing units -- 3.6.3.1 Summary -- 3.7 Other types of neural networks -- 3.7.1 Simulated annealing -- 3.8 Applications -- 3.8.1 Pattern recognition -- 3.8.2 Draw frame -- 3.8.3 Hairiness of worsted yarns -- 3.8.4 Draw-winding process -- 3.8.5 Weaving process -- 3.8.6 Yarn breakage rate during weaving -- 3.8.7 Yarn shrinkage -- 3.8.8 Spirality of cotton fabrics -- 3.8.9 Textile fabric appearance -- 3.8.10 Fabric inspection -- 3.8.11 Design of airbag fabrics -- 3.8.12 Thermal resistance and thermal conductivity of textile fabrics -- 3.8.13 Protective textiles -- 3.8.14 Emotion-based textile indexing -- 3.8.15 Drapability of textiles -- 3.8.16 Color prediction -- 3.8.17 Smart carpet -- 3.8.18 Applications in composites -- 3.8.19 Other applications -- 3.9 Practical advice -- 3.9.1 Significance of input parameters -- 3.9.2 Network structure and size-under- and overfitting -- 3.9.3 Data base -- 3.9.4 Recall stage -- 3.10 Summary -- 3.11 Web references of software tools -- 3.11.1 Comprehensive lists of neural network software -- 3.11.2 Free neural network software -- 3.11.3 Commercial neural network software -- 3.11.4 Other sources -- References -- 4 - Genetic algorithms and evolution strategy in textile engineering -- 4.1 Introduction -- 4.2 Biological background -- 4.2.1 Basics of genetics -- 4.2.2 Evolution theory -- 4.2.3 Chemical basics of inheritance -- 4.2.4 Genotype and phenotype -- 4.3 Mathematical model of the evolution strategy -- 4.3.1 Basics -- 4.3.2 Evolution strategies -- 4.3.2.1 (1+1) - evolution strategy -- 4.3.2.2 (μ+λ) - evolution strategy -- 4.3.2.3 (μ, λ) - evolution strategy -- 4.3.2.4 (μ/ρ λ) - evolution strategy -- 4.3.3 Recombination -- 4.3.4 Mutation -- 4.3.4.1 Self-regulating mutative increment control.
4.3.5 Selection pressure and population waves -- 4.3.5.1 Population size and life span -- 4.3.6 Summary -- 4.4 Genetic algorithms -- 4.5 Comparison of evolutionary algorithms and iteration processes -- 4.5.1 Speed -- 4.5.2 Accuracy -- 4.5.3 Reproducibility -- 4.5.4 Boundary conditions -- 4.6 Applications of evolutionary algorithms -- 4.6.1 Texturing process -- 4.6.1.1 Genotype and phenotype -- 4.6.1.2 Number of individuals and life span -- 4.6.1.3 Recombination factor -- 4.6.1.4 Size of the gene pool -- 4.6.1.5 Mutation increment -- 4.6.1.6 Number of mutants -- 4.6.1.7 Importance of mutants for the evolutionary progress -- 4.6.1.8 Final algorithm and settings -- 4.6.1.9 Reproducibility of the results -- 4.6.1.10 Robustness -- 4.6.2 Weaving machine -- 4.6.3 Degumming of kenaf -- 4.6.4 Assessing cotton quality -- 4.6.5 Staple fiber spinning -- 4.6.6 Smart production with optimized settings -- 4.6.7 Carpet pattern design -- 4.6.8 Reducing the weight of composite structures -- 4.6.9 Determining optimum machining parameters for composite processing -- 4.6.10 Optimizing model parameters for textile surface friction -- 4.6.11 Improving the structure of an ANN -- 4.6.12 Spinning of staple fiber yarns -- 4.6.13 Other applications -- 4.7 Practical advice -- References -- Web references of software tools -- 5 - Turbulence models for simulation and modeling in textile engineering -- 5.1 Introduction -- 5.1.1 Analysis of the forces generating vorticity -- 5.1.2 Introduction to turbulent flow -- 5.1.3 Main characteristics and parameters associated to turbulent flow -- 5.1.4 The Kolmogorov hypotheses and the energy cascade -- 5.1.5 A general view of some different turbulence models -- 5.2 Differential equations for turbulent flow -- 5.2.1 Reynolds averaged Navier-Stokes K-ε turbulence model -- 5.2.2 Large eddy simulation turbulence model.
5.3 Computational fluid dynamics simulation approach in textile engineering -- 5.3.1 Examples of real problems of the textile world solved using computational fluid dynamics simulations -- 5.3.1.1 Computational fluid dynamics simulations designed to optimize the transfer channel shape and dimensions used in rotor spinn ... -- 5.3.1.2 Computational fluid dynamics study of flow around drafting cylinders used in pneumatic spinning -- 5.4 Machine design modification -- 5.4.1 Machine design and modification from machine perspective -- 5.4.2 Machine design and modification from material processing perspective -- 5.4.3 Ring spinning technology -- 5.4.3.1 Simple steps to implement a successful spinning simulation using computational fluid dynamics approach -- 5.4.3.2 Computational fluid dynamics simulations on ring spinning -- 5.4.4 Open-end spinning technology -- 5.4.5 Computational fluid dynamics model assumptions of the dual-feed rotor spinning unit -- 5.5 Conclusion -- References -- Further reading -- 6 - Modeling and simulation applications in yarn formation technology -- 6.1 Introduction -- 6.2 Twist propagation simulation -- 6.2.1 Sliver model generation -- 6.2.2 Sliver generation -- 6.2.3 Algorithm implementation process -- 6.2.4 Sliver generation flowchart -- 6.3 Step-by-step model generation and simulation -- 6.3.1 Twisting method -- 6.3.2 Simulation results -- 6.3.3 Configurational comparison and simulation verification -- 6.4 Comparison and verification of staple yarn shape -- 6.4.1 Twisting device -- 6.4.2 Analysis of twisting process -- 6.5 Yarn formation optimization on a compact spinning with lattice apron -- 6.5.1 Three-dimensional model of the condensing zone -- 6.5.2 Creating the geometry -- 6.5.3 Mesh and setup and solution -- 6.5.4 Design evaluation -- 6.5.4.1 Design A-type and B-Type -- 6.5.4.2 Boundary conditions.
6.5.5 Results analysis.
Textile industry