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<p>In worldwide, fused deposition modelling (FDM)-based 3D printing technology are majorly used in manufacturing industry. The process parameters of FDM determine its performance. The FDM process is greatly impacted by a number of process variables, including build orientation, infill pattern, raster angle, bed temperature, infill density, layer thickness, print speed, extrusion temperature and nozzle diameter. The most crucial process parameter for FDM-based 3D printing must be identified. This work focuses on optimizing FDM process parameters using hybrid models based on multi-attribute decision analysis (MADA), specifically AHP TOPSIS and Entropy TOPSIS. Four criteria, or attributes—high strength, precise detail, better overhangs, and low warping—are selected for this analysis. In the hybrid models, the most crucial process parameter for 3D printing is determined using the TOPSIS method, while entropy and the AHP methods are utilized to calculate the weights of the criterion. The alternatives are ranked using the two models, and the most important 3D printing process parameter was found to be layer thickness. The research findings were contrasted using Spearman’s rank correlation and relative closeness to the ideal solution. In this investigation, the weights have been determined with the help of subjective expert judgment. The closeness score indicates the AHP-TOPSIS model outperforms the Entropy-TOPSIS model for choosing the process parameter of 3D printing.</p>

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Comparative Analysis of Two Hybrid MADA Models for the Selection of Process Parameters of FDM Based 3D Printing

  • 期刊论文

  • 发表日期：

    2026年2月14日

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  • Shantanu Datta,
  • Sourav Majumdar,
  • S. H. Mullick,
  • Barnali Kundu,
  • Sumit Chabri

摘要

In worldwide, fused deposition modelling (FDM)-based 3D printing technology are majorly used in manufacturing industry. The process parameters of FDM determine its performance. The FDM process is greatly impacted by a number of process variables, including build orientation, infill pattern, raster angle, bed temperature, infill density, layer thickness, print speed, extrusion temperature and nozzle diameter. The most crucial process parameter for FDM-based 3D printing must be identified. This work focuses on optimizing FDM process parameters using hybrid models based on multi-attribute decision analysis (MADA), specifically AHP TOPSIS and Entropy TOPSIS. Four criteria, or attributes—high strength, precise detail, better overhangs, and low warping—are selected for this analysis. In the hybrid models, the most crucial process parameter for 3D printing is determined using the TOPSIS method, while entropy and the AHP methods are utilized to calculate the weights of the criterion. The alternatives are ranked using the two models, and the most important 3D printing process parameter was found to be layer thickness. The research findings were contrasted using Spearman’s rank correlation and relative closeness to the ideal solution. In this investigation, the weights have been determined with the help of subjective expert judgment. The closeness score indicates the AHP-TOPSIS model outperforms the Entropy-TOPSIS model for choosing the process parameter of 3D printing.