A focused review on numerical computation in wire arc additive manufacturing for high strength low alloy steels: past insights and potential opportunities

Wire Arc Additive Manufacturing (WAAM) has emerged as a transformative technology in Metal Additive Manufacturing (MAM), offering significant advantages for fabricating large, complex metal structures that are often difficult or economically unfeasible to produce using traditional methods. Despite its cost-effectiveness and design flexibility, WAAM continues to rely heavily on trial-and-error attempts to achieve high-quality results, leading to significant time, effort and financial costs, particularly when producing large-scale parts. High Strength Low Alloy (HSLA) steels, known for their superior strength-to-weight ratios, mechanical properties and corrosion resistance, have become essential in industries where high performance is critical, such as aerospace, automotive and construction. The integration of HSLA steels into WAAM presents unique challenges that require precise predictions of material behavior and process dynamics. Recent advancements in numerical computation have significantly enhanced the understanding and optimization of WAAM process and have been disseminated in various publications. However, existing research on dedicated topics is often fragmented and lacks sufficient integration, which limits the potential for comprehensive insights. This review synthesizes the state of the art in research from 2020 to mid-2025, with a particular focus on integrating numerical computation, HSLA and WAAM. Through the application of staggered scaling methods, significant strides have been made in predicting critical outputs such as temperature distribution, residual stresses, part distortion and microstructural evolution at the grain level. Building on past insights, emerging research trends are focusing on more advanced methods to further optimize the WAAM process. One exciting direction is the use of Hybrid Physics-Informed Neural Networks (PINNs), which integrate neural networks, governing physical laws, analytical models and data-driven methods to offer more accurate and efficient process control. Although still in its early stages, this methodology provides an opportunity to address existing gaps in material performance and process optimization. By leveraging past insights and emerging computational methods, future research holds the potential to significantly advance the industrial adoption of HSLA-WAAM, enabling the production of parts with unprecedented design flexibility, customized material performance and enhanced reliability. This could reshape the manufacturing landscape and position manufacturing modelling and MAM as cornerstones of next-generation manufacturing technology.