Research on partition jump path optimization for large-scale metal additive components based on feature partitioning and temperature function method

Abstract

Laser deposition manufacturing (LDM) for large metallic components, especially in aerospace, is expanding significantly. However, thermal accumulation during deposition can induce excessive residual stress, causing warping and cracking. Therefore, quickly predicting and mitigating stress and distortion is essential for achieving high-quality large-scale metal additive manufacturing. Stress fields and distortion distribution in LDM using a feature partitioning method combined with the Temperature Function Method (TFM) are analyzed in this work. Experimental validation confirms its validity and accuracy. The TFM based on feature partitioning achieves exceptional computational efficiency, reducing simulation time by 96.55% compared to traditional methods while maintaining accuracy (6.83% distortion deviation). Building on this efficient TFM approach, the core contribution proposes and systematically evaluates a novel minimum temperature gradient jump strategy (TFM-MG) for active distortion control. TFM-based simulations assess various jump strategies’ effects. Results show the TFM-MG strategy significantly outperforms others: reducing maximum distortion by 29.15% (compared to conventional sequential deposition) and more effectively alleviating residual stress concentrations (>500 MPa), establishing a mechanism linking stress mitigation and distortion suppression. This work provides a foundation for formulating optimal forming paths in large metallic additive LDM.