Computational Intelligence based Optimization of Manufacturing Process for Sustainable Materials

The book discusses computational models for reliability engineering, including artificial neural networks, agent-based models, and decision field theory. It also covers sustainable materials development using metaheuristic approaches, swarm intelligence methods for manufacturing process optimization, and case studies for industrial optimizations. Additionally, it explores the use of computational optimization for reliability and maintainability theory.

Format: Hardback
Length: 196 pages
Publication date: 25 September 2023
Publisher: Taylor & Francis Ltd

Computational models play a crucial role in reliability engineering, enabling the analysis and optimization of complex systems. In this comprehensive discussion, we explore a range of computational models, including artificial neural networks, agent-based models, and decision field theory. These models serve as powerful tools for understanding and predicting the behavior of systems, enabling engineers to make informed decisions about their design, maintenance, and operation.

Artificial neural networks (ANNs) are a type of computational model that mimics the workings of the human brain. They are highly versatile and can be trained to recognize patterns, make predictions, and perform complex tasks. ANNs have been applied in various fields, including image recognition, speech recognition, and natural language processing.

Agent-based models, on the other hand, simulate the behavior of individual agents or entities within a system. These models are useful for modeling complex systems that involve interactions between multiple agents, such as supply chains, transportation networks, and social networks. Agent-based models can capture the dynamics and interactions of these systems, allowing engineers to analyze and optimize their performance.

Decision field theory is a mathematical framework that combines probability theory and decision theory to model the decision-making process of humans and other agents. It is used to analyze the risk and uncertainty associated with decision-making, and to develop decision-making algorithms that optimize performance under different conditions. Decision field theory has been applied in various fields, including finance, insurance, and healthcare.

In addition to these computational models, we also discuss the development of sustainable materials using metaheuristic approaches. Metaheuristics are optimization techniques that use algorithms inspired by natural processes, such as swarm intelligence, to solve complex problems. These approaches have been used to optimize the design of materials, reduce waste, and improve the sustainability of manufacturing processes.

Another important area of application is the use of computational optimization for reliability and maintainability theory. Computational optimization algorithms can be used to optimize the design of systems, reduce the risk of failure, and improve the reliability and maintainability of industrial components. Case studies are presented to illustrate the practical applications of these techniques in the manufacturing industry.

Swarm intelligence techniques, such as ant colony optimization, particle swarm optimization, cuckoo search, and genetic algorithms, are also discussed in detail. These techniques are used to solve complex industrial problems of the manufacturing industry, including predicting reliability, maintainability, and availability of several industrial components.

In conclusion, computational models play a vital role in reliability engineering, enabling engineers to analyze and optimize complex systems. From artificial neural networks to agent-based models, decision field theory to metaheuristic approaches, and swarm intelligence techniques to computational optimization, these models provide a comprehensive framework for understanding and improving the performance of systems. As technology continues to advance, we can expect to see new and innovative computational models emerge, further expanding the capabilities of reliability engineering.

Weight: 550g
Dimension: 234 x 156 (mm)
ISBN-13: 9781032191041