Abstract
Existing researches on coated tools do not provide predicted data about machining performance while exploring their changing rules. Meanwhile, the traditional cutting process parameters neither guarantees the surface quality of the 30CrMnSiNi2A nor attains high material removal rate (MRR). Accurate control and prediction of workpiece three-dimensional surface roughness (Sq) and specific cutting energy consumption (SCEC) play an important role in improving the quality, reducing the cost of workpieces, and improving the processing efficiency. In this paper, according to the new SCEC geometric calculation approach and the influence of measuring position on Sq, the SCEC and Sq values can be accurately obtained. Then, based on the idea of the fitting formula, the influence of cutting parameters on SCEC and Sq in high-speed dry (HSD) milling of 30CrMnSiNi2A steel with CVD and PVD coated inserts is analyzed. Finally, the SCEC and Sq prediction models considering coating type, cutting speed, feed per tooth, and cutting width are established by using the XGBoost algorithm. The R2 values of SCEC and Sq are 0.92465 and 0.91527, respectively, indicating that the model has a good prediction effect on experimental data. The feasibility of HSD milling of 30CrMnSiNi2A steel with CVD and PVD coated inserts is verified by analyzing SCEC, Sq, and cutting temperature, which provides an experimental basis for high efficiency and high precision machining of 30CrMnSiNi2A steel.
Similar content being viewed by others
Data availability
All authors confirm that the data supporting the findings of this study are available within the article.
References
Niu QL, Dong DP, Chen M, Zhang YS, Wang CD (2013) Dry milling of the ultra-high-strength steel 30CrMnSiNi2A with coated carbide inserts. J Shanghai Jiaotong Univ (Sci) 18(4):468–473. https://doi.org/10.1007/s12204-013-1410-5
An QL, Wang CY, Xu JY, Liu PL, Chen M (2014) Experimental investigation on hard milling of high strength steel using PVD-AlTiN coated cemented carbide tool. Int J Refract Met H 43:94–101. https://doi.org/10.1016/j.ijrmhm.2013.11.007
Chen M, Wang CD, Jiang L, Niu QL (2012) Experimental study on milling parameters regression and optimization of super high strength steel 30CrMnSiNi2A. Mater Sci Forum 723:293–298. https://doi.org/10.4028/www.scientific.net/MSF.723.293
Zhang H, Dang JQ, Ming WW, Xu XW, Chen M, An QL (2020) Cutting responses of additive manufactured Ti6Al4V with solid ceramic tool under dry high-speed milling processes. Ceram Int 46(10):14536–14547. https://doi.org/10.1016/j.ceramint.2020.02.253
Goindi GS, Sarkar P (2017) Dry machining: a step towards sustainable machining–challenges and future directions. J Clean Prod 165:1557–1571. https://doi.org/10.1016/j.jclepro.2017.07.235
Cui XB, Wang D, Guo JX (2016) Performance optimization for cemented carbide tool in high-speed milling of hardened steel with initial microstructure considered. Int J Mech Sci 114:52–59. https://doi.org/10.1016/j.ijmecsci.2016.05.017
Cho IS, Amanov A, Kim JD (2015) The effects of AlCrN coating, surface modification and their combination on the tribological properties of high speed steel under dry conditions. Tribol Int 81:61–72. https://doi.org/10.1016/j.triboint.2014.08.003
Çöl M, Kir D, Erisir E (2013) Wear and blanking performance of AlCrN PVD-coated punches. Mater Sci 48(4):514–520. https://doi.org/10.1007/s11003-013-9532-3
He Q, Paiva JM, Kohlscheen J, Beake BD, Veldhuis SC (2020) An integrative approach to coating/carbide substrate design of CVD and PVD coated cutting tools during the machining of austenitic stainless steel. Ceram Int 46(4):5149–5158. https://doi.org/10.1016/j.ceramint.2019.10.259
Kivak T (2014) Optimization of surface roughness and flank wear using the Taguchi method in milling of Hadfield steel with PVD and CVD coated inserts. Meas 50:19–28. https://doi.org/10.1016/j.measurement.2013.12.017
Zhang L, Zhong ZQ, Qiu LC, Shi HD, Layyous A, Liu SP (2019) Coated cemented carbide tool life extension accompanied by comb cracks: the milling case of 316L stainless steel. Wear 418–419:133–139. https://doi.org/10.1016/j.wear.2018.11.019
Oomen-Hurst S, Abad MD, Khanna M, Veldhuis SC (2012) Comparative wear behavior studies of coated inserts during milling of NiCrMoV steel. Tribol Int 53:115–123. https://doi.org/10.1016/j.triboint.2012.02.020
M. Branham, TG. Gutowski, A. Jones, D.P Sekulic (2008) A thermodynamic framework for analyzing and improving manufacturing processes. IEEE international symposium on electronics and the environment p. 1–6. https://doi.org/10.1109/ISEE.2008.4562892.
Warren RD (1992) Analysis of material removal process. New York: USA p. 208–228.
Pawade RS, Sonawane HA, Joshi SS (2009) An analytical model to predict specific shear energy in high-speed turning of Inconel 718. Int J Mach Tool Manu 49(12–13):979–990. https://doi.org/10.1016/j.ijmachtools.2009.06.007
Duan ZJ, Li CH, Zhang YB, Dong L, Bai XF, Yang M, Jia DZ, Li RZ, Cao HJ, Xu XF (2021) Milling surface roughness for 7050 aluminum alloy cavity influenced by nozzle position of nanofluid minimum quantity lubrication. Chinese J Aeronaut 34(6):33–53. https://doi.org/10.1016/j.cja.2020.04.029
Duan ZJ, Li CH, Ding WF, Zhang YB, Yang M, Gao T, Cao HJ, Xu XF, Wang DZ, Mao C, Li HN, Kumar GM, Said Z, Debnath SJ, Jamil M, Ali HM (2021) Milling force model for aviation aluminum alloy: academic insight and perspective analysis. Chin J Mech Eng 34(18):1–35. https://doi.org/10.1186/s10033-021-00536-9
Chetan SG, Rao PV (2018) Specific cutting energy modeling for turning nickel-based Nimonic 90 alloy under MQL condition. Int J Mech Sci 146–147:25–38. https://doi.org/10.1016/j.ijmecsci.2018.07.033
Draganescu F, Gheorghe M, Doicin CV (2003) Models of machine tool efficiency and specific consumed energy. J Mater Process Tech 141(1):9–15. https://doi.org/10.1016/S0924-0136(02)00930-5
Bever MB, Marshall ER, Ticknor LB (1953) The energy stored in metal chips during orthogonal cutting. J Appl Phys 24:1176. https://doi.org/10.1063/1.1721466
Yin QG, Li CH, Dong L, Bai XF, Zhang YB, Yang M, Jia DZ, Li RZ, Liu ZQ (2021) Effects of physicochemical properties of different base oils on friction coefficient and surface roughness in MQL milling AISI 1045. Int J PR Eng Man-GT 8:1629–1647. https://doi.org/10.1007/s40684-021-00318-7
Benardos PG, Vosniakos G-C (2003) Predicting surface roughness in machining: a review. Int J Mach Tool Manu 43(8):833–844. https://doi.org/10.1016/S0890-6955(03)00059-2
Lee KY, Kang MC, Jeong YH, Lee DW, Kim JS (2001) Simulation of surface roughness and profile in high-speed end milling. J Mater Process Tech 113(3):410–415. https://doi.org/10.1016/S0924-0136(01)00697-5
Zhou L, Cheng K (2009) Dynamic cutting process modelling and its impact on the generation of surface topography and texture in nano/micro cutting. P I Mech Eng B-J Eng 223(3):247–266. https://doi.org/10.1243/09544054JEM1316
Oktem H, Erzurumlu T, Erzincanli F (2006) Prediction of minimum surface roughness in end milling mold parts using neural network and genetic algorithm. Mater Design 27(9):735–744. https://doi.org/10.1016/j.matdes.2005.01.010
Bharathi Raja S, Baskar N (2012) Application of particle swarm optimization technique for achieving desired milled surface roughness in minimum machining time. Expert Syst Appl 39(5):5982–5989. https://doi.org/10.1016/j.eswa.2011.11.110
Li Y, Huang YX, Zhao LJ, Liu CL (2020) Multi-condition wear evaluation of tool based on T-SNE and XGBoost. Chin J Mech Eng-En 56(01):132–140. https://doi.org/10.3901/JME.2020.01.132
Zhang JZ, Chen JC, Kirby ED (2007) Surface roughness optimization in an end-milling operation using the Taguchi design method. J Mater Process Tech 184(2):233–239. https://doi.org/10.1016/j.jmatprotec.2006.11.029
Fratila D, Caizar C (2011) Application of Taguchi method to selection of optimal lubrication and cutting conditions in face milling of AlMg3. J Clean Prod 19(6):640–645. https://doi.org/10.1016/j.jclepro.2010.12.007
Liu N, Wang SB, Zhang YF, Lu WF (2016) A novel approach to predicting surface roughness based on specific cutting energy consumption when slot milling Al-7075. Int J Mech Sci 118:13–20. https://doi.org/10.1016/j.ijmecsci.2016.09.002
Yan X, Tao H, Cai J, Li HB (2011) Model of the instantaneous un-deformed chip thickness in milling based on real tooth trajectory. Chin J Mech Eng-En 47(01):182–186. https://doi.org/10.1177/0954405416639890
Vinay V, Akhil K, Ramesh MR, Chakradhar D (2019) Investigation on the performance of AlCrN and AlTiN coated cemented carbide inserts during end milling of maraging steel under dry, wet and cryogenic environments. J Manuf Process 43:136–144. https://doi.org/10.1016/j.jmapro.2019.05.021
Xiong YF, Wang WH, Shi YY, Jiang RS, Shan CW, Liu XF, Lin KY (2021) Investigation on surface roughness, residual stress and fatigue property of milling in-stiu TiB2/7050Al metal matrix composites. Chinese J Aeronaut 34(4):451–464. https://doi.org/10.1016/j.cja.2020.08.046
ISO 25178–2 (2012) Geometrical product specification (GPS)-surface texture: areal-part2: terms, definitions and surface texture parameters. https://www.iso.org/obp/ui/#iso:std:iso:25178:-2:ed-1:v1:en.
Eysion A, LIU Q Z, (2011) Machined surface error analysis-a face milling approach. J Manuf Syst 10(2):293–307. https://doi.org/10.1142/S0219686711002211
Trifunovi M, Madi M, Jankovi P, Rodic D, Gostimirovic M (2021) Investigation of cutting and specific cutting energy in turning of POM-C using a PCD tool: analysis and some optimization aspects. J Clean Prod 303:127043. https://doi.org/10.1016/j.jclepro.2021.127043
Zhang HC, Kong LL, Li T, Chen JC (2015) SCE modeling and influencing trend analysis of cutting parameters. China Mech Eng 26(8):1098–1104. https://doi.org/10.3969/j.issn.1004132X.2015.08.019
Schulz H, Moriwaki T (1992) High speed machining. CIRP Ann 41(2):637–643
Chen TQ, Guestrin C (2016) XGBoost: a scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining 785–794.
Chen T Q, He T (2015) Higgs boson discovery with boosted trees. Proceedings of the NIPS 2014 workshop on high-energy physics and machine learning 69–80
Bi Y, Xiang DX, Ge ZY, Li FY, Jia CZ, Song JN (2020) An interpretable prediction model for identifying N7-methylguanosine sites based on XGBoost and SHAP. Mol Ther-Nucl Acids 22:362–372. https://doi.org/10.1016/j.omtn.2020.08.022
Ma J, Cheng JCP, Xu ZR, Chen KY, Lin CQ, Jiang FF (2020) Identification of the most influential areas for air pollution control using XGBoost and grid importance rank. J Clean Prod 274:122835. https://doi.org/10.1016/j.jclepro.2020.122835
Acknowledgements
The authors gratefully acknowledge the reviewers and editors for their insightful comments.
Funding
This work is supported by the National Key R&D Program of China (2020YFB2010500).
Author information
Authors and Affiliations
Contributions
Jin Zhang: conceptualization; investigation; methodology; validation; roles/writing—original draft. Xinzhen Kang: software; data curation. Huajun Cao: supervision; writing—review and editing; project administration; funding acquisition; resources. Hao Yi: Writing—review and editing. Xuefeng Huang: visualization. Chengchao Li: data curation. Guibao Tao: major revision opinion; project administration; funding acquisition; resources.
Corresponding author
Ethics declarations
Ethics approval
The manuscript has not been submitted to any other journal for simultaneous consideration. The submitted work is original and has not been published elsewhere in any form or language.
Consent to participate
All authors voluntarily agree to participate in this research study.
Consent for publication
All authors voluntarily agree to publish in this research study.
Conflict of interest
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Zhang, J., Kang, X., Cao, H. et al. Research on feasible region of specific cutting energy and surface roughness in high-speed dry milling of 30CrMnSiNi2A steel with CVD and PVD coated inserts. Int J Adv Manuf Technol 125, 133–155 (2023). https://doi.org/10.1007/s00170-022-10647-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-022-10647-9