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Efficient removal of heat from the chip formation zone in the machining of aerospace materials is quite crucial for attaining viability with respect to cost and productivity. The recently embraced cooling and lubrication method in the context of environmental friendly and sustainable manufacturing includes the application of minimum quantity cutting fluid. This article presents an experimental investigation, complemented with an evolutionary optimization technique, for studying the impact on cutting forces, surface roughness, tool wear, and chip control in the turning of the two aero-engine alloys (Inconel-800 and titanium-II) with or without using minimal-quantity cooling lubrication fluid. In addition, the multiple regression technique is applied to find the relationship between responses and input parameter such as cutting speed, feed rate, and approach angle. Afterward, the sensitivity analysis and analysis of variance (ANOVA) tests have been performed to test the statistical significance of proposed predictive models. At the end of work, the experimental data have been optimized through two evolutionary techniques, i.e. particle swarm optimization and bacterial foraging optimization, also compared to the much-used desirability technique. It has been concluded that the cooling option of applying minimum quantity cutting fluid proved beneficial for machining these aerospace materials. Moreover, the evolutionary techniques gave much more accurate results when compared to the desirability technique with particle swarm optimization and was concluded as the best one out of the three techniques on the basis of minimum average time taken and minimum percentage error.

With regard to the manufacturing of innovative hard-machining super alloys (i.e., Inconel-800), a potential alternative for improving the process is using a novel cutting fluid approach. Generally, the cutting fluids allow the maintenance of a better tool topography that can generate a superior surface quality of machined material. However, the chemical components of fluids involved in that process may produce harmful effects on human health and can trigger environmental concerns. By decreasing the cutting fluids amount while using sustainable methods (i.e., dry), Near Dry Machining (NDM) will be possible in order to resolve these problems. This paper discusses the features of two innovative techniques for machining an Inconel-800 superalloy by plain turning while considering some critical parameters such as the cutting force, surface characteristics (Ra), the tool wear rate, and chip morphology. The research findings highlight the near-dry machining process robustness over the dry machining routine while its great potential to resolve the heat transfer concerns in this manufacturing method was demonstrated. The results confirm other benefits of these methods (i.e., NDM) linked to the sustainability aspects in terms of the clean process, friendly environment, and permits as well as in terms of improving the manufacturing characteristics.

Abstract:The prediction and optimization of surface roughness values remain a critical concern in nano-fluids based minimum quantity lubrication (NFMQL) turning of titanium (grade-2) alloys. Here, we discuss an application of response surface methodology with Box-Cox transformation to determine the optimal cutting parameters for three surface roughness values, i.e., R a , R q , and R z , in turning of titanium alloy under the NFMQL condition. The surface roughness prediction model has been established based on the selected input parameters such as cutting speed, feed rate, approach angle, and different nano-fluids used. Then the multiple regression technique is used to find the relationship between the given responses and input parameter. Further, the experimental data were optimized through the desirability function approach. The findings from the current investigation showed that feed rate is the most effective parameter followed by cutting speed, different nano-fluids, and approach angle on R a and R q values, whereas cutting speed is more effective in the case of R z under NFMQL conditions. Moreover, the predicted results are comparatively near to the experimental values and hence, the established models of RSM using Box-Cox transformation can be used for prediction satisfactorily.