Current Status and Way Forward of Microwave Hybrid Heating in India: A Bibliometric Analysis

Automotive and aerospace industries are keen to employ unique techniques and novel materials to reduce weight and cost and improve part performance. The application of microwave technology in the processing of metallic materials is a relative breakthrough in this direction. Recently, microwave hybrid heating (MHH) has evolved to extend the technique's utility further. Several studies on MHH have been carried out worldwide in the last three decades, and India is the prime contributor. This article documents a systematic and bibliographic review of MHH (between 1998-2022) in the Indian scenario. For this purpose, 125 documents are chosen from Scopus Core Collection and analyzed using a bibliometric analysis tool. The research status is examined based on the time distribution of articles, geography, top-cited documents, citation mapping of journals and researchers, mapping of co-occurrence, analysis of authors' keywords, country-wise publications, and cluster assessment. The result establishes that India is dominating, followed by theUSA. Moreover, there is an increasing trend in the number of publications. A guideline is also included to revive the research community's interest to mature the process further

Experimental investigation on surface modification of aluminum by electric discharge coating process using TiC/Cu green compact tool-electrode

This article presents the surface modification of aluminum workpiece by electric discharge coating (EDC) process using TiC /Cu green compact tool-electrode. Powder metallurgy (P/M) tool-electrodes were used to create hard layer(s) on the workpiece surface by a material transfer from this tool-electrode (produced with powder metallurgy) to the workpiece surface it was possible to create hard layer(s) there with high tribological properties and integrity. The present study has been carried out to investigate the effect of input parameters (peak current, pulse-on time, composition and compaction pressure of the tool-electrode) on process performance parameters (roughness and micro-hardness of workpiece surface and coating layer thickness). Regression models have been developed to define a mathematical correlation between input and process performance parameters. Analysis of variance has been conducted at the 95% confidence level, to test the significance of these models. The results of the experiments indicate that the regression models are highly significant. Confirmation experiments were conducted to validate the developed model. By the realization of ‘‘confirmation experiments’’, it became evident that the developed regression models were correct and acceptable. Moreover, EDX analysis of worksample obtained of the workpiece surface has been carried out to confirm the process of material transferring mentioned above

Empirical Modeling of Average Cutting Speed during WEDM of Gas Turbine Alloy

Cutting speed (CS) is a key performance measure to achieve optimal utilization of the WEDM process. However, input process parameters of WEDM and combination of wire and workpiece material greatly hamper CS and hence productivity and machining efficiency. Therefore, it is essential to pick the right combination of parameters and wire and workpiece material to obtain better CS. In this paper, four process parameters: Ton, Toff, Sv, and Ip were chosen to develop an empirical model for CS during WEDM of nimonic 263 to provide a guideline to the potential users of the technique. This paper describes the response surface methodology (RSM) based mathematical modeling for average cutting speed. Furthermore, analysis of variance (ANOVA) was applied to find out significant process parameters and it was depicted that pulse on time and peak current were the major parameters affecting CS. In addition, WEDMed surfaces were analysed through FE-SEM at various discharge energy levels. The WEDMed surfaces appeared in the form of micro-cracks, craters, spherical droplets and the lump of debris. It is obvious from the current investigation that input parameters have the significant influence on cutting speed. The key features of experimental procedure are also highlighted in this paper

Empirical Modeling of Average Cutting Speed during WEDM of Gas Turbine Alloy

Cutting speed (CS) is a key performance measure to achieve optimal utilization of the WEDM process. However, input process parameters of WEDM and combination of wire and workpiece material greatly hamper CS and hence productivity and machining efficiency. Therefore, it is essential to pick the right combination of parameters and wire and workpiece material to obtain better CS. In this paper, four process parameters: Ton, Toff, Sv, and Ip were chosen to develop an empirical model for CS during WEDM of nimonic 263 to provide a guideline to the potential users of the technique. This paper describes the response surface methodology (RSM) based mathematical modeling for average cutting speed. Furthermore, analysis of variance (ANOVA) was applied to find out significant process parameters and it was depicted that pulse on time and peak current were the major parameters affecting CS. In addition, WEDMed surfaces were analysed through FE-SEM at various discharge energy levels. The WEDMed surfaces appeared in the form of micro-cracks, craters, spherical droplets and the lump of debris. It is obvious from the current investigation that input parameters have the significant influence on cutting speed. The key features of experimental procedure are also highlighted in this paper

Empirical Modeling of Average Cutting Speed during WEDM of Hastelloy C22

Cutting speed (CS) is a key performance measure to achieve optimal utilization of the WEDM process. However, input process parameters of WEDM and combination of wire and workpiece material greatly hamper CS and hence productivity and machining efficiency. Therefore, it is essential to pick the right combination of parameters and wire and workpiece material to obtain better CS. In this paper, four process parameters: Pulse-on time, Pulse-off time, Spark-gap voltage, and Peak current were chosen to develop an empirical model for CS during WEDM of Hastelloy C22 to provide a guideline to the potential users of the technique. This paper describes the response surface methodology (RSM) based mathematical modeling for average cutting speed. Furthermore, analysis of variance (ANOVA) was applied to find out significant process parameters and it was depicted that pulse on time and peak current were the major parameters affecting CS

Performance evaluation of friction stir welding using machine learning approaches

The aim of the present study is to evaluate the potential of sophisticated machine learning methodologies, i.e. Gaussian process (GPR) regression, support vector machining (SVM), and multi-linear regression (MLR) for ultimate tensile strength (UTS) of friction stir welded joint. Three regression models are developed on the above methodologies. These models are projected to study the incongruity between the experimental and predicted outcomes and preferred the preeminent model according to their evaluation parameter performances. Out of 25 readings, 19 readings are selected for training models whereas remaining is used for testing models. Input process parameters consist of rotational speed (rpm), and feed rate (mm/min) whereas UTS is considered as output. Two kernel functions i.e. Pearson VII (PUK) and radial based kernel function (RBF) are used with both GPR and SVM regression. It is concluded that the GPR approach works better than SVM and MLR techniques. Therefore, GPR approach is used successfully for predicting the UTS of FS welded joint. • The aim of the present study is to evaluating the friction stir welding process using sophisticated machine learning methodology, i.e. Gaussian process (GP) regression, support vector machining (SVM) and multi-linear regression (MLR). • Three models are projected to study the incongruity between the experimental and predicted outcomes and preferred the preeminent model according to their evaluation parameter performances. Two kernel functions i.e. Pearson VII (PUK) and radial based kernel function (RBF) are used with both GPR and SVM regression. • GPR approach works better than SVM and MLR techniques. Therefore, GPR approach is used successfully for predicting the UTS of FS welded joint. Method name: Modelling of Friction Stir Welding Process, Keywords: Ultimate tensile strength, Gaussian process regression, Support vector machining, Multi-linear regression, Pearson VII, Radial based kernel functio