Artificial intelligence provides novel perspectives for laser metal additive manufacturing (LMAM), enhancing precision, efficiency, and structural and process optimization. Machine learning-assisted process–structure–property correlation in additive manufacturing (ML-PSP-AM) presents an effective pathway for structural innovation and performance optimization, leveraging automation and intelligence to address the growing processing demands across industries. This review differs from the existing literature by presenting a multi-scale, PSP-centered analysis of ML applications in LMAM, integrating discussions that span from processing-driven macro-scale formation to meso/micro-scale defect prediction and microstructure–property relationships. By evaluating state-of-the-art ML applications across various AM stages, we identify current limitations, propose targeted strategies, and outline opportunities to improve accuracy, minimize defects, and enhance mechanical properties such as strength and fatigue life. The advancement of ML-assisted AM should focus on breakthroughs from “0 to 1” in application and innovations from “1 to ∞” in algorithms. The realization of ML-PSP-AM represents a transformative yet disruptive integration of manufacturing engineering, artificial intelligence, and materials science, driving significant progress in modern manufacturing technologies.

On-machine inspection has a significant impact on improving high-precision and efficient machining of sculptured surfaces. Due to the lack of machining information and the inability to adapt the parameters to the dynamic cutting conditions, theoretical modeling of profile inspection usually leads to insufficient adaptation, which causes inaccuracy problems. To address the above issues, a novel coupled model for profile inspection is proposed by combining the theoretical model and the data-driven model. The key process is to first realize local feature extraction based on the acquired vibration signals. The hybrid sampling model, which fuses geometric feature terms and vibration feature terms, is modeled by the lever principle. Then, the weight of each feature term is adaptively assigned by a multi-objective multi-verse optimizer. Finally, an inspection error compensation model based on the attention mechanism considering different probe postures is proposed to reduce the impact of pre-travel and radius errors on inspection accuracy. The anisotropy of the probe system error and its influence mechanism on the inspection accuracy are analyzed quantitatively and qualitatively. Compared with the previous models, the proposed hybrid profile inspection model can significantly improve the accuracy and efficiency of on-machine sampling. The proposed compensation model is able to correct the inspection errors with better accuracy. Simulations and experiments demonstrate the feasibility and validity of the proposed methods. The proposed model and corresponding new findings contribute to high-precision and efficient on-machine inspection, and help to understand the coupling mechanism of inspection errors.

Additive manufacturing provides achievability for the fabrication of bimetallic and multi-material structures; however, the material compatibility and bondability directly affect the parts' formability and final quality. It is essential to understand the underlying printability of different material combinations based on an adapted process. Here, the printability disparities of two common and attractive material combinations (nickel- and iron-based alloys) are evaluated at the macro and micro levels via laser directed energy deposition (DED). The deposition processes were captured using in situ high-speed imaging, and the dissimilarities in melt pool features and track morphology were quantitatively investigated within specific process windows. Moreover, the microstructure diversity of the tracks and blocks processed with varied material pairs was comparatively elaborated and, complemented with the informative multi-physics modeling, the presented non-uniformity in mechanical properties (microhardness) among the heterogeneous material pairs was rationalized. The differences in melt flow induced by the unlike thermophysical properties of the material pairs and the resulting element intermixing and localized re-alloying during solidification dominate the presented dissimilarity in printability among the material combinations. This work provides an in-depth understanding of the phenomenological differences in the deposition of dissimilar materials and aims to guide more reliable DED forming of bimetallic parts.