Modeling and Optimization of Power Consumption for Economic Analysis, Energy-Saving Carbon Footprint Analysis, and Sustainability Assessment in Finish Hard Turning Under Graphene Nanoparticle–Assisted Minimum Quantity Lubrication

The present work addresses the issue on power consumption in finish hard turning of die steel under nanofluid-assisted minimum quantity lubrication condition. This study also aims to assess the propitious role of minimum quantity lubrication using graphene nanoparticle-enriched radiator green coolant-based nano-cutting fluid for machinability improvement of hardened steel. The hard turning trials are performed based on design of experiments by considering the geometrical parameters (insert’s nose radius) and machining parameters (cutting speed, axial feed, depth of cut). Combined approach of central composite design—analysis of variance, desirability function analysis, and response surface methodology—have been subsequently employed for analysis, predictive modeling, and optimization of machining power consumption. With a motivational philosophy of “Go Green-Think Green-Act Green”, the work also deals with energy-saving carbon footprint analysis, economic analysis, and sustainability assessment under environmental-friendly nanofluid-assisted minimum quantity lubrication condition. Results showed that machining with nanofluid-minimum quantity lubrication provided an effective cooling-lubrication strategy, safer and cleaner production, environmental friendliness, and assisted to improve sustainability.

[1]  Md. Zurais Ibne Ashraf,et al.  Assessing near-dry lubrication (35 ml/h) performance in hard turning process of hardened (48 HRC) AISI 1060 carbon steel , 2018, The International Journal of Advanced Manufacturing Technology.

[2]  Hussien Hegab,et al.  Towards Optimization of Machining Performance and Sustainability Aspects when Turning AISI 1045 Steel under Different Cooling and Lubrication Strategies , 2019, Materials.

[3]  Nuno Ricardo Costa,et al.  Desirability function approach: A review and performance evaluation in adverse conditions , 2011 .

[4]  M. Elbah,et al.  Machinability investigation in hard turning of AISI D3 cold work steel with ceramic tool using response surface methodology , 2014 .

[5]  H. Aouici,et al.  Design optimization of cutting parameters when turning hardened AISI H11 steel (50 HRC) with CBN7020 tools , 2017 .

[6]  Ashok Kumar Sahoo,et al.  Experimental investigations on machinability aspects in finish hard turning of AISI 4340 steel using uncoated and multilayer coated carbide inserts , 2012 .

[7]  Rabin Kumar Das,et al.  Measurement and machinability study under environmentally conscious spray impingement cooling assisted machining , 2019, Measurement.

[8]  Libo Wang,et al.  Self-repairing and tribological behaviour of steel–steel friction pairs lubricated with an oil with magnesium silicate hydrosilicate as additive , 2019, Manufacturing Review.

[9]  Salim Belhadi,et al.  Investigation of the performance of the MQL, dry, and wet turning by response surface methodology (RSM) and artificial neural network (ANN) , 2017 .

[10]  A. Sahoo,et al.  Comparative investigation towards machinability improvement in hard turning using coated and uncoated carbide inserts: part I experimental investigation , 2018 .

[11]  Jan-Eric Ståhl,et al.  Effect of cutting edge radius on surface roughness and tool wear in hard turning of AISI 52100 steel , 2017, The International Journal of Advanced Manufacturing Technology.

[12]  Tarek Mabrouki,et al.  Modeling and multi-objective optimization for minimizing surface roughness, cutting force, and power, and maximizing productivity for tempered stainless steel AISI 420 in turning operations , 2018, The International Journal of Advanced Manufacturing Technology.

[13]  R. Suresh,et al.  Some studies on hard turning of AISI 4340 steel using multilayer coated carbide tool , 2012 .

[14]  Sudhansu Ranjan Das,et al.  Surface Roughness Analysis for Economical Feasibility Study of Coated Ceramic Tool in Hard Turning Operation , 2017 .

[15]  Sounak Kumar Choudhury,et al.  Hard turning using HiPIMS-coated carbide tools: Wear behavior under dry and minimum quantity lubrication (MQL) , 2014 .

[16]  Tarek Mabrouki,et al.  Design optimization for minimum technological parameters when dry turning of AISI D3 steel using Taguchi method , 2017 .

[17]  Hakki Ozgur Unver,et al.  Energy consumption characteristics of turn-mill machining , 2017 .

[18]  Denni Kurniawan,et al.  Effect of cutting speed and feed in turning hardened stainless steel using coated carbide cutting tool under minimum quantity lubrication using castor oil , 2015 .

[19]  J. Paulo Davim,et al.  State-of-the-art research in machinability of hardened steels , 2013 .

[20]  Danil Yu. Pimenov,et al.  An approach to cleaner production for machining hardened steel using different cooling-lubrication conditions , 2018, Journal of Cleaner Production.

[21]  M. Yallese,et al.  Performance of coated and uncoated mixed ceramic tools in hard turning process , 2016 .

[22]  S. Patel,et al.  A Comparison of Machinability in Hard Turning of EN-24 Alloy Steel Under Mist Cooled and Dry Cutting Environments with a Coated Cermet Tool , 2018, Journal of Failure Analysis and Prevention.

[23]  Ajay Kumar Behera,et al.  An overview on economic machining of hardened steels by hard turning and its process variables , 2019, Manufacturing Review.

[24]  H. Aouici,et al.  Comparison between mixed ceramic and reinforced ceramic tools in terms of cutting force components modelling and optimization when machining hardened steel AISI 4140 (60 HRC) , 2015 .

[25]  Ming Li,et al.  Effect of cutting parameters on surface roughness using orthogonal array in hard turning of AISI 1045 steel with YT5 tool , 2017 .

[26]  Hari Singh,et al.  Optimizing power consumption for CNC turned parts using response surface methodology and Taguchi's technique—A comparative analysis , 2008 .

[27]  M. Yallese,et al.  Effects of coating material and cutting parameters on the surface roughness and cutting forces in dry turning of AISI 52100 steel , 2017 .

[28]  Xiaobin Cui,et al.  Identification of the optimum cutting parameters in intermittent hard turning with specific cutting energy, damage equivalent stress, and surface roughness considered , 2018 .

[29]  Mozammel Mia,et al.  Sustainability assessment associated with surface roughness and power consumption characteristics in nanofluid MQL-assisted turning of AISI 1045 steel , 2019, The International Journal of Advanced Manufacturing Technology.

[31]  C. Pruncu,et al.  Influence of Different Grades of CBN Inserts on Cutting Force and Surface Roughness of AISI H13 Die Tool Steel during Hard Turning Operation , 2019, Materials.

[32]  S. E. Kilic,et al.  Slot milling of titanium alloy with hexagonal boron nitride and minimum quantity lubrication and multi-objective process optimization for energy efficiency , 2020 .

[33]  Puneet Sharma,et al.  Investigation of effects of nanofluids on turning of AISI D2 steel using minimum quantity lubrication , 2015 .

[34]  Salim Belhadi,et al.  Investigation, modeling, and optimization of cutting parameters in turning of gray cast iron using coated and uncoated silicon nitride ceramic tools. Based on ANN, RSM, and GA optimization , 2018, The International Journal of Advanced Manufacturing Technology.

[35]  Mourad Nouioua,et al.  Comparative assessment of machining environments (dry, wet and MQL) in hard turning of AISI 4140 steel with CC6050 tools , 2019, The International Journal of Advanced Manufacturing Technology.

[36]  G. Serin,et al.  Integrated energy-efficient machining of rotary impellers and multi-objective optimization , 2020, Materials and Manufacturing Processes.

[37]  J. Paulo Davim,et al.  Machinability investigations in hard turning of AISI D2 cold work tool steel with conventional and wiper ceramic inserts , 2009 .