Advancing sustainable manufacturing decision-making through an online life cycle assessment tool for wire-based directed energy deposition

Abstract The manufacturing sector is highly material and energy-intensive, contributing significantly to environmental degradation. Despite its environmental impact, manufacturing is essential for economic growth and social benefits, such as improved living standards and job creation. Therefore, balancing economic, environmental, and social outcomes through sustainable manufacturing practices is crucial. In recent years, additive manufacturing (AM) has transformed the production of complex parts across various sectors by reducing material waste and lowering costs. One prominent wire-based Directed Energy Deposition (DED) type of AM process, commonly referred to as Wire Arc Additive Manufacturing (WAAM) is gaining attention for its ability to produce large-scale parts at higher build rates and lower material and machine costs compared to other metal AM technologies. A comprehensive sustainability assessment of manufacturing technologies, including WAAM, is necessary for informed sustainable decision-making. The Life Cycle Sustainability Assessment (LCSA) method, which integrates Life Cycle Assessment (LCA), Life Cycle Costing (LCC), and Social Life Cycle Assessment (S-LCA), provides a holistic approach to evaluating environmental, economic, and social impacts. Recent advancements in online LCSA tools have improved accessibility and usability, particularly for small and medium enterprises (SMEs) that may lack the financial and human resources to perform sustainability assessments. These tools, tailored for specific sectors, enable quick and user-friendly sustainability assessments, facilitating informed decision-making. This study develops an online LCSA tool for WAAM, integrating LCA, LCC, and S-LCA models. By compiling relevant databases and formulating LCA, LCC, and S-LCA models, an interactive online tool is developed, in which non-experts can visualize and compare the sustainability impacts of WAAM and traditional CNC machining processes. The tool was also validated with data from existing LCA studies, and it predicted environmental and economic impacts with a maximum error margin of 12.5%. A case study of an automotive part is presented to demonstrate the utility of this tool. This approach promotes life cycle thinking from the early stages of product development, supporting sustainable manufacturing practices.