Optimization of Environmentally Friendly Material Selection for Automotive Mechatronics Components Using LCA Data and Multi‑Criteria Decision Making (MCDM)
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The automotive industry faces an increasing demand for sustainable material selection as mechatronic components become more widespread in electrified vehicles. However, data-driven material selection approaches that simultaneously integrate environmental, economic, and technical criteria without laboratory experiments remain underdeveloped. This study addresses this gap by developing a computational framework that combines Life Cycle Assessment (LCA) with a Multi-Criteria Decision-Making (MCDM) approach, specifically the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, using Analytical Hierarchy Process (AHP)–based weights. The framework enables a transparent and reproducible evaluation of environmentally friendly materials for automotive mechatronic components. A case study on an actuator housing evaluates seven material alternatives: Al 6061 (die-cast), recycled Al (die-cast), Mg AZ91 (die-cast), PA6-GF30 (injection), PBT-GF30 (injection), PA12 (SLS 3D print), and bio-based PBT-GF30 (injection). The criteria include total global warming potential (GWP), cumulative energy demand (CED), water use, recyclability, cost, mass, stiffness index, thermal conductivity, and supply risk. Results show that recycled aluminum achieves the highest ranking (closeness coefficient = 0.939), followed by Al 6061 (0.727) and Mg AZ91 (0.547). A Monte Carlo analysis with 1,000 iterations confirms that recycled aluminum consistently remains the best option with 100% robustness under varying weighting conditions. The proposed workflow is replication-ready and can be directly integrated with established LCA databases such as GREET, Ecoinvent, or EPD, enabling engineers to perform sustainable and quantitative material decisions using only data and computational analysis.
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