Advanced Multiresponse Process Optimisation

This book presents an intelligent, integrated, problem-independent method for multiresponse process optimization. In contrast to traditional approaches, the idea of this method is to provide a unique model for the optimization of various processes, without imposition of assumptions relating to the type of process, the type and number of process parameters and responses, or interdependences among them. The presented method for experimental design of processes with multiple correlated responses is composed of three modules: an expert system that selects the experimental plan based on the orthogonal arrays; the factor effects approach, which performs processing of experimental data based on Taguchi’s quality loss function and multivariate statistical methods; and process modeling and optimization based on artificial neural networks and metaheuristic optimization algorithms. The implementation is demonstrated using four case studies relating to high-tech industries and advanced, non-conventional processes.

This research presents a novel approach for effectively addressing the multi-response problem in the Taguchi Method, a pervasive issue in the production process and productivity index improvement efforts. We achieved this by integrating the Taguchi Method with DEA, transforming experimental trials into Decision Making Units (DMUs) and response variables into inputs and outputs. Subsequently, advanced DEA formulations, like simultaneous DEA and super-efficiency models, were employed to compute the relative and cross efficiencies of the DMUs, leading to a specific combination of multiple control factors at different levels. This method preserves the benefits of the Taguchi Design of Experiments (DoE) while providing an optimal solution for improving multiple quality responses. The Addictive Model for Factor Effects confirmed that our proposed combination delivers superior improvement results when compared to empirical and previous studies' solutions, while also reducing computational effort. The study's findings offer significant implications for advanced manufacturing practices, especially those requiring multi-response optimization.

This book covers various multiple-criteria decision making (mcdm) methods for modeling and optimization of advanced manufacturing processes (AMPs). Processes such as non-conventional machining, rapid prototyping, environmentally conscious machining and hybrid machining are finally put together in a single book. It highlights the research advances and discusses the published literature of the last 15 years in the field. Case studies of real life manufacturing situations are also discussed.

Advanced Modeling and Optimization of Manufacturing Processes presents a comprehensive review of the latest international research and development trends in the modeling and optimization of manufacturing processes, with a focus on machining. It uses examples of various manufacturing processes to demonstrate advanced modeling and optimization techniques. Both basic and advanced concepts are presented for various manufacturing processes, mathematical models, traditional and non-traditional optimization techniques, and real case studies. The results of the application of the proposed methods are also covered and the book highlights the most useful modeling and optimization strategies for achieving best process performance. In addition to covering the advanced modeling, optimization and environmental aspects of machining processes, Advanced Modeling and Optimization of Manufacturing Processes also covers the latest technological advances, including rapid prototyping and tooling, micromachining, and nano-finishing. Advanced Modeling and Optimization of Manufacturing Processes is written for designers and manufacturing engineers who are responsible for the technical aspects of product realization, as it presents new models and optimization techniques to make their work easier, more efficient, and more effective. It is also a useful text for practitioners, researchers, and advanced students in mechanical, industrial, and manufacturing engineering.

Optimization for advanced manufacturing processes in fabrication is becoming increasingly important around the world across a range of industries because of its improved productivity, capabilities, efficacy, and sustainability characteristics. This new book explains how to optimize various industrial systems and processes utilizing new significant statistical and evolutionary methodologies. Statistical, multi-criteria decision-making, and evolutionary strategies for single- and multi-objective optimization are covered here, with relevant theoretical details, salient characteristics, implementation processes, effectiveness studies, and results in manufacturing. The chapters present a variety of advanced manufacturing processes and optimization techniques. Processes are described with basic explanations, characteristics, and applications in various technological and biomedical domains. The chapters address the processes prediction of mechanical properties, design and development of tools, impact of power supply, increasing machining precision and efficiency, genetic algorithm optimization, additive manufacturing, and much more. Providing a comprehensive study of advanced manufacturing processes, this volume is a valuable reference for engineers and R&D researchers involved in optimization for advanced manufacturing processes as well as for academics and postgraduate-level students in mechanical engineering, manufacturing engineering, and machining processes.

This book constitutes the refereed proceedings of the First Symposium on Machine Learning and Metaheuristics Algorithms, and Applications, held in Trivandrum, India, in December 2019. The 17 full papers and 6 short papers presented in this volume were thoroughly reviewed and selected from 53 qualified submissions. The papers cover such topics as machine learning, artificial intelligence, Internet of Things, modeling and simulation, disctibuted computing methodologies, computer graphics, etc.

Nature-Inspired Optimization in Advanced Manufacturing Processes and Systems Subject Guide: Engineering—Industrial and Manufacturing The manufacturing system is going through substantial changes and developments in light of Industry 4.0. Newer manufacturing technologies are being developed and applied. There is a need to optimize these techniques when applied in different circumstances with respect to materials, tools, product configurations, and process parameters. This book covers computational intelligence applied to manufacturing. It discusses nature-inspired optimization of processes and the design and development in manufacturing systems. It explores all manufacturing processes, at both macro and micro levels, and offers manufacturing philosophies. Nonconventional manufacturing, real industry problems and case studies, research on generative processes, and relevance of all this to Industry 4.0, is also included. Researchers, students, academicians, and industry professionals will find this reference title very useful.

This book covers several bases at once. It is useful as a textbook for a second course in experimental optimization techniques for industrial production processes. In addition, it is a superb reference volume for use by professors and graduate students in Industrial Engineering and Statistics departments. It will also be of huge interest to applied statisticians, process engineers, and quality engineers working in the electronics and biotech manufacturing industries. In all, it provides an in-depth presentation of the statistical issues that arise in optimization problems, including confidence regions on the optimal settings of a process, stopping rules in experimental optimization, and more.

This book comprises select peer-reviewed papers presented at the International Conference on Advanced Engineering Optimization Through Intelligent Techniques (AEOTIT) 2018. The book combines contributions from academics and industry professionals, and covers advanced optimization techniques across all major engineering disciplines like mechanical, manufacturing, civil, automobile, electrical, chemical, computer and electronics engineering. Different optimization techniques and algorithms such as genetic algorithm (GA), differential evolution (DE), simulated annealing (SA), particle swarm optimization (PSO), artificial bee colony (ABC) algorithm, artificial immune algorithm (AIA), teaching-learning-based optimization (TLBO) algorithm and many other latest meta-heuristic techniques and their applications are discussed. This book will serve as a valuable reference for students, researchers and practitioners and help them in solving a wide range of optimization problems.

It is almost impossible to imagine life today without the electronics, communications and networks we have all come to take for granted. The 6G network is currently under development and some chips able to operate at the Terahertz (THz) scale have already been introduced, so the next decade will probably see the consolidation of 6G-based technology, as well as many compliant devices. This book presents the proceedings of the 11th International Conference on Electronics, Communications and Networks (CECNet 2021), initially planned to be held from 18-21 November 2021 in Beijing, China, but ultimately held as an online event due to ongoing COVID-19 restrictions. The CECNet series is now an established annual event attracting participants in the interrelated fields of electronics, computers, communications and wireless communications engineering and technology from around the world. Careful review by program committee members, who took into consideration the breadth and depth of those research topics that fall within the scope of CECNet, resulted in the selection of the 88 papers presented here from the 325 submissions received. This represents an acceptance rate of around 27%. Providing an overview of current research and developments in these rapidly evolving fields, the book will be of interest to all those working with digital communications networks.