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9.5 MB
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BF1FF98671C4914C95814964D3FD1408A529519A
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Oct. 23, 2025, 11 a.m.
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(Last updated: Oct. 23, 2025, 11:01 a.m.)
| File | Size |
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| Singh R. Process Modeling and Optimization in Modern Manufacturing 2026.pdf | 9.5 MB |
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2025-06-30
| Uploaded by andryold1 | Size 15.5 MB | Health [ 31 /30 ] | Added 2025-06-30 |
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SOURCE: Singh R. Process Modeling and Optimization in Modern Manufacturing 2026
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COVER
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MEDIAINFO
Textbook in PDF format This book covers modeling and optimization of various modern manufacturing processes such as advanced machining, hybrid manufacturing, and additive manufacturing including related case studies in these domains. Various areas like smart manufacturing, hybrid manufacturing, 3D printing, process modeling and characterization, optimization, and so forth are covered in detail. The focus of this book is on artificial neural network (ANN), finite element analysis, firefly/genetic algorithm, particle swarm, and fuzzy-based techniques, which are the main optimization and modeling techniques. Artificial Intelligence (AI) and Machine Learning (ML) are expected to play an increasingly central role in process modeling and optimization. By training models on vast datasets from manufacturing processes, AI can help predict complex behaviors and optimize parameters in ways that would be difficult for traditional methods. Emmanouilidis et al. studied that the AI and ML have revolutionized process modeling and optimization across various industries. It is helping the industries in becoming more efficient and decision-making due to which industries can enable to know about the future failure which reduces the equipment’s cost. These technologies works on large datasets to create models that simulate complex processes, providing insights that traditional method cannot achieve. In the process of AI and ML, algorithms are used to identify patterns and relationships within data, allowing for the creation of highly accurate predictive models. These models help in optimizing processes by forecasting potential outcomes, reducing downtime, and enhancing workplace safety by identifying potential hazards. The techniques or the algorithms used in AI and ML are as neural networks, support vector machines, and Deep Learning which are trained on historical data to learn some unique patterns and relationship between input feature and equipment failure. Moreover, Al and ML are not just limited to the modeling but also extend to real-time optimization and adaptive control. AI and ML can seamlessly integrate with IoT and SCADA system. However, the challenges such as data quality issues and the need for skilled person must be required to get the full use of AI and ML in predictive industries. Calaon et al. present the role of AI and ML in process optimization within the Industry 4.0 framework, emphasizing the importance of data generated by connected products. The authors argue that while digitalization in industry enhances productivity and efficiency, it also presents new challenges in data management and processing. Through a bibliometric analysis, the study reveals that while data models and process optimization are well-researched topics, the involvement of shop floor personnel in these AI-driven processes is underexplored in academic literature. This highlights a potential gap in understanding the human element within smart manufacturing environments. The research underscores the need for a more comprehensive approach to AI implementation in industrial settings, one that considers both technological advancements and the role of human operators in the process optimization landscape. Features of the book: Provides in-depth investigations on prospects of modeling and optimization of modern manufacturing processes. Detailed overview on different evolutionary and bio-inspired optimization techniques and their implementation. Provides step-by-step guidance on how to use machine learning for the enhancement of productivity and quality in modern manufacturing processes. Discusses sustainability and Industry 4.0-based content. Includes case studies and practical examples. This book is aimed at researchers and graduate students in mechanical, manufacturing, production, and industrial engineering
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