{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T14:43:13Z","timestamp":1785422593064,"version":"3.56.0"},"reference-count":64,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T00:00:00Z","timestamp":1617753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"University of Slavonski Brod","award":["SV001"],"award-info":[{"award-number":["SV001"]}]},{"name":"Ministry of Education, Science and Technological Development of Republic of Serbia","award":["451-03-68\/2020-14\/200156"],"award-info":[{"award-number":["451-03-68\/2020-14\/200156"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2021,10]]},"DOI":"10.1007\/s00521-021-05877-z","type":"journal-article","created":{"date-parts":[[2021,4,7]],"date-time":"2021-04-07T19:04:42Z","timestamp":1617822282000},"page":"12445-12475","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Multi-objective optimization of steel AISI 1040 dry turning using genetic algorithm"],"prefix":"10.1007","volume":"33","author":[{"given":"Djordje","family":"Vukelic","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katica","family":"Simunovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zeljko","family":"Kanovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomislav","family":"Saric","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Branko","family":"Tadic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7159-2627","authenticated-orcid":false,"given":"Goran","family":"Simunovic","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,4,7]]},"reference":[{"key":"5877_CR1","doi-asserted-by":"publisher","first-page":"100","DOI":"10.1016\/j.measurement.2015.03.037","volume":"70","author":"A Gok","year":"2015","unstructured":"Gok A (2015) A new approach to minimization of the surface roughness and cutting force via fuzzy TOPSIS, multi-objective grey design and RSA. Measurement 70:100\u2013109. https:\/\/doi.org\/10.1016\/j.measurement.2015.03.037","journal-title":"Measurement"},{"key":"5877_CR2","doi-asserted-by":"publisher","first-page":"389","DOI":"10.1177\/0954405414558731","volume":"230","author":"J Dureja","year":"2016","unstructured":"Dureja J, Gupta V, Sharma VS, Dogra M, Bhatti MS (2016) A review of empirical modeling techniques to optimize machining parameters for hard turning applications. Proc Inst Mech Eng B J Eng Manuf 230:389\u2013404. https:\/\/doi.org\/10.1177\/0954405414558731","journal-title":"Proc Inst Mech Eng B J Eng Manuf"},{"key":"5877_CR3","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1108\/WJE-06-2019-0170","volume":"16","author":"R Singh","year":"2019","unstructured":"Singh R, Dureja JS, Dogra M, Randhawa JS (2019) Optimization of machining parameters under MQL turning of Ti\u20136Al\u20134V alloy with textured tool using multi-attribute decision-making methods. World J Eng 16:648\u2013659. https:\/\/doi.org\/10.1108\/WJE-06-2019-0170","journal-title":"World J Eng"},{"key":"5877_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-05149-2","author":"TT Nguyen","year":"2020","unstructured":"Nguyen TT (2020) An energy-efficient optimization of the hard turning using rotary tool. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-05149-2","journal-title":"Neural Comput Appl"},{"key":"5877_CR5","doi-asserted-by":"publisher","first-page":"1861","DOI":"10.1016\/j.jmatprotec.2004.04.277","volume":"155\u2013156","author":"K Tuffy","year":"2004","unstructured":"Tuffy K, Byrne G, Dowling D (2004) Determination of the optimum TiN coating thickness on WC inserts for machining carbon steels. J Mater Process Technol 155\u2013156:1861\u20131866. https:\/\/doi.org\/10.1016\/j.jmatprotec.2004.04.277","journal-title":"J Mater Process Technol"},{"key":"5877_CR6","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1016\/j.matdes.2005.04.003","volume":"27","author":"M Gunay","year":"2006","unstructured":"Gunay M, Seker U, Sur G (2006) Design and construction of a dynamometer to evaluate the influence of cutting tool rake angle on cutting forces. Mater Des 27:1097\u20131101. https:\/\/doi.org\/10.1016\/j.matdes.2005.04.003","journal-title":"Mater Des"},{"issue":"10","key":"5877_CR7","doi-asserted-by":"publisher","first-page":"1139","DOI":"10.1016\/j.matdes.2005.03.010","volume":"27","author":"S Yaldiz","year":"2006","unstructured":"Yaldiz S, Unsacar F, Saglam H (2006) Comparison of experimental results obtained by designed dynamometer to fuzzy model for predicting cutting forces in turning. Mater Des 27(10):1139\u20131147. https:\/\/doi.org\/10.1016\/j.matdes.2005.03.010","journal-title":"Mater Des"},{"key":"5877_CR8","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.ijmachtools.2005.05.002","volume":"46","author":"H Saglam","year":"2006","unstructured":"Saglam H, Unsacar F, Yaldiz S (2006) Investigation of the effect of rake angle and approaching angle on main cutting force and tool tip temperature. Int J Mach Tools Manuf 46:132\u2013141. https:\/\/doi.org\/10.1016\/j.ijmachtools.2005.05.002","journal-title":"Int J Mach Tools Manuf"},{"key":"5877_CR9","doi-asserted-by":"publisher","first-page":"2140","DOI":"10.1016\/j.ijmachtools.2007.04.013","volume":"47","author":"DR Salgado","year":"2007","unstructured":"Salgado DR, Alonso FJ (2007) An approach based on current and sound signals for in-process tool wear monitoring. Int J Mach Tools Manuf 47:2140\u20132152. https:\/\/doi.org\/10.1016\/j.ijmachtools.2007.04.013","journal-title":"Int J Mach Tools Manuf"},{"key":"5877_CR10","doi-asserted-by":"publisher","first-page":"5826","DOI":"10.1016\/j.eswa.2010.11.041","volume":"38","author":"I Asilturk","year":"2011","unstructured":"Asilturk I, Cunkas M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl 38:5826\u20135832. https:\/\/doi.org\/10.1016\/j.eswa.2010.11.041","journal-title":"Expert Syst Appl"},{"issue":"3","key":"5877_CR11","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1016\/j.measurement.2010.11.018","volume":"44","author":"S Neseli","year":"2011","unstructured":"Neseli S, Yaldiz S, Turkes E (2011) Optimization of tool geometry parameters for turning operations based on the response surface methodology. Measurement 44(3):580\u2013587. https:\/\/doi.org\/10.1016\/j.measurement.2010.11.018","journal-title":"Measurement"},{"key":"5877_CR12","doi-asserted-by":"publisher","first-page":"853","DOI":"10.1007\/s10845-009-0360-0","volume":"22","author":"ES Topal","year":"2011","unstructured":"Topal ES, Cogun C (2011) Computer-based estimation and compensation of diametral errors in CNC turning of cantilever bars. J Intell Manuf 22:853\u2013865. https:\/\/doi.org\/10.1007\/s10845-009-0360-0","journal-title":"J Intell Manuf"},{"key":"5877_CR13","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1007\/s00170-011-3372-9","volume":"58","author":"G Cohen","year":"2012","unstructured":"Cohen G, Gilles P, Segonds S, Mousseigne M, Lagarrigue P (2012) Thermal and mechanical modeling during dry turning operations. Int J Adv Manuf Technol 58:133\u2013140. https:\/\/doi.org\/10.1007\/s00170-011-3372-9","journal-title":"Int J Adv Manuf Technol"},{"key":"5877_CR14","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1007\/s00170-012-3903-z","volume":"63","author":"I Asilturk","year":"2012","unstructured":"Asilturk I (2012) Predicting surface roughness of hardened AISI 1040 based on cutting parameters using neural networks and multiple regression. Int J Adv Manuf Technol 63:249\u2013257. https:\/\/doi.org\/10.1007\/s00170-012-3903-z","journal-title":"Int J Adv Manuf Technol"},{"key":"5877_CR15","doi-asserted-by":"publisher","first-page":"4075","DOI":"10.1016\/j.measurement.2013.07.021","volume":"46","author":"K Venkata Rao","year":"2013","unstructured":"Venkata Rao K, Murthy BSN, Mohan Rao N (2013) Cutting tool condition monitoring by analyzing surface roughness, work piece vibration and volume of metal removed for AISI 1040 steel in boring. Measurement 46:4075\u20134084. https:\/\/doi.org\/10.1016\/j.measurement.2013.07.021","journal-title":"Measurement"},{"key":"5877_CR16","doi-asserted-by":"publisher","first-page":"703","DOI":"10.1177\/0954405414531247","volume":"229","author":"K Venkata Rao","year":"2015","unstructured":"Venkata Rao K, Murthy B, Mohan Rao N (2015) Experimental study on surface roughness and vibration of workpiece in boring of AISI 1040 steels. Proc Inst Mech Eng B J Eng Manuf 229:703\u2013712. https:\/\/doi.org\/10.1177\/0954405414531247","journal-title":"Proc Inst Mech Eng B J Eng Manuf"},{"key":"5877_CR17","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1177\/0954405414554018","volume":"230","author":"BS Prasad","year":"2016","unstructured":"Prasad BS, Babu MP, Reddy YR (2016) Evaluation of correlation between vibration signal features and three-dimensional finite element simulations to predict cutting tool wear in turning operation. Proc Inst Mech Eng B J Eng Manuf 230:203\u2013214. https:\/\/doi.org\/10.1177\/0954405414554018","journal-title":"Proc Inst Mech Eng B J Eng Manuf"},{"key":"5877_CR18","doi-asserted-by":"publisher","first-page":"919","DOI":"10.1007\/s00170-015-7621-1","volume":"83","author":"K Venkata Rao","year":"2016","unstructured":"Venkata Rao K, Vidhu KP, Kumar TA, Rao NN, Murthy PBGSN, Balaji M (2016) An artificial neural network approach to investigate surface roughness and vibration of workpiece in boring of AISI1040 steels. Int J Adv Manuf Technol 83:919\u2013927. https:\/\/doi.org\/10.1007\/s00170-015-7621-1","journal-title":"Int J Adv Manuf Technol"},{"key":"5877_CR19","doi-asserted-by":"publisher","first-page":"131","DOI":"10.1016\/j.measurement.2016.12.060","volume":"100","author":"RN Yadav","year":"2017","unstructured":"Yadav RN (2017) A hybrid approach of Taguchi-response surface methodology for modeling and optimization of duplex turning process. Measurement 100:131\u2013138. https:\/\/doi.org\/10.1016\/j.measurement.2016.12.060","journal-title":"Measurement"},{"key":"5877_CR20","doi-asserted-by":"publisher","first-page":"1","DOI":"10.18052\/www.scipress.com\/ijet.10.1","volume":"10","author":"T Haque","year":"2017","unstructured":"Haque T, Kumar S, Upadhaya D, Barman M, Mukhopadhyay A (2017) Optimization of multiple roughness characteristics for turning of AISI 1040 steel under different cutting conditions. Int J Eng Technol 10:1\u201310. https:\/\/doi.org\/10.18052\/www.scipress.com\/ijet.10.1","journal-title":"Int J Eng Technol"},{"key":"5877_CR21","doi-asserted-by":"publisher","first-page":"781","DOI":"10.17222\/mit.2018.110","volume":"52","author":"H Akkus","year":"2018","unstructured":"Akkus H (2018) Optimising the effect of cutting parameters on the average surface roughness in a turning process with the Taguchi method. Mater Tehnol 52:781\u2013785. https:\/\/doi.org\/10.17222\/mit.2018.110","journal-title":"Mater Tehnol"},{"key":"5877_CR22","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1007\/s12206-018-0512-2","volume":"32","author":"D Jhodkar","year":"2018","unstructured":"Jhodkar D, Amarnath M, Chelladurai H, Ramkumar J (2018) Performance assessment of microwave treated WC insert while turning AISI 1040 steel. J Mech Sci Technol 32:2551\u20132558. https:\/\/doi.org\/10.1007\/s12206-018-0512-2","journal-title":"J Mech Sci Technol"},{"key":"5877_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s40430-018-1096-6","author":"D Jhodkar","year":"2018","unstructured":"Jhodkar D, Amarnath M, Chelladurai H, Ramkumar J (2018) Experimental investigations to enhance the machining performance of tungsten carbide tool insert using microwave treatment process. J Braz Soc Mech Sci Eng. https:\/\/doi.org\/10.1007\/s40430-018-1096-6","journal-title":"J Braz Soc Mech Sci Eng"},{"key":"5877_CR24","doi-asserted-by":"publisher","first-page":"932","DOI":"10.1016\/s0043-1648(01)00825-0","volume":"249","author":"NR Dhar","year":"2002","unstructured":"Dhar NR, Paul S, Chattopadhyay AB (2002) The influence of cryogenic cooling on tool wear, dimensional accuracy and surface finish in turning AISI 1040 and E4340C steels. Wear 249:932\u2013942. https:\/\/doi.org\/10.1016\/s0043-1648(01)00825-0","journal-title":"Wear"},{"key":"5877_CR25","doi-asserted-by":"publisher","first-page":"748","DOI":"10.1016\/j.ijmachtools.2006.09.017","volume":"47","author":"NR Dhar","year":"2007","unstructured":"Dhar NR, Ahmed MT, Islam S (2007) An experimental investigation on effect of minimum quantity lubrication in machining AISI 1040 steel. Int J Mach Tools Manuf 47:748\u2013753. https:\/\/doi.org\/10.1016\/j.ijmachtools.2006.09.017","journal-title":"Int J Mach Tools Manuf"},{"key":"5877_CR26","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1243\/13506501jet475","volume":"223","author":"P Vamsi Krishna","year":"2009","unstructured":"Vamsi Krishna P, Rao DN, Srikant RR (2009) Predictive modelling of surface roughness and tool wear in solid lubricant assisted turning of AISI 1040 steel. Proc Inst Mech Eng J Eng Tribol 223:929\u2013934. https:\/\/doi.org\/10.1243\/13506501jet475","journal-title":"Proc Inst Mech Eng J Eng Tribol"},{"key":"5877_CR27","doi-asserted-by":"publisher","first-page":"1491","DOI":"10.1243\/09544054jem1862","volume":"224","author":"SV Ramana","year":"2010","unstructured":"Ramana SV, Ramji K, Satyanarayana B (2010) Studies on the behaviour of the green particulate fluid lubricant in its nano regime when machining AISI 1040 steel. Proc Inst Mech Eng B J Eng Manuf 224:1491\u20131501. https:\/\/doi.org\/10.1243\/09544054jem1862","journal-title":"Proc Inst Mech Eng B J Eng Manuf"},{"key":"5877_CR28","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1016\/j.ijmachtools.2010.06.001","volume":"50","author":"P Vamsi Krishna","year":"2010","unstructured":"Vamsi Krishna P, Srikant RR, Nageswara Rao D (2010) Experimental investigation on the performance of nanoboric acid suspensions in SAE-40 and coconut oil during turning of AISI 1040 steel. Int J Mach Tools Manuf 50:911\u2013916. https:\/\/doi.org\/10.1016\/j.ijmachtools.2010.06.001","journal-title":"Int J Mach Tools Manuf"},{"key":"5877_CR29","doi-asserted-by":"publisher","first-page":"1334","DOI":"10.1177\/1350650113491934","volume":"227","author":"M Amrita","year":"2013","unstructured":"Amrita M, Srikant R, Sitaramaraju A, Prasad M, Krishna PV (2013) Experimental investigations on influence of mist cooling using nanofluids on machining parameters in turning AISI 1040 steel. Proc Inst Mech Eng J Eng Tribol 227:1334\u20131346. https:\/\/doi.org\/10.1177\/1350650113491934","journal-title":"Proc Inst Mech Eng J Eng Tribol"},{"key":"5877_CR30","doi-asserted-by":"publisher","first-page":"1570","DOI":"10.1177\/0954406213509612","volume":"228","author":"S Srikiran","year":"2014","unstructured":"Srikiran S, Ramji K, Satyanarayana B, Ramana S (2014) Investigation on turning of AISI 1040 steel with the application of nano-crystalline graphite powder as lubricant. Proc Inst Mech Eng C J Mech Eng Sci 228:1570\u20131580. https:\/\/doi.org\/10.1177\/0954406213509612","journal-title":"Proc Inst Mech Eng C J Mech Eng Sci"},{"key":"5877_CR31","doi-asserted-by":"publisher","first-page":"373","DOI":"10.1007\/s40032-015-0178-9","volume":"96","author":"MK Gupta","year":"2015","unstructured":"Gupta MK, Singh G, Sood PK (2015) Experimental investigation of machining AISI 1040 medium carbon steel under cryogenic machining: a comparison with dry machining. J Inst Eng India Ser C 96:373\u2013379. https:\/\/doi.org\/10.1007\/s40032-015-0178-9","journal-title":"J Inst Eng India Ser C"},{"key":"5877_CR32","doi-asserted-by":"publisher","first-page":"493","DOI":"10.1177\/1350650115601694","volume":"230","author":"R Padmini","year":"2016","unstructured":"Padmini R, Krishna PV, Mohana Rao GK (2016) Experimental evaluation of nano-molybdenum disulphide and nano-boric acid suspensions in vegetable oils as prospective cutting fluids during turning of AISI 1040 steel. Proc Inst Mech Eng J Eng Tribol 230:493\u2013505. https:\/\/doi.org\/10.1177\/1350650115601694","journal-title":"Proc Inst Mech Eng J Eng Tribol"},{"key":"5877_CR33","doi-asserted-by":"publisher","DOI":"10.1007\/s40430-018-1379-y","author":"BS Ajay Vardhaman","year":"2018","unstructured":"Ajay Vardhaman BS, Amarnath M, Jhodkar D, Ramkumar J, Chelladurai H, Roy MK (2018) Influence of coconut oil on tribological behavior of carbide cutting tool insert during turning operation. J Braz Soc Mech Sci Eng. https:\/\/doi.org\/10.1007\/s40430-018-1379-y","journal-title":"J Braz Soc Mech Sci Eng"},{"key":"5877_CR34","doi-asserted-by":"publisher","first-page":"2349","DOI":"10.1007\/s00521-017-3192-4","volume":"31","author":"M Mia","year":"2019","unstructured":"Mia M, Dhar NR (2019) Prediction and optimization by using SVR, RSM and GA in hard turning of tempered AISI 1060 steel under effective cooling condition. Neural Comput Appl 31:2349\u20132370. https:\/\/doi.org\/10.1007\/s00521-017-3192-4","journal-title":"Neural Comput Appl"},{"key":"5877_CR35","doi-asserted-by":"publisher","first-page":"70","DOI":"10.24874\/ti.2020.42.01.07","volume":"42","author":"M Usha","year":"2020","unstructured":"Usha M, Rao GS (2020) Optimization of multiple objectives by genetic algorithm for turning of AISI 1040 steel using Al2O3 nano fluid with MQL. Trib Ind 42:70\u201380. https:\/\/doi.org\/10.24874\/ti.2020.42.01.07","journal-title":"Trib Ind"},{"key":"5877_CR36","doi-asserted-by":"publisher","first-page":"85","DOI":"10.3139\/120.111458","volume":"62","author":"A Sahinoglu","year":"2020","unstructured":"Sahinoglu A, Rafighi M (2020) Optimization of cutting parameters with respect to roughness for machining of hardened AISI 1040 steel. Mater Test 62:85\u201395. https:\/\/doi.org\/10.3139\/120.111458","journal-title":"Mater Test"},{"key":"5877_CR37","doi-asserted-by":"publisher","DOI":"10.1080\/14484846.2020.1756067","author":"S Gugulothu","year":"2020","unstructured":"Gugulothu S, Pasa VK (2020) Experimental investigation to study the performance of CNT\/MoS2 hybrid nanofluid in turning of AISI 1040 steel. Aust J Mech Eng. https:\/\/doi.org\/10.1080\/14484846.2020.1756067","journal-title":"Aust J Mech Eng"},{"key":"5877_CR38","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1016\/j.ins.2012.03.005","volume":"210","author":"AR Yildiz","year":"2012","unstructured":"Yildiz AR (2012) A comparative study of population-based optimization algorithms for turning operations. Inf Sci 210:81\u201388. https:\/\/doi.org\/10.1016\/j.ins.2012.03.005","journal-title":"Inf Sci"},{"key":"5877_CR39","doi-asserted-by":"publisher","first-page":"1543","DOI":"10.1016\/j.asoc.2012.03.071","volume":"13","author":"C Ahilan","year":"2013","unstructured":"Ahilan C, Kumanan S, Sivakumaran N, Edwin Raja Dhas J (2013) Modeling and prediction of machining quality in CNC turning process using intelligent hybrid decision making tools. Appl Soft Comput 13:1543\u20131551. https:\/\/doi.org\/10.1016\/j.asoc.2012.03.071","journal-title":"Appl Soft Comput"},{"key":"5877_CR40","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s00170-009-2104-x","volume":"46","author":"M Chandrasekaran","year":"2010","unstructured":"Chandrasekaran M, Muralidhar M, Krishna CM, Dixit US (2010) Application of soft computing techniques in machining performance prediction and optimization: a literature review. Int J Adv Manuf Technol 46:445\u2013464. https:\/\/doi.org\/10.1007\/s00170-009-2104-x","journal-title":"Int J Adv Manuf Technol"},{"key":"5877_CR41","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1504\/ijmic.2013.056184","volume":"20","author":"A Garg","year":"2013","unstructured":"Garg A, Bhalerao Y, Tai K (2013) Review of empirical modelling techniques for modelling of turning process. Int J Model Identif Control 20:121\u2013129. https:\/\/doi.org\/10.1504\/ijmic.2013.056184","journal-title":"Int J Model Identif Control"},{"key":"5877_CR42","doi-asserted-by":"publisher","first-page":"105743","DOI":"10.1016\/j.asoc.2019.105743","volume":"84","author":"TV Sibalija","year":"2019","unstructured":"Sibalija TV (2019) Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008\u20132018). Appl Soft Comput 84:105743. https:\/\/doi.org\/10.1016\/j.asoc.2019.105743","journal-title":"Appl Soft Comput"},{"key":"5877_CR43","doi-asserted-by":"publisher","first-page":"9909","DOI":"10.1016\/j.eswa.2012.02.109","volume":"39","author":"N Yusup","year":"2012","unstructured":"Yusup N, Zain AM, Hashim SZM (2012) Evolutionary techniques in optimizing machining parameters: review and recent applications (2007\u20132011). Expert Syst Appl 39:9909\u20139927. https:\/\/doi.org\/10.1016\/j.eswa.2012.02.109","journal-title":"Expert Syst Appl"},{"key":"5877_CR44","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.engappai.2017.08.005","volume":"65","author":"SP Leo Kumar","year":"2017","unstructured":"Leo Kumar SP (2017) State of the art-intense review on artificial intelligence systems application process i planning and manufacturing. Eng Appl Artif Intell 65:294\u2013329. https:\/\/doi.org\/10.1016\/j.engappai.2017.08.005","journal-title":"Eng Appl Artif Intell"},{"key":"5877_CR45","doi-asserted-by":"publisher","first-page":"632","DOI":"10.2507\/IJSIMM18(4)495","volume":"18","author":"G Sterpin Valic","year":"2019","unstructured":"Sterpin Valic G, Cukor G, Jurkovic Z, Brezocnik M (2019) Multi-criteria optimization of turning of martensitic stainless steel for sustainability. Int J Simul Model 18:632\u2013642. https:\/\/doi.org\/10.2507\/IJSIMM18(4)495","journal-title":"Int J Simul Model"},{"key":"5877_CR46","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1016\/j.jestch.2019.09.003","volume":"23","author":"T Ghosh","year":"2020","unstructured":"Ghosh T, Martinsen K (2020) Generalized approach for multi-response machining process optimization using machine learning and evolutionary algorithms. Eng Sci Technol Int J 23:650\u2013663. https:\/\/doi.org\/10.1016\/j.jestch.2019.09.003","journal-title":"Eng Sci Technol Int J"},{"key":"5877_CR47","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1016\/j.simpat.2018.02.008","volume":"84","author":"H Chavez-Garcia","year":"2018","unstructured":"Chavez-Garcia H, Castillo-Villar KK (2018) Simulation-based model for the optimization of machining parameters in a metal-cutting operation. Simul Model Pract Theory 84:204\u2013221. https:\/\/doi.org\/10.1016\/j.simpat.2018.02.008","journal-title":"Simul Model Pract Theory"},{"key":"5877_CR48","doi-asserted-by":"publisher","first-page":"1889","DOI":"10.1007\/s00170-019-03988-5","volume":"104","author":"D Weichert","year":"2019","unstructured":"Weichert D, Link P, Stoll A, Ruping S, Ihlenfeldt S, Wrobel S (2019) A review of machine learning for the optimization of production processes. Int J Adv Manuf Technol 104:1889\u20131902. https:\/\/doi.org\/10.1007\/s00170-019-03988-5","journal-title":"Int J Adv Manuf Technol"},{"key":"5877_CR49","doi-asserted-by":"publisher","first-page":"16245","DOI":"10.1007\/s00521-020-04849-z","volume":"32","author":"N Rana","year":"2020","unstructured":"Rana N, Latiff MSA, Abdulhamid SM, Chiroma H (2020) Whale optimization algorithm: a systematic review of contemporary applications, modifications and developments. Neural Comput Appl 32:16245\u201316277. https:\/\/doi.org\/10.1007\/s00521-020-04849-z","journal-title":"Neural Comput Appl"},{"key":"5877_CR50","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1007\/s10462-011-9212-3","volume":"36","author":"S Srinivasan","year":"2011","unstructured":"Srinivasan S, Ramakrishnan S (2011) Evolutionary multi objective optimization for rule mining: a review. Artif Intell Rev 36:205\u2013248. https:\/\/doi.org\/10.1007\/s10462-011-9212-3","journal-title":"Artif Intell Rev"},{"key":"5877_CR51","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1504\/ijbic.2019.101640","volume":"14","author":"M Ojha","year":"2019","unstructured":"Ojha M, Singh KP, Chakraborty P, Verma S (2019) A review of multi-objective optimisation and decision making using evolutionary algorithms. Int J Bio Inspir Com 14:69. https:\/\/doi.org\/10.1504\/ijbic.2019.101640","journal-title":"Int J Bio Inspir Com"},{"key":"5877_CR52","doi-asserted-by":"publisher","first-page":"106382","DOI":"10.1016\/j.asoc.2020.106382","volume":"93","author":"Q Liu","year":"2020","unstructured":"Liu Q, Li X, Liu H, Guo Z (2020) Multi-objective metaheuristics for discrete optimization problems: a review of the state-of-the-art. Appl Soft Comput 93:106382. https:\/\/doi.org\/10.1016\/j.asoc.2020.106382","journal-title":"Appl Soft Comput"},{"key":"5877_CR53","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1007\/s00521-016-2360-2","volume":"28","author":"H Gullu","year":"2017","unstructured":"Gullu H (2017) A novel approach to prediction of rheological characteristics of jet grout cement mixtures via genetic expression programming. Neural Comput Appl 28:407\u2013420. https:\/\/doi.org\/10.1007\/s00521-016-2360-2","journal-title":"Neural Comput Appl"},{"key":"5877_CR54","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.engappai.2005.06.007","volume":"19","author":"R Quiza Sardinas","year":"2006","unstructured":"Quiza Sardinas R, Rivas Santana M, Alfonso Brindis E (2006) Genetic algorithm-based multi-objective optimization of cutting parameters in turning processes. Eng Appl Artif Intell 19:127\u2013133. https:\/\/doi.org\/10.1016\/j.engappai.2005.06.007","journal-title":"Eng Appl Artif Intell"},{"key":"5877_CR55","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1016\/j.procir.2013.05.055","volume":"7","author":"DM D\u2019Addona","year":"2013","unstructured":"D\u2019Addona DM, Teti R (2013) Genetic algorithm-based optimization of cutting parameters in turning processes. Procedia CIRP 7:323\u2013328. https:\/\/doi.org\/10.1016\/j.procir.2013.05.055","journal-title":"Procedia CIRP"},{"key":"5877_CR56","doi-asserted-by":"publisher","first-page":"517","DOI":"10.4028\/www.scientific.net\/amm.281.517","volume":"281","author":"J Lv","year":"2013","unstructured":"Lv J, Zhao JB, Liu QG (2013) Optimization of cutting parameters based on multi-objective genetic algorithm NSGA- II. Appl Mech Mater 281:517\u2013522. https:\/\/doi.org\/10.4028\/www.scientific.net\/amm.281.517","journal-title":"Appl Mech Mater"},{"key":"5877_CR57","doi-asserted-by":"publisher","first-page":"366","DOI":"10.14743\/apem2016.4.234","volume":"11","author":"S Klancnik","year":"2016","unstructured":"Klancnik S, Hrelja M, Balic J, Brezocnik M (2016) Multi-objective optimization of the turning process using a gravitational search algorithm (GSA) and NSGA-II approach. Adv Prod Eng Manag 11:366\u2013376. https:\/\/doi.org\/10.14743\/apem2016.4.234","journal-title":"Adv Prod Eng Manag"},{"key":"5877_CR58","doi-asserted-by":"publisher","first-page":"12240","DOI":"10.1016\/j.matpr.2018.02.201","volume":"5","author":"O Manav","year":"2018","unstructured":"Manav O, Chinchanikar S (2018) Multi-objective optimization of hard turning: a genetic algorithm approach. Mater Today 5:12240\u201312248. https:\/\/doi.org\/10.1016\/j.matpr.2018.02.201","journal-title":"Mater Today"},{"key":"5877_CR59","doi-asserted-by":"publisher","first-page":"6897","DOI":"10.1016\/j.matpr.2017.11.351","volume":"5","author":"N Sathiya Narayanan","year":"2018","unstructured":"Sathiya Narayanan N, Baskar N, Ganesan M (2018) Multi objective optimization of machining parameters for hard turning OHNS\/AISI H13 material, using genetic algorithm. Mater Today 5:6897\u20136905. https:\/\/doi.org\/10.1016\/j.matpr.2017.11.351","journal-title":"Mater Today"},{"key":"5877_CR60","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1007\/s00521-007-0166-y","volume":"18","author":"D Venkatesan","year":"2009","unstructured":"Venkatesan D, Kannan K, Saravanan R (2009) A genetic algorithm-based artificial neural network model for the optimization of machining processes. Neural Comput Appl 18:135\u2013140. https:\/\/doi.org\/10.1007\/s00521-007-0166-y","journal-title":"Neural Comput Appl"},{"key":"5877_CR61","doi-asserted-by":"publisher","first-page":"318","DOI":"10.14743\/apem2020.3.368","volume":"15","author":"M Jasiewicz","year":"2020","unstructured":"Jasiewicz M, Miadlicki K (2020) An integrated CNC system for chatter suppression in turning. Adv Prod Eng Manag 15:318\u2013330. https:\/\/doi.org\/10.14743\/apem2020.3.368","journal-title":"Adv Prod Eng Manag"},{"key":"5877_CR62","doi-asserted-by":"publisher","first-page":"689","DOI":"10.2507\/IJSIMM18(4)CO17","volume":"18","author":"MS Yang","year":"2019","unstructured":"Yang MS, Ba L, Xu EB, Li Y, Gao XQ, Liu Y, Li Y (2019) Batch optimization in integrated scheduling of machining and assembly. Int J Simul Model 18:689\u2013698. https:\/\/doi.org\/10.2507\/IJSIMM18(4)CO17","journal-title":"Int J Simul Model"},{"key":"5877_CR63","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-01007-1","volume-title":"Applied machining technology","author":"H Tschatsch","year":"2009","unstructured":"Tschatsch H (2009) Applied machining technology. Springer, Berlin. https:\/\/doi.org\/10.1007\/978-3-642-01007-1"},{"key":"5877_CR64","volume-title":"Multi-objective optimization using evolutionary algorithms","author":"D Kalyanmoy","year":"2001","unstructured":"Kalyanmoy D (2001) Multi-objective optimization using evolutionary algorithms. Wiley, Chichester"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05877-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-05877-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-05877-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,10,19]],"date-time":"2021-10-19T00:27:35Z","timestamp":1634603255000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-05877-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,7]]},"references-count":64,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2021,10]]}},"alternative-id":["5877"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-05877-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,4,7]]},"assertion":[{"value":"13 November 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 April 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}