Evaluation of computationally optimized design variants for additive manufacturing using a fuzzy multi-criterion decision-making approach

The additive manufacturing industry requires effective and standardized methods for selecting design variants generated through computational tools. To address this need and overcome the current barriers in the industry, a decision support system based on quantitative metrics is necessary. This research aims to establish multiple criteria for evaluating design variations in additive manufacturing, considering both opportunistic and constraint-based approaches. The multi-criterion decision-making process integrates four distinct metrics that capture aspects such as geometric complexity, cost-benefit, and the additional cost associated with support structures. To facilitate the evaluation of design variants in metal additive manufacturing using laser powder bed fusion, a fuzzy power Maclaurin symmetric mean operator is employed for metric aggregation. The proposed approach is demonstrated by assessing topologically optimized design variants of an airplane bearing bracket and an engine bracket. The ranking and selection of design variants using this approach resulted in significant cost reductions, with a 50% reduction for the airplane bracket and a 75% reduction for the engine bracket, compared to the original designs manufactured using additive manufacturing techniques.