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Sustainable Manufacturing
Sustainability enables the development of products with minimal environmental impact coupled with economical and societal benefits. This book provides an understanding of theoretical and practical perspectives pertaining to sustainable manufacturing.
The book offers theoretical concepts and practical descriptions of sustainable manufacturing. It provides insights from research and practical applications, as well as industrial case studies, and several illustrations and examples. The book goes on to discuss design strategies that support sustainable manufacturing and includes ISO 14001 and PAS 2050 standards.
The book addresses the needs of undergraduate and postgraduate engineering students, academic researchers and industry practitioners.
eBook
Preface
It is with great enthusiasm that we present the proceedings of the 2025 5th International Conference on Industrial Manufacturing and New Materials (IMNM 2025). Held in Zhengzhou, China from April 18–20, 2025, this conference serves as a dynamic platform for global researchers, engineers, and industry pioneers to exchange pioneering insights and foster collaborations in the rapidly evolving fields of industrial manufacturing and advanced materials.This year’s conference attracted 138 submissions, with 58 high-quality papers accepted through a stringent peer-review process (acceptance rate: 42%). The proceedings are structured into two interconnected pillars that drive modern technological progress:List of Committee Member is available in this PDF.
Journal Article
Machine learning techniques in additive manufacturing: a state of the art review on design, processes and production control
For several industries, the traditional manufacturing processes are time-consuming and uneconomical due to the absence of the right tool to produce the products. In a couple of years, machine learning (ML) algorithms have become more prevalent in manufacturing to develop items and products with reduced labor cost, time, and effort. Digitalization with cutting-edge manufacturing methods and massive data availability have further boosted the necessity and interest in integrating ML and optimization techniques to enhance product quality. ML integrated manufacturing methods increase acceptance of new approaches, save time, energy, and resources, and avoid waste. ML integrated assembly processes help creating what is known as smart manufacturing, where technology automatically adjusts any errors in real-time to prevent any spillage. Though manufacturing sectors use different techniques and tools for computing, recent methods such as the ML and data mining techniques are instrumental in solving challenging industrial and research problems. Therefore, this paper discusses the current state of ML technique, focusing on modern manufacturing methods i.e., additive manufacturing. The various categories especially focus on design, processes and production control of additive manufacturing are described in the form of state of the art review.
Journal Article
Monitoring and control the Wire Arc Additive Manufacturing process using artificial intelligence techniques: a review
Wire Arc Additive Manufacturing is a Direct Energy Deposition additive technology that uses the principle of wire welding to deposit layers of material to create a finished component. This technology is finding an increasing interest in the manufacturing industry, especially thanks the low cost and the possibility to build large-scale components. Nowadays, the boosting to transition into smart manufacturing systems and the increasingly computational resources allowed the development of intelligent applications for smart production systems for both in situ inspection and process parameter control. This paper aims to provide an review of applications developed using artificial intelligence techniques for Wire Arc Additive Manufacturing, with particular focus on defect detection software modules, feedback generation for control system and innovative control strategies as reinforcement learning to overcome problems related to model non-linearity and uncertainties.
Journal Article
