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<p>This study presents a hybrid framework for the optimization of Fused Filament Fabrication (FFF) process parameters for PETG by integrating the Grey Entropy Method, regression analysis, and desirability-based multi-response optimization. Conventional optimization techniques such as Taguchi design, response surface methodology (RSM), and grey relational analysis (GRA) have been widely applied in additive manufacturing; however, they may face limitations in simultaneously handling multiple conflicting responses, response prioritization, and process interactions. In the present study, Grey Entropy determines objective empirically derived weights for tensile strength (TS), flexural strength (FS), print time, and material usage, while regression modelling establishes predictive relationships between process parameters and the response parameter quality index (RPQ). Further, the desirability approach balances conflicting objectives into a single performance index. The optimal parameter combination corresponds to build orientation (BO) of 45°, speed (S) of 45&#xa0;mm/s, infill density (ID) of 100%, nozzle temperature (T) of 245&#xa0;°C, and layer thickness (LT) of 0.225&#xa0;mm, resulting in a maximum TS of 32.08&#xa0;MPa and FS of 77.85&#xa0;MPa, while maintaining reduced print time and material usage, with an overall desirability value of 0.918. The proposed integrated framework offers a feasible and interpretable strategy in balancing mechanical performance and process efficiency of PETG-based additive manufacturing.</p>

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Hybrid Quantitative Analysis and Multi-Objective Optimization of PETG FFF Parameters on Mechanical Properties and Process Efficiency Using Linear Regression, Grey Entropy, and Desirability Functions

  • 期刊论文

  • 发表日期：

    2026年7月7日

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  • Gayatri,
  • Sourabh Anand,
  • Manoj Kumar Satyarthi

摘要

This study presents a hybrid framework for the optimization of Fused Filament Fabrication (FFF) process parameters for PETG by integrating the Grey Entropy Method, regression analysis, and desirability-based multi-response optimization. Conventional optimization techniques such as Taguchi design, response surface methodology (RSM), and grey relational analysis (GRA) have been widely applied in additive manufacturing; however, they may face limitations in simultaneously handling multiple conflicting responses, response prioritization, and process interactions. In the present study, Grey Entropy determines objective empirically derived weights for tensile strength (TS), flexural strength (FS), print time, and material usage, while regression modelling establishes predictive relationships between process parameters and the response parameter quality index (RPQ). Further, the desirability approach balances conflicting objectives into a single performance index. The optimal parameter combination corresponds to build orientation (BO) of 45°, speed (S) of 45 mm/s, infill density (ID) of 100%, nozzle temperature (T) of 245 °C, and layer thickness (LT) of 0.225 mm, resulting in a maximum TS of 32.08 MPa and FS of 77.85 MPa, while maintaining reduced print time and material usage, with an overall desirability value of 0.918. The proposed integrated framework offers a feasible and interpretable strategy in balancing mechanical performance and process efficiency of PETG-based additive manufacturing.