Optimization of mechanical properties of acrylonitrile styrene acrylate (ASA) in FDM via TOPSIS-simulated annealing hybrid approach

Purpose The aim of this study is to enhance the mechanical properties of tensile, compression and flexural strength of acrylonitrile styrene acrylate (ASA) components manufactured via fused deposition modeling (FDM). This is achieved by developing a robust multi-response optimization framework that integrates statistical design, multi criteria decision-making (MCDM) and metaheuristic techniques. Design/methodology/approach A definitive screening design (DSD) was employed to investigate the influence of seven key FDM process parameters: layer height (LH), extrusion temperature (ET), bed temperature (BT), print speed (PS), infill density (ID), number of contours (NC) and raster angle (RA). Mechanical tests were conducted as per ASTM standards. MCDM approach, namely, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), was used to combine the mechanical responses into a single performance index. A regression model was developed to capture the relationship between process settings and performance, which was then further optimized using the Simulated Annealing (SA) algorithm. Findings Regression analysis of variance (RANOVA) revealed ID, LH, NC, ET, BT, RA as the most influential parameters affecting mechanical properties. The proposed hybrid optimization framework, combining TOPSIS with SA, demonstrated superior performance over the standalone TOPSIS method. The optimal parameters obtained are LH = 0.1 mm, ET = 260°C, BT = 90°C, PS = 37 mm/s, ID = 70%, NC = 6 and RA = 0° resulted in mechanical strength of 35.17 MPa (compression), 34.87 MPa (tensile) and 67.75 MPa (flexural strength). SA exhibited reliable convergence, low constraint violations and consistent high-quality solutions across multiple runs. Originality/value This study presents a novel approach that systematically integrates TOPSIS with SA for multi-response optimization of ASA-based FDM processes. It fills a significant research gap by contributing a validated hybrid framework that efficiently balances exploration and exploitation to yield optimal solutions. The findings contribute to both theoretical advancement in additive manufacturing optimization and practical improvements in the mechanical performance of ASA parts.