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<p>Fused Deposition Modeling (FDM) is widely adopted for fabricating polymer components. However, the fatigue strength of FDM printed parts remains a critical limitation for load bearing applications. Existing studies have largely focused on conventional process parameters while the influence of motion related kinematic parameters has received little attention. This study presents a novel and systematic investigation into the combined effects of two kinematic parameters (acceleration and jerk) and a directional dependent parameter (hatching angle) on the fatigue strength of FDM fabricated acrylonitrile butadiene styrene (ABS) parts. An optimal (custom) response surface based experimental design was employed followed by tensile–tensile fatigue testing in accordance with ASTM standards. Analysis of variance revealed that hatching angle is the most influential parameter followed by jerk and acceleration. A hybrid genetic algorithm–artificial neural network (GA-ANN) framework was developed to capture the nonlinear interactions among parameters and to optimize fatigue performance. The proposed GA-ANN model demonstrated superior prediction accuracy compared to conventional RSM and standalone ANN approaches. The maximum experimental fatigue strength within the design space was 11.67&#xa0;MPa whereas the GA-ANN optimized parameter combination (acceleration of 211.32&#xa0;mm/s2, jerk of 7.48&#xa0;mm/s, and hatching angle of 450/-450) achieved a significantly higher fatigue strength of 13.58&#xa0;MPa, which was experimentally validated with a prediction error of only 0.59%. The findings establish kinematic parameters and directional dependent parameter as critical design variables and provide an effective data driven framework for enhancing the fatigue performance of FDM fabricated components without material modification or post-processing.</p>

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Enhancing the Fatigue Strength of FDM Fabricated ABS Parts by Optimizing the Kinematic and Directional Dependent Process Parameters

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Rajan Narang,
  • Shivam Rohilla,
  • Ashwani Kumar Dhingra,
  • Ravinder Kumar Sahdev,
  • Deepak Chhabra

摘要

Fused Deposition Modeling (FDM) is widely adopted for fabricating polymer components. However, the fatigue strength of FDM printed parts remains a critical limitation for load bearing applications. Existing studies have largely focused on conventional process parameters while the influence of motion related kinematic parameters has received little attention. This study presents a novel and systematic investigation into the combined effects of two kinematic parameters (acceleration and jerk) and a directional dependent parameter (hatching angle) on the fatigue strength of FDM fabricated acrylonitrile butadiene styrene (ABS) parts. An optimal (custom) response surface based experimental design was employed followed by tensile–tensile fatigue testing in accordance with ASTM standards. Analysis of variance revealed that hatching angle is the most influential parameter followed by jerk and acceleration. A hybrid genetic algorithm–artificial neural network (GA-ANN) framework was developed to capture the nonlinear interactions among parameters and to optimize fatigue performance. The proposed GA-ANN model demonstrated superior prediction accuracy compared to conventional RSM and standalone ANN approaches. The maximum experimental fatigue strength within the design space was 11.67 MPa whereas the GA-ANN optimized parameter combination (acceleration of 211.32 mm/s2, jerk of 7.48 mm/s, and hatching angle of 450/-450) achieved a significantly higher fatigue strength of 13.58 MPa, which was experimentally validated with a prediction error of only 0.59%. The findings establish kinematic parameters and directional dependent parameter as critical design variables and provide an effective data driven framework for enhancing the fatigue performance of FDM fabricated components without material modification or post-processing.