Manufacturing Process Optimization for Sustainable Development Using Digital Twin Applications explores the potential of digital twin applications in the manufacturing sector, with a primary focus on achieving sustainable development.

The edited volume provides a comprehensive overview of the interdisciplinary nature of digital twin applications, considering how collaboration between engineering, data science, environmental science, and other fields can lead to holistic and impactful solutions. The content addresses the challenges and potential hurdles in implementing digital twin solutions in manufacturing while also proposing future directions for research and development in this rapidly evolving field, further reviewing how digital twin technologies can be effectively applied to enhance efficiency, reduce waste, and improve overall productivity in manufacturing processes. The role of digital twins in contributing to sustainable development goals is also analyzed, including considerations such as resource conservation, energy efficiency, and the reduction of environmental impact throughout the manufacturing lifecycle. Case studies and real-world examples are included to illustrate successful implementations.

The publication caters to a diverse audience of professionals, researchers, and academics from engineering disciplines, including mechanical engineers, industrial engineers, electrical and electronic engineers, data scientists and AI engineers, and manufacturing and operations managers. The comprehensive approach makes it an essential resource for anyone seeking to understand the intersection of digital twin technology and sustainable manufacturing practices.

Manufacturing Process Optimization for Sustainable Development Using Digital Twin Applications explores the potential of digital twin applications in the manufacturing sector, with a primary focus on achieving sustainable development.

The edited volume provides a comprehensive overview of the interdisciplinary nature of digital twin applications, considering how collaboration between engineering, data science, environmental science, and other fields can lead to holistic and impactful solutions. The content addresses the challenges and potential hurdles in implementing digital twin solutions in manufacturing while also proposing future directions for research and development in this rapidly evolving field, further reviewing how digital twin technologies can be effectively applied to enhance efficiency, reduce waste, and improve overall productivity in manufacturing processes. The role of digital twins in contributing to sustainable development goals is also analyzed, including considerations such as resource conservation, energy efficiency, and the reduction of environmental impact throughout the manufacturing lifecycle. Case studies and real-world examples are included to illustrate successful implementations.

The publication caters to a diverse audience of professionals, researchers, and academics from engineering disciplines, including mechanical engineers, industrial engineers, electrical and electronic engineers, data scientists and AI engineers, and manufacturing and operations managers. The comprehensive approach makes it an essential resource for anyone seeking to understand the intersection of digital twin technology and sustainable manufacturing practices.