Prospects of Digital Twin for Dynamic Life Cycle Assessment of Smart Manufacturing Systems

Abstract

Smart manufacturing systems are poised to revolutionize industrial processes by leveraging advanced technologies for increased efficiency and productivity. However, alongside these advancements, there is a growing imperative to address environmental sustainability concerns. Conventional static life cycle assessment (LCA) methods often provide valuable insights into the environmental impacts of such manufacturing systems but often fall short in capturing real-time data and dynamic system interactions. Further, using the digital twin technology, physical assets can be virtually replicated in order to monitor, evaluate, and improve the particular manufacturing system. The dynamic properties can be effectively brought to LCA investigations by utilizing this technique. This paper explores the prospects of integrating digital twin technology for facilitating the dynamic LCA to enable comprehensive and timely environmental performance evaluation of smart manufacturing systems. We discuss the concepts, technological components, and potential applications of digital twin-enabled dynamic LCA, along with challenges and future research directions.