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<p>The uncertainty and lack of repeatability of the additive manufacturing (AM) processes have brought significant issues hindering the wider industrial adoption of these novel fabrication techniques. The process-induced defects have compromised the structural integrity of the specimens where the mechanical properties have failed to reach the required industrial qualifications. The uncertainty within the AM processes has caused issues such as variation in part quality. Therefore, it is crucial to develop a robust model to address the issue of uncertainty. Our objective in this study is to pave the way toward a better understanding of the uncertainty in the process-defect-structures relationship using an inverse robust design exploration method. The method involves two steps. In the first step, mathematical models are developed to characterize and model the forward flow of information in the intended additive manufacturing process. In the second step, inverse robust design exploration is carried out to investigate satisfying design solutions that meet multiple AM goals. We test the utility of the method for an AM problem; in the first step, a two-phase methodology is developed to predict the fatigue life from initial process parameters through intermediate process-induced defect properties such as maximum defect size. In the second step, we carry out robust inverse design exploration to maximize the predefined fatigue-related goals while managing the uncertainty in the design variables. The model is implemented for fatigue experiments of Ti-6AL-4&#xa0;V material, and the results of satisfying design solutions are reported.</p>

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A robust design exploration approach for the integrated inverse design of additive manufacturing process chain

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Seyyed Hadi Seifi,
  • Anand Balu Nellippallil,
  • Wenmeng Tian,
  • Aref Yadollahi,
  • Linkan Bian,
  • Raj K. Prabhu

摘要

The uncertainty and lack of repeatability of the additive manufacturing (AM) processes have brought significant issues hindering the wider industrial adoption of these novel fabrication techniques. The process-induced defects have compromised the structural integrity of the specimens where the mechanical properties have failed to reach the required industrial qualifications. The uncertainty within the AM processes has caused issues such as variation in part quality. Therefore, it is crucial to develop a robust model to address the issue of uncertainty. Our objective in this study is to pave the way toward a better understanding of the uncertainty in the process-defect-structures relationship using an inverse robust design exploration method. The method involves two steps. In the first step, mathematical models are developed to characterize and model the forward flow of information in the intended additive manufacturing process. In the second step, inverse robust design exploration is carried out to investigate satisfying design solutions that meet multiple AM goals. We test the utility of the method for an AM problem; in the first step, a two-phase methodology is developed to predict the fatigue life from initial process parameters through intermediate process-induced defect properties such as maximum defect size. In the second step, we carry out robust inverse design exploration to maximize the predefined fatigue-related goals while managing the uncertainty in the design variables. The model is implemented for fatigue experiments of Ti-6AL-4 V material, and the results of satisfying design solutions are reported.