Scholarlink

A high-speed intersection of artificial intelligence (AI), data-oriented analytics, and digital manufacturing technologies is radically altering sustainable design and manufacturing. This chapter provides an in-depth look into how AI-driven processes are transforming product development, manufacturing planning, business management, and life-cycle management to capture the urgent sustainability issues. The focus is made on the combination of digital threads, digital twins, and AI as an integrated operating model that allows maintaining lifecycle visibility, making decisions in real time, and optimizing adaptively. The chapter covers the major developments in energy-aware analytics, digital twins, and optimization processes that enable manufacturers to track, anticipate, and reduce energy usage and still maintain a high level of efficiency and system resiliency. The AI-based scheduling and control solutions are discussed as the key to integrating sustainability goals directly into the operational decisions, minimizing waste, emissions, and resource inefficiency in changing and uncertain environments. Moreover, AI as a facilitator of circular manufacturing is discussed, and it is important to emphasize digital product passports, intelligent traceability, and optimized reverse logistics in relation to adopting closed loops of materials and energy. Generative AI is discussed as a strong facilitator of sustainable design exploration and digital twin development, especially in the new paradigms of Industry 5.0 and Industry 6.0, in which human-centricity, resilience, and ethical alignment are the key area of interest. Making these views come together, the chapter shows that AI-driven, data-centric solutions are not a luxury anymore but the key to the implementation of scalable, resilient, and sustainable manufacturing systems. The presented insights can be used by researchers, practitioners, and policymakers who aim at utilising AI to create long-term environmental, economic, and societal values in progressive manufacturing systems.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

AI and Data-Driven Sustainable Design and Manufacturing

  • 章节

  • 发表日期：

    2026年

查看全文
  • Praveen Kumar Verma,
  • Hitesh Vasudev

摘要

A high-speed intersection of artificial intelligence (AI), data-oriented analytics, and digital manufacturing technologies is radically altering sustainable design and manufacturing. This chapter provides an in-depth look into how AI-driven processes are transforming product development, manufacturing planning, business management, and life-cycle management to capture the urgent sustainability issues. The focus is made on the combination of digital threads, digital twins, and AI as an integrated operating model that allows maintaining lifecycle visibility, making decisions in real time, and optimizing adaptively. The chapter covers the major developments in energy-aware analytics, digital twins, and optimization processes that enable manufacturers to track, anticipate, and reduce energy usage and still maintain a high level of efficiency and system resiliency. The AI-based scheduling and control solutions are discussed as the key to integrating sustainability goals directly into the operational decisions, minimizing waste, emissions, and resource inefficiency in changing and uncertain environments. Moreover, AI as a facilitator of circular manufacturing is discussed, and it is important to emphasize digital product passports, intelligent traceability, and optimized reverse logistics in relation to adopting closed loops of materials and energy. Generative AI is discussed as a strong facilitator of sustainable design exploration and digital twin development, especially in the new paradigms of Industry 5.0 and Industry 6.0, in which human-centricity, resilience, and ethical alignment are the key area of interest. Making these views come together, the chapter shows that AI-driven, data-centric solutions are not a luxury anymore but the key to the implementation of scalable, resilient, and sustainable manufacturing systems. The presented insights can be used by researchers, practitioners, and policymakers who aim at utilising AI to create long-term environmental, economic, and societal values in progressive manufacturing systems.