Environmental, Economical and Technological Analysis of MQL-Assisted Machining of Al-Mg-Zr Alloy Using PCD Tool

Abstract

Clean technological machining operations can improve traditional methods’ environmental, economic, and technical viability, resulting in sustainability, compatibility, and human-centered machining. This, this work focuses on sustainable machining of Al-Mg-Zr alloy with minimum quantity lubricant (MQL)-assisted machining using a polycrystalline diamond (PCD) tool. The effect of various process parameters on the surface roughness and cutting temperature were analyzed. The Taguchi L25 orthogonal array-based experimental design has been utilized. Experiments have been carried out in the MQL environment, and pressure was maintained at 8 bar. The multiple responses were optimized using desirability function analysis (DFA). Analysis of variance (ANOVA) shows that cutting speed and depth of cut are the most prominent factors for surface roughness and cutting temperature. Therefore, the DFA suggested that, to attain reasonable response values, a lower to moderate value of depth of cut, cutting speed and feed rate are appreciable. An artificial neural network (ANN) model with four different learning algorithms was used to predict the surface roughness and temperature. Apart from this, to address the sustainability aspect, life cycle assessment (LCA) of MQL-assisted and dry machining has been carried out. Energy consumption, CO2 emissions, and processing time have been determined for MQL-assisted and dry machining. The results showed that MQL-machining required a very nominal amount of cutting fluid, which produced a smaller carbon footprint. Moreover, very little energy consumption is required in MQL-machining to achieve high material removal and very low tool change.

Turning SKD 11 Hardened Steel: An Experimental Study of Surface Roughness and Material Removal Rate Using Taguchi Method

“…In contrast, for material removal rate, feed rate is moderately signifcant to induce a favorable rate of material removal. Tis result is consistent with the earlier research conducted by various researchers [68,69]. Te R 2 value of 93.01% obtained for the MMR model provides evidence of its statistical signifcance.…”

“…For surface roughness, it is clearly showing that the optimal level for turning is at the frst level of cutting speed, feed rate, and depth of cut. Specifcally, this means that the cutting speed at 119 m/min, feed rate at 0.11 mm/rev, and the depth of cut at 0.2 mm are the optimum values of cutting parameters for lowest surface roughens, which is in slight contrast with the results found by Karim et al [69]. However, Figure 6 plot for the S/N ratio indicates that there is less variance for changes in cutting speed, whereas there is greater variation for changes in feed.…”

A review on minimum quantity lubrication (MQL) assisted machining processes using mono and hybrid nanofluids

“…Results revealed that MQL reduces carbon emissions and energy expenditure significantly by 9%–24% and 9%–27%, respectively, compared to dry machining. Similarly, Karim et al (2021) performed a sustainability study using a bottom-up approach during MQL machining of Al-Mg-Zr alloy. The findings demonstrated that MQL machining required relatively less cutting fluid, resulting in a decreased carbon footprint.…”

Sustainability Evaluation of Machining Ti6Al4V with Graphene Inclusion

“…Khanna et al [22] evaluated the sustainability of conventional flood machining, cryogenic machining, and minimum quantity lubrication while machining Ti6Al4V and found cryogenic machining to be the most sustainable. Karim et al [23] evaluated the sustainability of MQL machining Al-based alloy using the PCD tool by evaluating environmental, economic and machining performance. MQL application consumed less energy and achieved higher metal removal with lesser tool changes.…”