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

Sustainability

Raman Kumar¹  Catalin I. Pruncu²  Sunpreet Singh³  Yadaiah Nirsanametla⁴  Shah Murtoza Morshed⁵  Juairiya Binte Tariq⁵  Sabbir Hossain Shawon⁵  Abir Hasan⁵  Md. Rezaul Karim⁵  Chander Prakash⁶ 

[1] Department of Mechanical Engineering, Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India;Department of Mechanical Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK;Department of Mechanical Engineering, National University of Singapore, Singapore 119077, Singapore;Department of Mechanical Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli 791109, India;Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh;School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, Punjab, India;

关键词: Al-Mg-Zr alloy;    minimum quantity lubricant;    PCD;    optimization;    life cycle assessment (LCA);    sustainability;   

DOI  :  10.3390/su13137321

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown