MSEval: A Dataset for Material Selection in Conceptual Design to Evaluate Algorithmic Models

TL;DR

MSEval is a new dataset of expert material evaluations across diverse design briefs, created to benchmark and improve machine learning models for material selection in conceptual design.

Contribution

The paper introduces MSEval, a comprehensive dataset for evaluating and enhancing machine learning algorithms in the context of material selection during early design phases.

Findings

Provides a diverse set of expert evaluations for benchmarking

Facilitates the development of better ML models for material selection

Supports research in computational design and optimization

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 typically treated as an optimization problem with a single correct answer. Moreover, it is also often restricted to specific types of objects or design functions, which can make the selection process computationally expensive and time-consuming. In this paper, we introduce MSEval, a novel dataset which is comprised of expert material evaluations across a variety of design briefs and criteria. This data 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.