Additive manufacturing with an emphasis on 3D printing has recently become popular due to its exceptional advantages over conventional manufacturing processes. However, 3D printing process parameters are challenging to optimize, as they influence the properties and usage time of printed parts. Therefore, it is a complex task to develop a correlation between process parameters and printed parts’ properties via traditional optimization methods. A machine-learning technique was recently validated to carry out intricate pattern identification and develop a deterministic relationship, eliminating the need to develop and solve physical models. In machine learning, artificial neural network (ANN) is the most widely utilized model, owing to its capability to solve large datasets and strong computational supremacy. This study compiles the advancement of ANN in several aspects of 3D printing. Challenges while applying ANN in 3D printing and their potential solutions are indicated. Finally, upcoming trends for the application of ANN in 3D printing are projected.

Additive manufacturing (AM) processes can produce three-dimensional (3D) near-net-shape parts based on computer-aided design (CAD) models. Compared to traditional manufacturing processes, AM processes can generate parts with intricate geometries, operational flexibility and reduced manufacturing time, thus saving time and money. On the other hand, AM processes face complex issues, including poor surface finish, unwanted microstructure phases, defects, wear tracks, reduced corrosion resistance and reduced fatigue life. These problems prevent AM parts from real-time operational applications. Post-processing techniques, including laser shock peening, laser polishing, conventional machining methods and thermal processes, are usually applied to resolve these issues. These processes have proved their capability to enhance the surface characteristics and physical and mechanical properties. In this study, various post-processing techniques and their implementations have been compiled. The effect of post-processing techniques on additively manufactured parts has been discussed. It was found that laser shock peening (LSP) can cause severe strain rate generation, especially in thinner components. LSP can control the surface regularities and local grain refinement, thus elevating the hardness value. Laser polishing (LP) can reduce surface roughness up to 95% and increase hardness, collectively, compared to the as-built parts. Conventional machining processes enhance surface quality; however, their influence on hardness has not been proved yet. Thermal post-processing techniques are applied to eliminate porosity up to 99.99%, increase corrosion resistance, and finally, the mechanical properties’ elevation. For future perspectives, to prescribe a particular post-processing technique for specific defects, standardization is necessary. This study provides a detailed overview of the post-processing techniques applied to enhance the mechanical and physical properties of AM-ed parts. A particular method can be chosen based on one’s requirements.

Metal matrix composites (MMCs) present extraordinary characteristics, including high wear resistance, excellent operational properties at elevated temperature, and better chemical inertness as compared to traditional alloys. These properties make them prospective candidates in the fields of aerospace, automotive, heavy goods vehicles, electrical, and biomedical industries. MMCs are challenging to process via traditional manufacturing techniques, requiring high cost and energy. The laser-melting deposition (LMD) has recently been used to manufacture MMCs via rapid prototyping, thus, solving these drawbacks. Besides the benefits mentioned above, the issues such as lower ultimate tensile strength, yield strength, weak bonding between matrix and reinforcements, and cracking are still prevalent in parts produced by LMD. In this article, a detailed analysis is made on the MMCs manufactured via LMD. An illustration is presented on the LMD working principle, its classification, and dependent and independent process parameters. Moreover, a brief comparison between the wire and powder-based LMDs has been summarized. Ex- and in-situ MMCs and their preparation techniques are discussed. Besides this, various matrices available for MMCs manufacturing, properties of MMCs after printing, possible complications and future research directions are reviewed and summarized.

Laser melting deposition (LMD) is a promising technology to produce net-shape parts. The deposited layers' characteristics and induced residual stress distribution influence the quality, mechanical, and physical properties of the manufactured parts. In this study, two theoretical models are presented. Initially, the clad geometry of the 1st deposited layer is estimated using the primary process parameters. Then, a hatch distance is used to calculate the re-melting depth and total clad geometry for all the deposited layers. The output of the 1st model is then used as an input in the 2nd model to estimate the residual stress distribution within the substrate and deposited layers. The model, for clad geometry, is validated using published experimental data for the depositions of AISI316L powder debits on AISI321 bulk substrate by the LMD process. For the residual stress distribution model validation, the published experimental results for X-ray diffractometry, in case of AISI4340 steel powder debits depositions on the AISI4140 bulk substrate by the LMD setup, are used. It was found that the current models can estimate the clad geometry and induced residual stress distribution with an accuracy of 10–15 % mean absolute deviation. An optimum selection of hatch distance is necessary for proper energy density utilization and dimensional control stability. The induced residual stress distribution was caused by the heating and cooling mechanisms, which appeared due to rapid heating and moderate cooling, in combination with slow conduction. These phenomena became incrementally iterative with the number of layers to be deposited, thus presenting a direct relationship between the residual stress distribution and the number of layers deposited on the substrate. The proposed models have high computational efficiency without restoring the meshing and iterative calculations. The high prediction accuracy and computational efficiency allow the presented model to investigate further the part distortion, part porosity, life-expectancy and mechanical properties of the part, and process parameter planning.