Friction stir welding is a method used to weld together materials considered challenging by fusion welding. FSW is primarily a solid phase method that has been proven efficient due to its ability to manufacture low-cost, low-distortion welds. The quality of weld and stresses can be determined by calculating the amount of heat transferred. Recently, many researchers have developed algorithms to optimize manufacturing techniques. These machine learning techniques have been applied to FSW, which allows it to predict the defect before its occurrence. ML methods such as the adaptive neurofuzzy interference system, regression model, support vector machine, and artificial neural networks were studied to predict the error percentage for the friction stir welding technique. This article examines machine learning applications in FSW by utilizing an artificial neural network (ANN) to control fracture failure and a convolutional neural network (CNN) to detect faults. The ultimate tensile strength is predicted using a regression and classification model, a decision tree model, a support vector machine for defecting classification, and Gaussian process regression (UTS). Machine learning implementation mainly promotes uniformity in the process and precision and maximally averts human error and involvement.

The main objective of this study is to characterize the effect of infill percentage, printing orientation and raster angle on ABS samples prepared with 3D printing technology. In this research, samples used were fabricated with two different infill percentages (50% & 75%), 6 different raster patterns and three different orientations. The influence of these parameters on tensile properties were studied with the help of 3D printed samples as per ASTM standard D638 Type IV. From the experimental analysis it was found that the tensile properties were highly anisotropic. Stress strain graphs are plotted for all the samples and the variation of strength with respect to the three parameters are analysed. It was observed that Infill density is directly proportional to mechanical properties. Flat and vertical orientation have better strength and stiffness in comparison with vertical build. The experimental results proved that flat oriented samples exhibited variable strength for changes in raster orientation, other samples have shown minimal changes only.

Fused deposition modelling (FDM), one of the most commonly used additive manufacturing techniques in the industry, involves layer-by-layer deposition of melted material to create a 3D structure. The staircase and beading effect caused by the printing process and temperature variation cause delamination and poor surface finish in FDM-printed parts. This hinders the use of these specimens in various applications, which are then usually resolved using pre-processing and post-processing techniques. Higher surface finish in pre-processing is achieved by increasing the resolution, changing layer thickness and optimizing build orientation. However, this increases the processing time considerably. On the other hand, post-processing techniques involve different processes such as mechanical, chemical, thermal and hybrid methods but can affect the mechanical and structural properties of the printed components. This review paper analyses three different aspects in the area of improving the surface finish of FDM-printed parts. First, this article reviews the state-of-the-art attempts made to improve the surface finish of FDM-printed parts concentrated mainly on different vapour polishing techniques and their respective merits and demerits. Second, it focuses on the changes in mechanical properties before and after polishing. Finally, the paper explores the development in the 3D printing of thermosets and composite materials and their post-processing processes and process parameters.

Phase change materials are the category of materials that release or absorb enough energy during phase change transformation to provide heating or cooling. Divided into two principal classes of organic and inorganic, these materials find a wide range of uses in commercial applications of casting where stable temperature and heat storage are a requirement. In this research work, application of inorganic phase change materials having significantly elevated temperature zone, especially within metal casting processes, has been discussed. Phase change material with high enthalpy of fusion and high melting point can be used for metal casting, but in a limited temperature range (between 200°C and 1300°C). In sand casting, inorganic PCM has the potential to be used as chills to provide directional solidification. Despite having advantages, inorganic PCM comes with major disadvantages, that is, toxicity, corrosivity, supercooling, and low thermal expansion. Few solutions to overcome these problems have been discussed in this research paper. Future research is required to reduce the disadvantage to a low level, so that PCM can be used in application where elevated temperature is achieved.

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