The machined surface integrity in the turning of hardened steels is adversely influenced by heat generation and friction which requires pacification of the temperature by the effective cooling-lubrication approach and cutting tool performance. The present research analyzes the surface integrity of hardened AISI 4140 steel during turning with recently developed scalable pulsed power plasma SPPP-AlTiSiN coated carbide tool under nanofluid-assisted minimum quantity lubrication (MQL). Zinc oxide nanoparticles and environmentally friendly radiator coolant are mixed to prepare the nano cutting fluid. This analysis addresses the various aspects of surface integrity concerning surface morphology, machined surface hardness, residual stress and white layer development, and machined surface finish under varying cutting parameters (depth of cut, speed, feed, nose radius). Response surface methodology (RSM) is suggested to predict and to optimize the surface roughness in hard turning. Thereafter, the predictive modelling and optimization results are implemented for economic analysis. According to the findings of the experiments, with a contribution of 58.18%, the feed rate possesses a high impact on the surface finish, followed by the nose radius (12.32%) and speed (0.85%). Consequently, the machined surface quality improved with the increase of the nose radius because of the minimum tool wear and due to the increase of the effective length of the cutting edge. At optimum cutting conditions, the tool life of SPPP-AlTiSiN coated carbide insert is noted as 46 minutes under nanofluid-MQL and consequently, it estimated the overall machining cost per component as Rs.23.12 in Indian currency.

Advances in developed and developing countries are more attributable to growth in industrial activities that directly impact increasing energy demand. Energy availability has been inconsistent globally, necessitating energy storage (ES) for use as per requirement. Various energy storage technologies (ESTs) are available in mechanical, electrochemical, electrical, chemical, and thermal forms to fulfil the energy demand of a user as and when required. The factors responsible for making a commercially viable energy storage product are further being researched for an eco-friendly and optimal solution to store energy for a longer duration. Researchers are employing different strategies to evaluate the energy efficiency of storage technologies. This paper uses the VIKOR technique to analyze ESTs while assigning objective weights with the entropy weights method based on identified energy performance indicators and ranking them according to their commercialization viability. The method helps a consumer choose better ESTs as per their requirement while manufacturers compete with each other to enhance the commercial value of their energy storage products. Sensitivity analysis has been performed to understand the uncertainties, pros, and cons with the limitations and scope of using the decision model and thus taking an informed decision. The analysis of different energy storage technologies has indicated Hydrogen Fuel Cells (HFC) to be impressive and promising for the future.

Lubrication and cooling (LC) are critical for mechanical devices’ effective and dependable functioning, because they decrease friction and wear of moving components, ensuring superior efficiency. However, the cutting fluids in machining operations are a key cause of fear, due to their high cost, environmental impact, and health risks, particularly in computer numerical control (CNC) machine tools (MTs). During the industrial revolutions, MTs superseded manual labour and increased efficiency and output. Therefore, much research was conducted on lubrication and cooling in CNC machine tools (LC in CNC MTs). Therefore, it has become necessary to review and highlight research hotspots and gaps using specific means that can benefit budding researchers. The present review aims to identify research hotspots and gaps of LC in CNC MTs utilizing content and visualization analysis, employing VOSviewer and Biblioshiny software. The analysis comprises 136 documents retrieved by Scopus between 1988 and 2022. The analysis revealed a consistent growth in publications, primarily consisting of articles, with a minor proportion of review documents and conference papers. The keywords were categorized into seven clusters, with a notable prevalence of ‘surface roughness’ and ‘CNC machine tools’. A word cloud was generated to visualize the author’s frequently used keywords, where larger font sizes represented higher frequency. The treemaps demonstrated that ‘CNC’ appeared 34 times and contributed 8%, followed by ‘machine’, ‘tool’, ‘machining’, and ‘thermal’. In the abstract-terms tree plot, ‘machine’ appeared 235 times and contributed 7%, followed by ‘CNC’, ‘machining’, ‘tool’, and ‘cutting’. The content and visualization analysis identified six research hotspots: computer control systems, machine tools, computer numerical control, machining, numerical control systems, and surface roughness (Ra). The research gaps are temperature, cooling systems, cutting forces, energy utilization, tool life, nanoparticles, electric power utilization, and energy conservation. Based on hotspots and gaps, literature evaluations extensively addressed the strong roadmap of technical improvements and problems of LC in CNC MTs. A complete visualization and content analysis also produced a conceptual framework for best practices, and the study offers insight into the issues and prospects.

In machining activities, sound emission is one of the key factors toward the operator's health and safety. Sound generation during cutting is the outcome of the interaction between tool and work. The intensity of sound greatly influences the cutting power consumption and surface finish obtained during machining. Therefore, the current work emphasized the analysis of sound emission, power consumption, and surface roughness in hard turning of AISI 4340 steel using a CBN tool which was rarely found in the literature. Response surface methodology (RSM) and artificial neural network (ANN) techniques were utilized to formulate the model for each response. The results indicated that the maximum value of input parameters exhibited the highest level of sound due to the creation of vibration in the machine and tool. Higher sound level indicates the generation of lower power consumption but at the same instant surface roughness was leading with increment in sound level. The feed rate exhibited the utmost noteworthy consequence on surface quality with 87.71% contribution. The cutting power can be decreased by choosing the high level of cutting parameters. The RSM and ANN have a good correlation with experimental data, but the accuracy of the ANN is better than the RSM.

AISI 4140 alloy steel has high abrasion resistance, toughness, torsional, and fatigue strength. Different types of this alloy with different hardness are used to manufacture gears, crankshafts, collars, jigs, and milling spindles. In this study, turning tests were carried out on AISI 4140 steel using coated carbide inserts considering Taguchi L₉ orthogonal array at three different cutting speeds, feed rates, and cutting depths. The output parameters were selected as cutting forces, surface roughness, current and sound intensity. According to the ANOVA results, feed rate was the most effective parameter on the cutting force and surface roughness. As the feed rate increases, the cutting force and surface roughness value enhance. The feed rate was also the most important factor affecting the current with 81.61% contribution, followed by cutting depth with 12.67% contribution. The cutting depth with 66.95% contribution has the highest impact on the sound intensity. It was followed by a feed rate with 26.07% contribution. According to the optimisation results, the experimental and the estimated values ⁣⁣were significantly close to each other.