The TC4/AlSi12 bimetallic structures (BS) with Nb interlayer transition were fabricated by laser additive manufacturing (LAM). The results showed that the TC4/AlSi12 BS with Nb interlayer prepared with optimized process parameters can be divided into three regions (the TC4 region, Nb region and the AlSi12 region) and two interfaces (the TC4/Nb interface and the Nb/AlSi12 interface). The high melting point (Ti, Nb) solid solution formed in the Nb region acted as a diffusion barrier between the TC4 alloy and the AlSi12 alloy, thereby effectively inhibiting the formation of Ti-Al intermetallic compounds (IMCs). With the decrease of the laser output power for AlSi12 deposition, the NbAl₃ IMC changed from layered to dispersed distribution, while γ-TiAl and Ti₅Si₃ IMC disappeared, thus significantly reducing the crack susceptibility of the BS deposited layer. The tensile strength of TC4/AlSi12 BS with Nb interlayer was about 128MPa, and the fracture was located near the Nb/AlSi12 interface.

Laser metal deposition is a branch of additive manufacturing that offers advantages over traditional manufacturing techniques for forming overhanging thin-walled metal parts. Previously, helical paths that were suitable for manufacturing such parts were not only limited to stacking material on a flat surface but were also fixed to the model boundaries. In order to solve these two problems to meet more complex process requirements, a non-planar helical path generation method is proposed for laser metal deposition. The method is based on the characteristics of the additive manufacturing process planning flow, which first slices the model using curved surfaces, then offsets the contours on the sliced layering, and finally generates continuous helical paths according to the contracted or expanded contours. In order to verify the feasibility of the method, hollow blades are formed on cylindrical surfaces following the planned paths. The results show that the proposed method is not only capable of assisting the laser metal deposition process to fabricate thin-walled structures on non-planar surfaces but also capable of freely adjusting the contour dimension.

Additive manufacturing is capable of forming part solids from a digital model of the workpiece, which often needs to be sliced due to the nature of the process of stacking materials layer by layer. Previous algorithms require more time to order the intersection points during slicing and only consider a fixed axis vector as the slicing direction. With the increasing complexity of the workpiece model, it is necessary to improve the efficiency of the slicing algorithm, and the given slicing direction should be flexible and convenient. Therefore, this paper proposes an efficient algorithm for slicing in any direction using the shared edges of adjacent triangles. Firstly, the model can be efficiently sliced in any direction by custom vectors, which is convenient for process validation; secondly, the shared edges of neighboring triangular facets intersecting the plane need to be calculated only once to reduce redundant calculations; finally, the ordered intersections of the constructed inner and outer contours are directly outputted without any additional sorting to reduce the running time. The experimental results show that compared with other algorithms, the proposed algorithm can improve the execution efficiency and eliminate the constraint that the slicing direction is fixed on the Z-axis, which is of application value and reference significance for additive.

The integration of Selective Laser Melting (SLM) into high-end manufacturing necessitates robust machine-condition monitoring and subsystem fault classification to navigate the intricate coupling and dynamic transients inherent in these systems. Current diagnostic frameworks often struggle to decouple high-dimensional state variables or track their underlying temporal evolution. To overcome these bottlenecks, this paper develops a spatiotemporal deep learning architecture by coupling Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units. This hybrid approach leverages CNNs to distill multi-dimensional spatial features from subsystem sensor arrays, while LSTMs interpret the sequential dependencies critical for identifying systemic drifts. The proposed framework was validated using an extensive industrial dataset comprising over 310,000 temporal samples across seven critical SLM subsystems, including optical, cooling, and energy units. We systematically investigated three data-handling strategies—feature weighting, balancing, and distribution-based synthesis—to optimize the model’s sensitivity to rare-event anomalies. Benchmarking across six architectural variants reveals that a specific CNN × 3 + LSTM × 1 configuration yields superior diagnostic fidelity, achieving a classification accuracy of 98.81%. Visualization of the feature space confirms high inter-class separability, demonstrating the model’s ability to isolate faults within complex manufacturing cycles. This research offers a scalable paradigm for the intelligent monitoring of SLM equipment and provides a technical benchmark for equipment health management and predictive maintenance in advanced additive manufacturing.

With the intelligent upgrading of manufacturing equipment, high precision and efficiency fault diagnosis improves the stability and productivity. To detect the fault state accurately, fault diagnosis methods based on dual-model fusion are widely used. Actually, fault diagnosis needs to response fast, so high efficiency of fusion model requirements is imminent. The fusion model may have complex interrelationships and dependencies between multiple parameters, which impact on diagnostic accuracy. Therefore, a dual-model fusion method based on DBTBoost for fault diagnosis is proposed. Firstly, based on discrete gradient boosting method, multiple model weak classifiers are constructed, and the results of the classifiers are integrated through the Top-K mechanism to initially construct the dual-model fusion single-objective function. Secondly, based on the dynamic Bayesian multi-parameters optimization method, the multi-objective parameters are adjusted globally to construct the optimal fusion model. Finally, the model performance is verified by model accuracy and efficiency evaluation indexes. The method is practically verified in the fault diagnosis of additive manufacturing (AM) equipment. The experimental results show that the dual-model fusion method based on DBTBoost achieves 99.45% accuracy, with diagnosis time of 0.65 s and training time of 503s. The method improves the accuracy by 4.92% and efficiency by 41.4%.