[1] Ni Y, Li YG, Liu CQ et al (2022) A mechanism informed neural network for predicting machining deformation of annular parts. Adv Eng Inform 53:101661. https://doi.org/10.1016/j.aei.2022.101661 [2] Liu HB, Wang CX, Li T et al (2022) Fixturing technology and system for thin-walled parts machining: a review. Front Mech Eng 17:55. https://doi.org/10.1007/s11465-022-0711-5 [3] Li X, Gong YD, Ding MX et al (2023) Research on prediction and compensation strategy of milling deformation error of aitanium alloy integral blisk blade. Int J Adv Manuf Technol 127:1-19 [4] Zhang ZZ, Cai YL, Xi XL et al (2023) Non-uniform machining allowance planning method of thin-walled parts based on the workpiece deformation constraint. Int J Adv Manuf Technol 124(7):2185-2198 [5] Sun H, Zhao SQ, Peng FY et al (2022) In-situ prediction of machining errors of thin-walled parts: an engineering knowledge based sparse Bayesian learning approach. J Intell Manuf 35:387-411 [6] Zhang T, Li BB, Zhao SQ et al (2022) A knowledge-embedded end-to-end intelligent reasoning method for processing quality of shaft parts. In: Liu H, Yin ZP, Liu LQ (eds) Intelligent robotics and applications. The 15th international conference, ICIRA 2022, Harbin, China, 1-3 August, 2022, Proceedings, Part IV, Springer, Cham. https://doi.org/10.1007/978-3-031-13841-6_39 [7] Lacalle LNLD, Lamikiz A, Sánchez JA et al (2007) Toolpath selection based on the minimum deflection cutting forces in the programming of complex surfaces milling. Int J Mach Tools Manuf 47(2):388-400 [8] Geng L, Liu PL, Liu K (2015) Optimization of cutter posture based on cutting force prediction for five-axis machining with ball-end cutters. Int J Adv Manuf Technol 78(5/8):1289-1303 [9] Ma JW, Song DN, Jia ZY et al (2018) Tool-path planning with constraint of cutting force fluctuation for curved surface machining. Precis Eng J Int Soc Precis Eng Nanotechnol 51:614-624 [10] Wang L, Yuan X, Si H et al (2019) Feedrate scheduling method for constant peak cutting force in five-axis flank milling process. Chin J Aeronaut 33(7):2055-2069 [11] Sivasakthivel PS, Sudhakaran R (2013) Optimization of machining parameters on temperature rise in end milling of Al 6063 using response surface methodology and genetic algorithm. Int J Adv Manuf Technol 67:2313-2323 [12] Cakiroglu R, Acr A (2013) Optimization of cutting parameters on drill bit temperature in drilling by Taguchi method. Measurement 46(9):3525-3531 [13] Wei B, Tan G, Yin N et al (2016) Research on inverse problems of heat flux and simulation of transient temperature field in high-speed milling. Int J Adv Manuf Technol 84(9/12):2067-2078 [14] Mirkoohi E, Bocchini P, Liang SY (2019) Analytical temperature predictive modeling and non-linear optimization in machining. Int J Adv Manuf Technol 102:1557-1566 [15] Hu PC, Kai T (2011) Improving the dynamics of five-axis machining through optimization of workpiece setup and tool orientations. Comput Aided Des 43(12):1693-1706 [16] Huang T, Zhang XM, Jürgen L et al (2018) Tool orientation planning in milling with process dynamic constraints: a minimax optimization approach. J Manuf Sci Eng 140(11):111002. https://doi.org/10.1115/1.4040872 [17] Mokhtari A, Jalili MM, Mazidi A (2021) Optimization of different parameters related to milling tools to maximize the allowable cutting depth for chatter-free machining. Proc Inst Mech Eng Part B J Eng Manuf 235(1/2):230-241 [18] Lin L, He M, Wang Q et al (2021) Chatter stability prediction and process parameters’ optimization of milling considering uncertain tool information. Symmetry 13(6):1071. https://doi.org/10.3390/sym13061071 [19] Soori M, Arezoo B, Habibi M (2016) Tool deflection error of three-axis computer numerical control milling machines, monitoring and minimizing by a virtual machining system. J Manuf Sci Eng 138(8):081005. https://doi.org/10.1115/1.4032393 [20] Duan XY, Peng FY, Zhu KP et al (2019) Tool orientation optimization considering cutter deflection error caused by cutting force for multi-axis sculptured surface milling. Int J Adv Manuf Technol 103(5/8):1925-1934 [21] Silva L, Yoshioka H, Shinno H et al (2019) Tool orientation angle optimization for a multi-axis robotic milling system. Int J Autom Technol 13(5):574-582 [22] Xiao QB, Wan M, Zhang WH et al (2022) Tool orientation optimization for the five-axis CNC machining to constrain the contour errors without interference. J Manuf Process 76:46-56 [23] Koike Y, Matsubara A, Yamaji I (2013) Design method of material removal process for minimizing workpiece displacement at cutting point. CIRP Ann Manuf Technol 62(1):419-422 [24] Li ZL, Zhu LM (2014) Envelope surface modeling and tool path optimization for five-axis flank milling considering cutter runout. J Manuf Sci Eng Trans ASME 136(4):041021. https://doi.org/10.1115/1.4027415 [25] Li ZP, Peng FY, Yan R et al (2021) Configuration optimization through redundancy angle and tool posture by force induced error index in robot ball-end milling. Procedia CIRP 101:150-153 [26] Li XY, Li L, Yang YF et al (2022) Machining deformation of single-sided component based on finishing allowance optimization. Chin J Aeronaut 33(9):2434-2444 [27] Li ZP, Peng FY, Yan R et al (2022) A virtual repulsive potential field algorithm of posture trajectory planning for precision improvement in robotic multi-axis milling. Robot Comput Integr Manuf 74:102288. https://doi.org/10.1016/j.rcim.2021.102288 [28] Lan T (2010) Fuzzy deduction material removal rate optimization for computer numerical control turning. Am J Appl Sci 7(7):1026-1031 [29] Das MK, Kumar K, Barman TK et al (2012) Optimization of material removal rate in EDM using Taguchi method. Adv Mater Res 626:270-274 [30] Mukherjee S, Kamal A, Kumar K (2014) Optimization of material removal rate during turning of SAE 1020 material in CNC lathe using Taguchi technique. Proc Eng 97:29-35 [31] Ringgaard K, Mohammadi Y, Merrild C et al (2019) Optimization of material removal rate in milling of thin-walled structures using penalty cost function. Int J Mach Tools Manuf 145:103430. https://doi.org/10.1016/j.ijmachtools.2019.103430 [32] Balogun VA, Edem IF, Adekunle AA et al (2016) Specific energy based evaluation of machining efficiency. J Clean Prod 116:187-197 [33] Xu K, Luo M, Tang K (2016) Machine based energy-saving tool path generation for five-axis end milling of freeform surfaces. J Clean Prod 139:1207-1223 [34] Zhang C, Jiang P, Zhang L et al (2017) Energy-aware integration of process planning and scheduling of advanced machining workshop. Proc Inst Mech Eng Part B J Eng Manuf 231(11):2040-2055 [35] Shin SJ, Woo J, Rachuri S (2017) Energy efficiency of milling machining: component modeling and online optimization of cutting parameters. J Clean Prod 161:12-29 [36] Xu K, Tang K (2014) Five-axis tool path and feed rate optimization based on the cutting force-area quotient potential field. Int J Adv Manuf Technol 75(9/12):1661-1679 [37] Li C, Chen X, Tang Y et al (2017) Selection of optimum parameters in multi-pass face milling for maximum energy efficiency and minimum production cost. J Clean Prod 140:1805-1818 [38] Cui XB, Guo JX (2018) Identification of the optimum cutting parameters in intermittent hard turning with specific cutting energy, damage equivalent stress, and surface roughness considered. Int J Adv Manuf Technol 96:4281-4293 [39] Zhu ZR, Peng FY, Tang XW et al (2019) Specific cutting energy index (SCEI)-based process signature for high-performance milling of hardened steel. Int J Adv Manuf Technol 103:1-13 [40] Chen C, Peng FY, Yan R et al (2019) Stiffness performance index based posture and feed orientation optimization in robotic milling process. Robot Comput Integr Manuf 55:29-40 [41] Zhu ZR, Peng FY, Yan R et al (2020) Influence mechanism of machining angles on force induced error and their selection in five axis bullnose end milling. Chin J Aeronaut 33(12):3447-3459 [42] Ye CC, Yang JX, Zhao H et al (2021) Task-dependent workpiece placement optimization for minimizing contour errors induced by the low posture-dependent stiffness of robotic milling. Int J Mech Sci 205:106601. https://doi.org/10.1016/j.ijmecsci.2021.106601 [43] Chen QZ, Zhang CR, Hu TL et al (2022) Posture optimization in robotic machining based on comprehensive deformation index considering spindle weight and cutting force. Robot Comput Integr Manuf 74:102290. https://doi.org/10.1016/j.rcim.2021.102290 [44] Sun YW, Xu JT, Guo DM et al (2009) A unified localization approach for machining allowance optimization of complex curved surfaces. Precis Eng 33(4):516-523 [45] Zhang Y, Zhang DH, Wu BH (2015) An approach for machining allowance optimization of complex parts with integrated structure. J Comput Des Eng 2:248-252 [46] Wu XN, Dai W (2016) Research on machining allowance distribution optimization based on processing defect risk. Procedia CIRP 56:508-511 [47] Chen YZ, Chen WF, Liang RJ et al (2017) Machining allowance optimal distribution of thin-walled structure based on deformation control. Appl Mech Mater 868:158-165 [48] Jiang S, Li YG, Liu CQ (2018) A non-uniform allowance allocation method based on interim state stiffness of machining features for NC programming of structural parts. Vis Comput Ind Biomed Art 1:4. https://doi.org/10.1186/s42492-018-0005-2 [49] Wu BH, Zhang Y, Liu GX et al (2021) Feedrate optimization method based on machining allowance optimization and constant power constraint. Int J Adv Manuf Technol 115(9/10):3345-3360 [50] Xin HM, Dong MM, Xian C et al (2023) Optimization method for rough-finish milling allowance based on depth control of milling affected layer. Int J Adv Manuf Technol 126(5/6):2083-2095 [51] Sun H, Peng FY, Zhou L et al (2020) A hybrid driven approach to integrate surrogate model and Bayesian framework for the prediction of machining errors of thin-walled parts. Int J Mech Sci 192:106111. https://doi.org/10.1016/j.ijmecsci.2020.106111 [52] Sun H, Peng FY, Zhao SQ et al (2022) Uncertainty calibration and quantification of surrogate model for estimating the machining distortion of thin-walled parts. Int J Adv Manuf Technol 120(1):719-741 [53] Zhu ZR, Peng FY, Yan R et al (2018) High efficiency simulation of five-axis cutting force based on the symbolically solvable cutting contact boundary model. Int J Adv Manuf Technol 94(5/8):2435-2455 [54] Jin JH, Shi JJ (1999) State space modeling of sheet metal assembly for dimensional control. J Manuf Sci Eng Trans ASME 121(4):756-762 [55] Zhou SY, Huang Q, Shi JJ (2003) State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors. IEEE Trans Robot Autom 19(2):296-309 [56] Zhang L, Zhang ZS, Zhou YF et al (2013) Stream of variation modeling and analysis for manufacturing processes based on a semi-parametric regression model. Chin J Mech Eng 49(15):180-185 [57] Sun H, Zhao SQ, Zhang T et al (2022) Analysis and inference of stream of dimensional errors in multistage machining process based on an improved semi-parametric model. In: 2022 IEEE/ASME international conference on advanced intelligent mechatronics (AIM), 11-15 July, Sapporo, Hokkaido, Japan [58] Frazier PI (2018) A tutorial on Bayesian optimization. arXiv 1807.02811. https://doi.org/10.48550/arXiv.1807.02811 [59] Hoteit H (2023) Uncertainty analysis of CO2 storage in deep saline aquifers using machine learning and Bayesian optimization. Energies 16(4):1684. https://doi.org/10.3390/en16041684 [60] Sun J, Wu S, Zhang H et al (2022) Based on multi-algorithm hybrid method to predict the slope safety factor-stacking ensemble learning with bayesian optimization. J Comput Sci 59:105187. https://doi.org/10.1016/j.jocs.2022.101587 [61] Patil JJ, Wan TC, Gong S et al (2023) Bayesian-optimization-assisted laser reduction of poly(acrylonitrile) for electrochemical application. ACS Nano 17(5):4999-5013 [62] Rasmussen CE (2003) Gaussian processes in machine learning. In: Advanced lectures on machine learning, ML Summer Schools, Canberra, Australia, 2-14 Feb 2003, pp 63-71 [63] Jones DR, Schonlau M, Welch WJ (1998) Efficient global optimization of expensive black-box functions. J Global Optim 13(4):455-492 |