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

Md. Rezaul Karim, Juairiya Binte Tariq, Shah Murtoza Morshed, Sabbir Hossain Shawon, Abir Hasan, Chander Prakash, Sunpreet Singh, Raman Kumar, Yadaiah Nirsanametla and Catalin I. Pruncu
Additional contact information
Md. Rezaul Karim: Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh
Juairiya Binte Tariq: Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh
Shah Murtoza Morshed: Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh
Sabbir Hossain Shawon: Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh
Abir Hasan: Department of Mechanical and Production Engineering, Ahsanullah University of Science and Technology, Dhaka 1208, Bangladesh
Chander Prakash: School of Mechanical Engineering, Lovely Professional University, Phagwara 144411, Punjab, India
Sunpreet Singh: Department of Mechanical Engineering, National University of Singapore, Singapore 119077, Singapore
Raman Kumar: Department of Mechanical Engineering, Guru Nanak Dev Engineering College, Ludhiana 141006, Punjab, India
Yadaiah Nirsanametla: Department of Mechanical Engineering, North Eastern Regional Institute of Science and Technology, Nirjuli 791109, India
Catalin I. Pruncu: Department of Mechanical Engineering, Imperial College London, Exhibition Road, London SW7 2AZ, UK

Sustainability, 2021, vol. 13, issue 13, 1-22

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 L 25 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, CO 2 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.

Keywords: Al-Mg-Zr alloy; minimum quantity lubricant; PCD; optimization; life cycle assessment (LCA); sustainability; energy consumption; CO 2 emission and carbon foot prints (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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