Energy saving design of the machining unit of hobbing machine tool with integrated optimization

Abstract

The machining unit of hobbing machine tool accounts for a large portion of the energy consumption during the operating phase. The optimization design is a practical means of energy saving and can reduce energy consumption essentially. However, this issue has rarely been discussed in depth in previous research. A comprehensive function of energy consumption of the machining unit is built to address this problem. Surrogate models are established by using effective fitting methods. An integrated optimization model for reducing tool displacement and energy consumption is developed on the basis of the energy consumption function and surrogate models, and the parameters of the motor and structure are considered simultaneously. Results show that the energy consumption and tool displacement of the machining unit are reduced, indicating that energy saving is achieved and the machining accuracy is guaranteed. The influence of optimization variables on the objectives is analyzed to inform the design.

Graphical abstract

Keywords

energy saving design / energy consumption / machining unit / integrated optimization / machine tool

References

[1] Triebe M J , Zhao F , Sutherland J W . Genetic optimization for the design of a machine tool slide table for reduced energy consumption. Journal of Manufacturing Science and Engineering, 2021, 143(10): 101003

[2] Zhong Q Q , Tang R Z , Peng T . Decision rules for energy consumption minimization during material removal process in turning. Journal of Cleaner Production, 2017, 140(3): 1819–1827

[3] Papetti A , Menghi R , Di Domizio G , Germani M , Marconi M . Resources value mapping: a method to assess the resource efficiency of manufacturing systems. Applied Energy, 2019, 249: 326–342

[4] Zhao J H , Li L , Wang Y , Sutherland J W . Impact of surface machining complexity on energy consumption and efficiency in CNC milling. The International Journal of Advanced Manufacturing Technology, 2019, 102(9): 2891–2905

[5] Apostolos F , Alexios P , Georgios P , Panagiotis S , George C . Energy efficiency of manufacturing processes: a critical review. Procedia CIRP, 2013, 628–633

[6] Kara S , Li W . Unit process energy consumption models for material removal processes. CIRP Annals, 2011, 60(1): 37–40

[7] Xiao Y M , Jiang Z G , Gu Q , Yan W , Wang R P . A novel approach to CNC machining center processing parameters optimization considering energy-saving and low-cost. Journal of Manufacturing Systems, 2021, 59: 535–548

[8] Zhang T , Liu Z Q , Shi Z Y , Xu C H . Investigation on size effect of specific cutting energy in mechanical micro-cutting. The International Journal of Advanced Manufacturing Technology, 2017, 91(5): 2621–2633

[9] Xiao Q G , Li C B , Tang Y , Pan J , Yu J , Chen X Z . Multi-component energy modeling and optimization for sustainable dry gear hobbing. Energy, 2019, 187: 115911

[10] Li C B , Xiao Q G , Tang Y , Li L . A method integrating Taguchi, RSM and MOPSO to CNC machining parameters optimization for energy saving. Journal of Cleaner Production, 2016, 135: 263–275

[11] Vu N C, Dang X P, Huang S C. Multi-objective optimization of hard milling process of AISI H13 in terms of productivity, quality, and cutting energy under nanofluid minimum quantity lubrication condition. Measurement and Control, 2021, 54(5–6): 820–834

[12] Nguyen T T , Nguyen T A , Trinh Q H . Optimization of milling parameters for energy savings and surface quality. Arabian Journal for Science and Engineering, 2020, 45(11): 9111–9125

[13] Arriaza O V , Kim D W , Lee D Y , Suhaimi M A . Trade-off analysis between machining time and energy consumption in impeller NC machining. Robotics and Computer-Integrated Manufacturing, 2017, 43: 164–170

[14] Sihag N , Sangwan K S . A systematic literature review on machine tool energy consumption. Journal of Cleaner Production, 2020, 275: 123125

[15] Li B , Tian X T , Zhang M . Modeling and multi-objective optimization method of machine tool energy consumption considering tool wear. International Journal of Precision Engineering and Manufacturing-Green Technology, 2022, 9(1): 127–141

[16] Zhang X W , Yu T B , Dai Y X , Qu S , Zhao J . Energy consumption considering tool wear and optimization of cutting parameters in micro milling process. International Journal of Mechanical Sciences, 2020, 178: 105628

[17] He K Y , Tang R Z , Jin M Z . Pareto fronts of machining parameters for trade-off among energy consumption, cutting force and processing time. International Journal of Production Economics, 2017, 185: 113–127

[18] Kumar R , Bilga P S , Singh S . Multi objective optimization using different methods of assigning weights to energy consumption responses, surface roughness and material removal rate during rough turning operation. Journal of Cleaner Production, 2017, 164: 45–57

[19] Yoon H S , Kim E S , Kim M S , Lee J Y , Lee G B , Ahn S H . Towards greener machine tools—a review on energy saving strategies and technologies. Renewable and Sustainable Energy Reviews, 2015, 48: 870–891

[20] Triebe M J , Zhao F , Sutherland J W . Achieving energy efficient machine tools by mass reduction through multi-objective optimization. Procedia CIRP, 2019, 80: 73–78

[21] Ji Q Q , Li C B , Zhu D G , Jin Y , Lv Y , He J X . Structural design optimization of moving component in CNC machine tool for energy saving. Journal of Cleaner Production, 2020, 246: 118976

[22] Lv J X , Tang R Z , Tang W C J , Liu Y , Zhang Y F , Jia S . An investigation into reducing the spindle acceleration energy consumption of machine tools. Journal of Cleaner Production, 2017, 143: 794–803

[23] Wójcicki J , Bianchi G . Electric load management in spindle run-up and run-down for multi-spindle machine tools via optimal power-torque trajectories and peak load synchronization. The International Journal of Advanced Manufacturing Technology, 2018, 95(5): 1819–1835

[24] Denkena B , Abele E , Brecher C , Dittrich M A , Kara S , Mori M . Energy efficient machine tools. CIRP Annals, 2020, 69(2): 646–667

[25] Liu S , Liu F , Hu S H , Yin Z B . Energy survey of machine tools: separating power information of the main transmission system during machining process. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 2012, 6(4): 445–455

[26] Liu X , Zhao F , Mei X S . A fuzzy adaptive controller for constant cutting torque in high-performance gear hobbing process. In: Proceedings of 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). Munich: IEEE, 2017, 1725–1730

[27] Hall E , Ramamurthy S S , Balda J C . Optimum speed ratio of induction motor drives for electrical vehicle propulsion. In: Proceedings of APEC 2001 the 16th Annual IEEE Applied Power Electronics Conference and Exposition (Cat. NO.01CH37181). Anaheim: IEEE, 2001, 371–377

[28] Afzal A , Kim K Y , Seo J W . Effects of Latin hypercube sampling on surrogate modeling and optimization. International Journal of Fluid Machinery and Systems, 2017, 10(3): 240–253

[29] Li C B , Li Y S , Gao L , Garg A , Li W . Surrogate model-based heat dissipation optimization of air-cooling battery packs involving herringbone fins. International Journal of Energy Research, 2021, 45(6): 8508–8523

[30] Amouzgar K , Strömberg N . Radial basis functions as surrogate models with a priori bias in comparison with a posteriori bias. Structural and Multidisciplinary Optimization, 2017, 55(4): 1453–1469

[31] Kleijnen J P C . Regression and Kriging metamodels with their experimental designs in simulation: a review. European Journal of Operational Research, 2017, 256(1): 1–16

[32] Ni H X , Yan C P , Ni S F , Shu H , Zhang Y . Multi-verse optimizer based parameters decision with considering tool life in dry hobbing process. Advances in Manufacturing, 2021, 9(2): 216–234

[33] Paul D , Jain A , Saha S , Mathew J . Multi-objective PSO based online feature selection for multi-label classification. Knowledge-Based Systems, 2021, 222: 106966

[34] Oliveira B , Ballio F , Maia R . Numerical modelling-based sensitivity analysis of fluvial morphodynamics. Environmental Modelling & Software, 2021, 135: 104903

[35] Xu C , Zhu P , Liu Z , Tao W . Mapping-based hierarchical sensitivity analysis for multilevel systems with multidimensional correlations. Journal of Mechanical Design, 2021, 143(1): 011707

RIGHTS & PERMISSIONS

Higher Education Press