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.

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.