In recent decades, laser additive manufacturing has seen rapid development and has been applied to various fields, including the aerospace, automotive, and biomedical industries. However, the residual stresses that form during the manufacturing process can lead to defects in the printed parts, such as distortion and cracking. Therefore, accurately predicting residual stresses is crucial for preventing part failure and ensuring product quality. This critical review covers the fundamental aspects and formation mechanisms of residual stresses. It also extensively discusses the prediction of residual stresses utilizing experimental, computational, and machine learning methods. Finally, the review addresses the challenges and future directions in predicting residual stresses in laser additive manufacturing.

Laser-directed energy deposition (DED), a metal additive manufacturing method, is renowned for its role in repairing parts, particularly when replacement costs are prohibitive. Ensuring that repaired parts avoid residual stresses and deformation is crucial for maintaining functional integrity. This study conducts experimental and numerical analyses on trapezoidal shape repairs, validating both the thermal and mechanical models with experimental results. Additionally, the study presents a methodology for creating a toolpath applicable to both the DED process and Abaqus CAE software. The findings indicate that employing a pre-heating strategy can reduce residual stresses by over 70% compared to no pre-heating. However, pre-heating may not substantially reduce final distortion. Notably, final distortion can be significantly mitigated by pre-heating and subsequently cooling to higher temperatures, thereby reducing the cooling rate. These insights contribute to optimizing DED repair processes for enhanced part functionality and longevity.

In directed energy deposition (DED), accurately controlling and predicting melt pool characteristics is essential for ensuring desired material qualities and geometric accuracies. This paper introduces a robust surrogate model based on recurrent neural network (RNN) architectures—Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), and Gated Recurrent Unit (GRU). Leveraging a time series dataset from multi-physics simulations and a three-factor, three-level experimental design, the model accurately predicts melt pool peak temperatures, lengths, widths, and depths under varying conditions. RNN algorithms, particularly Bi-LSTM, demonstrate high predictive accuracy, with an R-square of 0.983 for melt pool peak temperatures. For melt pool geometry, the GRU-based model excels, achieving R-square values above 0.88 and reducing computation time by at least 29%, showcasing its accuracy and efficiency. The RNN-based surrogate model built in this research enhances understanding of melt pool dynamics and supports precise DED system setups.

Purpose: This paper aims to investigate the thermal behavior and amorphous stability of zirconium-based metallic glass (Zr65.7Cu15.6Ni11.7Al3.7Ti3.3) fabricated using the laser foil printing (LFP) technique. This study focuses on establishing the relationship between laser scanning strategies and melt pool cooling rates, emphasizing their influence on amorphous retention and crystallization behavior during multilayer deposition on Zr702 substrates. Design/methodology/approach: In-situ thermography using a mid-wavelength infrared camera was applied to monitor the real-time temperature evolution of the melt pool and heat-affected zone. Finite element simulations were performed to analyze melt pool temperature gradients and transient heat transfer. Continuous and discrete welding strategies were compared to evaluate their effects on cooling rate and amorphous stability. Postprocess characterization, including X-ray diffraction, energy-dispersive spectroscopy and microhardness testing, was conducted to assess structural, compositional and mechanical changes induced by different thermal conditions. Findings: Continuous welding produced larger and deeper melt pools due to sustained energy input, resulting in significant heat accumulation, and lower cooling rates that led to partial crystallization. In contrast, discrete welding achieved localized and intermittent energy input, generating higher and more stable cooling rates that effectively suppressed heat buildup. Consequently, fully amorphous structures were retained across multiple layers. Compositional analysis revealed a gradual decrease in zirconium content and enrichment of nickel and copper with increasing build height, while microhardness testing confirmed higher hardness values in samples fabricated using discrete welding. Originality/value: This study provides new insights into the correlation between laser scanning strategy and thermal dynamics in the LFP processing of metallic glass. The findings demonstrate that discrete welding offers superior thermal control, enhanced amorphous retention and improved mechanical performance. The established process–structure–property relationship offers practical guidelines for advancing additive manufacturing of bulk metallic glasses with high thermal stability and reliability.