Abstract
Green machining strategies in the manufacturing sector help to maintain the product value by considering the environmental impacts. Also, improvisation in the quality contribution of the parts can minimize the environmental consequences by improving resource efficiency, specifically in terms of coolants used in machining. Certain hazardous impacts have been witnessed because of longer exposure to such a machining environment. To address it, many researchers have concentrated on providing a healthy machining environment either by introducing dry machining or by minimum quantity lubrication (MQL). The proposed study addresses this context. The influence of these tactics on the attained surface quality of Al‐6063 is quantified in this paper in terms of surface integrity (Ra) and removal rate of material (MRR). The study involves single‐response optimization using the Taguchi design and multiresponse optimization using grey relational analysis (GRA). The results reveal that the depth of cut (Dc) and spindle speed (Ss) have the greatest impact on Ra and MRR. The machinability of Al‐6063 is examined by considering the key machinability parameters, such as the spindle speed (Ss), feed rate (Fr), and the depth of cut (Dc), to arrive at the best possible surface roughness and removal rate of the material. As a typical Taguchi approach cannot perform multiresponse optimization, grey relational analysis is used. The grey relational analysis combined with Taguchi gives a novel methodology for multioptimization. The entire study is performed in dry condition and under minimum quantity lubrication. The results suggest that the responses are highly influenced by the depth of cut and spindle speed.
Highlights
Advancements in nonconventional machining technologies are widely employed to address a variety of challenges in machining processes, such as machining high-strength materials, improving surface integrity, achieving high levels of precision, reducing surplus material, and shortening production time
Such results are validated with the experimental values. e researcher used design of experiment (DoE) to evaluate the roughness value (Ra) and materials removal rate (MRR) of the aluminum metal matrix composites. e quality achieved postmachining is compared with the projected values. e results attained are found to be Advances in Materials Science and Engineering increased [1,2,3,4,5,6,7] based on Taguchi’s prediction involving improvisations in the efficiency of resource management, energy consumption, parameter settings, and energy efficient systems
A study of machining factors is performed for estimating MRR by considering the spindle speed, coolant flow, and the depth of cut in a combination set framed by the central composite rotatable design [8]
Summary
Many industries and researches are employing different techniques to understand the behavior of alloy materials during machining by monitoring Ra, MRR, etc. On the machining performance of both brittle and ductile materials Such studies usually incorporate the application of numerical and statistical models combined with the design of experiment (DoE). The surface roughness of the CNC end milling parameters is predicted using a mathematical model followed by the application of regression analysis for predicting the results. Such results are validated with the experimental values. E study involved a minimum energy formulation by looking at the tactics and constraints for lowering the machine tool energy consumption, while maintaining the tool’s life and parts’ quality. An attempt has been made on the application of green machining strategy on Al-6063, which is one of the most successful architectural materials in today’s scenario
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