The growing consumption of oil and gas resources and the increasing difficulty of extraction have created major challenges for traditional manufacturing and maintenance, particularly in the timely supply of critical components, customized production, and complex structure fabrication. Additive manufacturing (AM) technology, with its high design freedom, precision, and rapid prototyping, provides new approaches to address these issues. However, systematic reviews of related efforts are scarce. This paper reviews the applications and progress of metal and non-metal AM technologies in oil and gas extraction and gathering engineering, focusing on the just-in-time (JIT) manufacturing of failed components, the manufacturing and repair of specialized equipment and tools for oil and gas extraction and gathering, and artificial core and reservoir geological modeling fabrication. AM applications in this field remain exploratory and face challenges with regard to their standards, supply chains, materials, and processes. Future research should emphasize developing materials and processes for extreme conditions, optimizing process parameters, establishing standards and traceability systems, and integrating AM with digital design and reverse engineering to support efficient, safe, and sustainable industry development. This work aims to provide a reference for advancing AM research and engineering applications in the oil and gas sector.

In this study, TiB₂/AlSi10Mg, 2 wt.% SiC + TiB₂/AlSi10Mg, and 5 wt.% SiC + TiB₂/AlSi10Mg composite powders were prepared via high-energy ball milling. For the first time, TiB₂ and SiC hybrid particle-reinforced aluminum matrix composites (AMCs) were fabricated using the Laser-Directed Energy Deposition (LDED) technique. The effects of processing parameters on the microstructure evolution and mechanical properties were systematically investigated. Using areal energy density as the main variable, the experiments combined microstructural characterization and mechanical testing to elucidate the underlying strengthening and failure mechanisms. The results indicate that both 2 wt.% and 5 wt.% SiC + TiB₂/AlSi10Mg composites exhibit excellent formability, achieving a relative density of 98.9%. However, the addition of 5 wt.% SiC leads to the formation of brittle Al₄C₃ and TiC phases within the matrix. Compared with the LDED-fabricated AlSi10Mg alloy, the tensile strength of the TiB₂/AlSi10Mg composite increased by 21.4%. In contrast, the tensile strengths of the 2 wt.% and 5 wt.% SiC + TiB₂/AlSi10Mg composites decreased by 3.7% and 2.6%, respectively, mainly due to SiC particle agglomeration and the consumption of TiB₂ particles caused by TiC formation. Nevertheless, their elastic moduli were enhanced by 9% and 16.3%, respectively. Fracture analysis revealed that the composites predominantly exhibited ductile fracture characteristics. However, pores larger than 10 μm and SiC/TiB₂ clusters acted as crack initiation sites, inducing stress concentration and promoting the propagation of secondary cracks.

The prevailing screening and qualification methodologies heavily depend on conventional manufacturing processes, which incur significant costs and prolonged lead times due to extensive physical testing. These challenges are also present in the growing field of additive manufacturing (AM), where numerous process parameters must be considered. However, the net-shape forming advantage of AM renders conventional screening and qualification methods inadequate. In the context of ongoing industrial digital transformation, a promising approach to enhancing process screening and qualification for metal AM is the adoption of a digital methodology tailored to the unique characteristics of this manufacturing technique. In this study, a convolutional neural network model is employed to extract features from images to predict material properties in laser-directed energy deposition (L-DED) processes. The model achieved a mean absolute percentage error of 2.3% and a root mean square error of 15.0 MPa for predicting ultimate tensile strength, with a prediction residual within ±1% for density. Unlike conventional approaches that rely on bulk or multilayer builds, this study uniquely demonstrates the feasibility of using early-stage single-track print features to predict final part properties with limited view and material involvement. This established model and workflow pave the way for highly efficient and low-cost property prediction in L-DED processes.