Utilizing an AI material database for sustainable material selection

Abstract

The rising importance of sustainability in the automotive industry has increased the demand for more environmentally conscious decision-making during product development. The materials chosen for a vehicle directly shape its environmental impact, resource efficiency, and sustainability performance across its entire lifecycle. Yet in practice, engineers regularly find themselves working with scattered material information, insufficient sustainable data, tight time constraints, and increasing complexity in evaluating sustainable material alternatives during early-stage product development. While a number of tools and methods have been created over the years to help bring sustainability into engineering workflows, research consistently shows that such tools are often not adopted or used to the extent intended in industrial practice due to challenges related to usability, workflow integration, trust and alignment with engineering needs. This thesis investigates how an AI-enabled Sustainable Material Data Ecosystem (SMDE), developed within an automotive manufacturing context, is perceived and used by engineers in practice, as an industrial case study in collaboration with Volvo Trucks Technology & Industrial Division (TTI). Empirical data was collected through interviews and user feedback sessions with engineers from different product development domains, and analysed qualitatively to understand what helps and what hinders the practical use of the system within existing workflows. The findings show that the system has genuine potential in terms of enabling more convenient access to material information, helping increase the rate at which alternatives are explored, and being useful for early engineering investigation. However, some issues also emerged during the study, including inconsistent response quality, excessive and irrelevant output, concerns around trusting AI-generated information, and the problem of the system’s integration into everyday work along with other existing tools. Overall, results suggest that it is very important to develop AI-based decision support systems specifically according to the industrial needs and workflows of the engineers using it to support successful adoption within product development environments.