<p>Metal Additive Manufacturing (MAM) has experienced significant growth in recent years due to its ability to fabricate complex geometries with minimal material waste, compared to traditional manufacturing technologies. It is especially prevalent in the automotive, aerospace, and biomedical industries because of its capabilities and flexibility to accommodate lightweight design, rapid prototyping, and design customisation. However, the quality of additively manufactured metal parts is often compromised by defects such as cracks and distortions, resulting from high residual stresses caused by extreme temperatures and rapid cooling during the additive manufacturing process. These challenges have slowed the widespread adoption of AM for structurally critical components. In addition, qualification and certification processes remain expensive and time-consuming. To address these issues, this study proposes a novel high-accuracy dual-cantilever calibration inherent strain method (ISM) that can anticipate and mitigate potential defects prior to fabrication for the Direct Metal Laser Sintering (DMLS) process of 17 − 4 PH stainless steel. The novel method integrates multiscale modelling and simulations across three length scales including a microscale model to capture the dynamics of the melt pool and thermal gradients, a mesoscale model to evaluate residual stresses and a macroscale model for a distributed orthotropic mechanical calibration to apply for the dual cantilever using an inherent-strain method. The calibrated inherent strain is then used to forecast deformation, providing a practical tool for design engineers to enhance the quality of 3D printed components. Deformation can be minimised by optimising key process parameters. The devise calibration process is validated via conducting the additive manufacturing of a dual-cantilever beam with 17 − 4 PH stainless steel as an efficient and effective approach.</p>

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Minimising residual stresses in direct metal laser sintering: a novel dual-cantilever calibration method for 17 − 4 PH stainless steel

  • Raju Majji,
  • Y.X. Zhang,
  • Zhongpu Zhang,
  • Richard Chunhui Yang

摘要

Metal Additive Manufacturing (MAM) has experienced significant growth in recent years due to its ability to fabricate complex geometries with minimal material waste, compared to traditional manufacturing technologies. It is especially prevalent in the automotive, aerospace, and biomedical industries because of its capabilities and flexibility to accommodate lightweight design, rapid prototyping, and design customisation. However, the quality of additively manufactured metal parts is often compromised by defects such as cracks and distortions, resulting from high residual stresses caused by extreme temperatures and rapid cooling during the additive manufacturing process. These challenges have slowed the widespread adoption of AM for structurally critical components. In addition, qualification and certification processes remain expensive and time-consuming. To address these issues, this study proposes a novel high-accuracy dual-cantilever calibration inherent strain method (ISM) that can anticipate and mitigate potential defects prior to fabrication for the Direct Metal Laser Sintering (DMLS) process of 17 − 4 PH stainless steel. The novel method integrates multiscale modelling and simulations across three length scales including a microscale model to capture the dynamics of the melt pool and thermal gradients, a mesoscale model to evaluate residual stresses and a macroscale model for a distributed orthotropic mechanical calibration to apply for the dual cantilever using an inherent-strain method. The calibrated inherent strain is then used to forecast deformation, providing a practical tool for design engineers to enhance the quality of 3D printed components. Deformation can be minimised by optimising key process parameters. The devise calibration process is validated via conducting the additive manufacturing of a dual-cantilever beam with 17 − 4 PH stainless steel as an efficient and effective approach.