This article presents a detailed systematic review on the current developments in the machining of superalloys. Superalloys have extensive high temperature applications and research has been conducted in various machining processes. The conventional and nonconventional processing of superalloys is an emerging research area and needs attention. This systematic literature survey intends to consolidate, review, and critically highlight the machining aspects carried out in this field. Bibliometric analysis using BibExcel is conducted to capture new insights in the focused field of research. Articles published since 1991 on material processing of superalloys are listed and referenced. Article statistics on each class of publications have been highlighted. A citation analysis is also reported and the articles with the highest impact are identified. Geographical mapping reveals the role of universities and institutions in the focused research. Gephi is used to identify the key research clusters and topics. Seven prominent clusters were identified and articles in each research cluster have been elaborated. A quality versus quantity analysis of the various publications is done. Present state of art in the focused research and the future directions on the material processing of superalloys based on the review are reported.

Aerospace alloys with reduced wall thickness but possessing higher hardness, good tensile strength and reasonable corrosion resistance are essential in manufacturing of structures such as fuselage. In this work, friction stir welding has been carried out on such an aerospace aluminum alloy AA8090 T87 which contains 2.3% lithium. Tool rotational speed of 900 rpm and traverse speeds of 90 mm/min., 110 mm/min. are the welding parameters. Hardness analysis, surface roughness analysis and corrosion analysis are conducted to analyze the suitability of the joint for the intended application. The samples were corrosion tested in acid alkali solution and they resulted in the formation of pits of varying levels which indicate the extent of surface degradation. Hardness of the samples was measured after corrosion analysis to observe the changes. The analysis suggests that the change in tool traverse speed transformed the corrosion behavior of the joint and affected both the hardness and surface roughness which mitigated the quality of the joint.

Several components are made from Al-Mg-based composites. MoS₂ is used to increase the composite’s machinability. Different weight percent (3, 4, and 5) of MoS₂ are added as reinforcement to explore the machinability properties of Al-Mg-reinforced composites. The wire cut electrical discharge machining (WEDM) process is used to study the machinability characteristics of the fabricated Al-Mg-MoS₂ composite. The machined surface’s roughness and overcut under different process conditions are discussed. The evaluation-based distance from average solution (EDAS) method is used to identify the optimal setting to get the desired surface roughness and overcut. The following WEDM process parameters are taken to determine the impact of peak current, pulse on time, and gap voltage on surface roughness, and overcut. The WEDM tests were carried out on three different reinforced samples to determine the impact of reinforcement on surface roughness and overcut. The surface roughness and overcut increase as the reinforcement level increases, but the optimal parameters for all three composites are the same. According to EDAS analysis, I₃, Ton₂, and V₁ are the best conditions. Furthermore, peak current and pulse on-time significantly influence surface roughness and overcut.

This paper details the selection of machine learning models for predicting the effectiveness of a heat pipe system in a concentric tube exchanger. Heat exchanger experiments with methanol as the working fluid were conducted. The value of the angle varied from 0° to 90°, values of temperature varied from 50 °C to 70 °C, and the flow rate varied from 40 to 120 litres per min. Multiple experiments were conducted at different combinations of the input parameters and the effectiveness was measured for each trial. Multiple machine learning algorithms were taken into consideration for prediction. Experimental data were divided into subsets and the performance of the machine learning model was analysed for each of the subsets. For the overall analysis, which included all the three parameters, the random forest algorithm returned the best results with a mean average error of 1.176 and root-mean-square-error of 1.542.

Different kind of statistical optimization techniques are available for optimizing the different parameters of a CNC end milling process. In this paper a comparison is done between five different techniques such as principal components analysis, utility theory, Grey relational analysis, technique of order preference by similarity to ideal solution and their hybrid variants. The Taguchi optimization principle is common to all the methods which are presented in the paper. The experiments were carried out and the different response features such as surface roughness (Ra, Rz and Rq) and material removal rate (MRR) were measured and the different optimization techniques were applied. Three different surface roughness values are used for the analysis and they act as indices of surface quality whereas MRR acts as index of productivity. Hence the optimization is carried out such that the resulting optimized parameters will lead to a compromise between the productivity and the surface quality. The aim of the work is to carry out multi objective optimization on a single process and compare the results

In this work, molybdenum disilicide (MoSi2) and hard reinforcement particles, namely silicon carbide (SiC), have been used as matrix and reinforcement material, respectively. In general, SiC material is difficult to manufacture using traditional machining methods. As a result, electric discharge machining was utilized to investigate the geometrical tolerance of MoSi2-SiC composites. The orthogonal (L25) design is utilized to investigate the electrical discharge machining (EDM) process parameters such as dielectric pressure, current, gap voltage, pulse-off-time, and pulse-on-time. Material removal rate, surface roughness, wear rate, and drilled hole precision are investigated as measuring parameters. Analysis of variance, based on a determination of the desirability function, has revealed the much more crucial measurable parameter for enhanced output responses. In determining the effectiveness of the machining, the pulse current is significant parameter. The artificial neural network model has been utilized in this research work rather than the standard regression model. An optimal neural network with the highest correlation coefficient (0.949274) is discovered. Furthermore, the sparking EDM method significantly improves the composite desirability ratings for a variety of performance parameters, increasing them from 0.7466 to 0.7778. The improvement in composites desirability values obtained is 0.0312. Finally, a confirmation experiment is utilized to estimate the results, which demonstrate that spark erosion may be improved.