Room-temperature laser shock peening without coating (RT-LSPwoC) is the mainstream method for surface strengthening, yet it induces tensile residual stress on the surface during rapid heating and cooling. Warm LSPwoC, combining the benefits of dynamic strain aging and LSPwoC, exhibits higher performance than RT-LSPwoC. However, overall high-temperature is time- and resource-consuming, and it is prone to causing thermal stress relaxation during processing, making it unsuitable for large-scale applications. In this paper, a novel localized heating assisted LSPwoC (LHA-LSPwoC) method, utilizing a continuous wave laser for local heating, is proposed. Additionally, the deep physics-informed neural network model, embedded with physical formulas, is designed for process optimization. It enables rapid and accurate responses for an extensive number of cases (40 cases with an average deviation below 1%), overcoming the drawbacks of traditional numerical simulations that are time-consuming and difficult to efficiently handle numerous cases. Compared to RT-LSPwoC, LHA-LSPwoC samples have higher dislocation density and higher grain refinement, demonstrating higher hardness and residual stress, particularly superior fatigue performance. The mechanism of LHA-LSPwoC is discussed, and thermally coupled finite element simulations and molecular dynamics calculations are conducted to provide theoretical support. The proposed method is cost-effective and flexible, showing outstanding potential for industrial applications.
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