Due to their unique combination of characteristics (strong yet flexible and lightweight), fibrous materials are increasingly being used in technical applications, making it imperative to understand the properties of the materials before their manufacturing. Soft computing techniques—including artificial neural networks, fuzzy logic, and genetic algorithms—offer potential solutions to the modeling and optimization of fibrous materials. This work presents a compendium of research on the application of soft computing techniques in fibrous materials modeling, optimization, and engineering.

Textiles and computing have long been associated. High volume and low profit margins of textile products have driven the industry to invest in high technology, particularly in the area of data interpretation and analysis. Thus, it is virtually inevitable that soft computing has found a home in the textile industry. Contained in this volume are six chapters discussing various aspects of soft computing in the field of textiles and apparel.

Edited by a leading expert in the field with contributions from experienced researchers in fibers and textiles, this handbook reviews the current state of fibrous materials and provides a broad overview of their use in research and development. Volume One focuses on the classes of fibers, their production and characterization, while the second volume concentrates on their applications, including emerging ones in the areas of energy, environmental science and healthcare. Unparalleled knowledge of high relevance to academia and industry.

The evolution of soft computing applications has offered a multitude of methodologies and techniques that are useful in facilitating new ways to address practical and real scenarios in a variety of fields. In particular, these concepts have created significant developments in the engineering field. Soft Computing Techniques and Applications in Mechanical Engineering is a pivotal reference source for the latest research findings on a comprehensive range of soft computing techniques applied in various fields of mechanical engineering. Featuring extensive coverage on relevant areas such as thermodynamics, fuzzy computing, and computational intelligence, this publication is an ideal resource for students, engineers, research scientists, and academicians involved in soft computing techniques and applications in mechanical engineering areas.

The articles in this book present advanced soft methods related to genetic and evolutionary algorithms, immune systems, formulation of deterministic neural networks and Bayesian NN. Many attention is paid to hybrid systems for inverse analysis fusing soft methods and the finite element method. Numerical efficiency of these soft methods is illustrated on the analysis and design of complex engineering structures.

Due to problems associated with the design and manufacturing of composite materials, there is a need to introduce computational and intelligent systems engineering methodology in materials engineering. Soft Computing in the Design and Manufacturing of Composite Material offers an intelligent approach to advance material engineering, and significantly improves the process of designing and manufacturing a new material. This title includes chapters covering topics such as soft computing techniques, composite materials engineering, design and manufacturing of composite materials, numerical modeling, prediction, and optimization of the composite materials performance, development of the hybrid models, and control of the composite material performance. Introduction of soft computing in the composite materials engineering Includes accurate and detailed analysis of the current state of the art in the field Development of the intelligent models for design and manufacturing of composite material Details composite material performance prediction Optimization of the manufacturing process of composite materials

This book aims at addressing the challenges of contemporary manufacturing in Industry 4.0 environment and future manufacturing (aka Industry 5.0), by implementing soft computing as one of the major sub-fields of artificial intelligence. It contributes to development and application of the soft computing systems, including links to hardware, software and enterprise systems, in resolving modern manufacturing issues in complex, highly dynamic and globalized industrial circumstances. It embraces heterogeneous complementary aspects, such as control, monitoring and modeling of different manufacturing tasks, including intelligent robotic systems and processes, addressed by various machine learning and fuzzy techniques; modeling and parametric optimization of advanced conventional and non-conventional, eco-friendly manufacturing processes by using machine learning and evolutionary computing techniques; cybersecurity framework for Internet of Things-based systems addressing trustworthiness and resilience in machine-to-machine and human-machine collaboration; static and dynamic digital twins integration and synchronization in a smart factory environment; STEP-NC technology for a smart machine vision system, and integration of Open CNC with Service-Oriented Architecture for STEP-NC monitoring system in a smart manufacturing. Areas of interest include but are not limited to applications of soft computing to address the following: dynamic process/system modeling and simulation, dynamic process/system parametric optimization, dynamic planning and scheduling, smart, predictive maintenance, intelligent and autonomous systems, improved machine cognition, effective digital twins integration, human-machine collaboration, robots, and cobots.