| description abstract | Material selection plays a pivotal role in many industries, from manufacturing to construction. Material selection is usually carried out after several cycles of conceptual design, during which designers iteratively refine the design solution and the intended manufacturing approach. In design research, material selection is frequently modeled as a structured decision-making process, where optimization techniques, whether single-objective or multiobjective, are employed to identify solutions that best meet the design requirements. However, traditional approaches can be limited by their reliance on existing knowledge and data, which may not adequately capture the full range of considerations involved in material selection. In this article, we introduce MSEval, a novel dataset comprised expert material evaluations across a variety of design briefs and criteria. This dataset is designed to serve as a benchmark to facilitate the evaluation and modification of machine learning models in the context of material selection for conceptual design. By focusing on a diverse set of design tasks and criteria, MSEval enables a more nuanced understanding of the material selection and the thought process, providing valuable insights for both human designers and AI systems. | |
