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The main aim of this research work is to examine the impact of input process factors (cutting speed, feed rate, and depth of cut) on machining characteristics (arithmetic mean surface roughness, tool flank face wear and tool chip interface temperature) and further to optimize during turning of Incoloy alloy 800. The cutting tool used was PVD multilayer coated (TiAlN-TIN) carbide insert. The experiment was performed based on Taguchi L27 methodology with a focus on the sustainability of turning. Sustainable manufacturing is considered for accomplishing overall efficiency with regard to economic, environmental and social aspects. The ANOVA (Analysis of Variance) was employed to assess the contributions of input process variables on cutting variable outputs. Further, the main effect diagrams exemplified the influences of main parameters on response variables. ANOVA analysis results demonstrated that the feed rate was the most leading factor that affects average surface roughness. The relationship(s) among input process factors and the evaluated measured outputs are determined using a quadratic regression model. Taguchi grey relational analysis has been implemented to trial results so as to optimize responses. The results revealed that the grey relational grade is significantly improved (0.308) through the setting of optimal parametric combinations. This integrated Grey–Taguchi approach was established quite effectual for simultaneously optimization of multifaceted machining output responses of turning process. This study provides a novel strategy to develop the machining efficiency of Incoloy 800 steel toward the improvement of sustainable processes and provide suitable applications in aerospace industry.

Over the years, the need for appropriate selection and implementation of optimal cutting conditions has been a major issue among the metal cutting researchers. The implementations of multicriteria decision tools in modern machining were highly valued due to a better capacity to select the optimum parametric combinations. In the optimization method, an objective function is developed with or without mathematical model whereas a mathematical equation based model is formulated, based on input and response parameter in modeling technique to obtain the best efficient cutting environment. In this review paper, various modeling techniques like artificial neural networks, fuzzy modeling, and regression modeling analysis along with Taguchi technique, genetic algorithm and response methodology for application in turning have been focused. Moreover, some new investigations and observations are focused on the advancement of new strategies like (PSO) Particle Swarm Optimization, as it is simple and easy to utilize. In addition, multi-objective optimization is an essentially needed when a single optimization technique can’t produce a satisfactory result. This overview states the significance of optimization and modeling technique in the field of high-temperature material and discloses the critical issues related to it.

Analyzing and comprehending cutting process mechanisms is a crucial step in creating a cost-effective, long-lasting, and safe machining process.In modern manufacturing, it is very challenging to achieve a high surface finish product with high dimensional accuracy. To reduce the high production cost as well as for the sustainable manufacturing selection of both cutting tool materials and cutting environments is necessary. Nowadays high-temperature alloys are in very demand in industries like power generation, gas turbines, and chemical processing. Superalloys are recognised as a significant problem in a high-temperature work environment with the sophisticated material technology in space and aviation industries. At the time of machining of these superalloys, a high amount of heat developed at the cutting area which affects the microstructure of the work specimen and also the cutting inserts. To minimize that excess heat, researchers and manufacturing industries are adopting different cooling lubrication techniques. When the lubricant penetrated the cutting region, the temperature is reduced since cooling effect. Several studies focus on the optimization of individual performance features in the machining processes and in recent past, Super-alloys and cutting materials like ceramic, carbide, and hybrid tools are gaining popularity. Since the application of the superalloy in various sectors is ever- increasing, therefore it is necessary to evaluate a machining behavior during the experimental investigation. In this contemporary review, the effects of various cooling and lubrication strategy on machining outputs will be discussed considering the sustainable and greenway of manufacturing.