Scholarlink

This chapter explores how artificial intelligence (AI) can transform sustainable materials engineering and circular design within manufacturing. AI enables intelligent decision-making across the product lifecycle—supporting zero-waste goals by optimizing design, disassembly, resource recovery, and reuse. The chapter proposes a multi-layered AI-based framework integrating supervised learning, generative design, reinforcement learning, and digital twins. The role of Product Lifecycle Management (PLM) and environmental Key Performance Indicators (KPIs) is emphasized to bridge sustainability with technical and economic feasibility. Practical implementation is illustrated via case studies in packaging, automotive, and electronics sectors. These demonstrate AI’s efficacy in reducing material usage, improving recyclability, and enhancing energy efficiency. Further, challenges such as data limitations, ethical implications, and organizational inertia are explored. This work provides a roadmap for AI integration in circular manufacturing and highlights its role in global Net-Zero transitions.

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 Machine Learning Driven Circular Design for Smart Manufacturing and Zero Waste Production

  • 章节

  • 发表日期：

    2026年

查看全文
  • Sumit Kumar Kapoor,
  • Kriti Sankhla,
  • Shikha Khullar,
  • Udit Mamodiya,
  • Vipin Khattri

摘要

This chapter explores how artificial intelligence (AI) can transform sustainable materials engineering and circular design within manufacturing. AI enables intelligent decision-making across the product lifecycle—supporting zero-waste goals by optimizing design, disassembly, resource recovery, and reuse. The chapter proposes a multi-layered AI-based framework integrating supervised learning, generative design, reinforcement learning, and digital twins. The role of Product Lifecycle Management (PLM) and environmental Key Performance Indicators (KPIs) is emphasized to bridge sustainability with technical and economic feasibility. Practical implementation is illustrated via case studies in packaging, automotive, and electronics sectors. These demonstrate AI’s efficacy in reducing material usage, improving recyclability, and enhancing energy efficiency. Further, challenges such as data limitations, ethical implications, and organizational inertia are explored. This work provides a roadmap for AI integration in circular manufacturing and highlights its role in global Net-Zero transitions.