Abstract
The crossbeam plays a vital role in computer numerical controlled milling machines, especially in machines with a gantry structure, as it directly influences the machining precision. In this study, a machine tool crossbeam was designed, and the modal frequency of the crossbeam was analyzed using the finite element model (FEM) analysis. In the improved structure obtained through FEM analysis, the X-type structure of the internal unit of the crossbeam was replaced by an O-type structure. The specific structure dimensions were further optimized using a neural-network algorithm and a nondominated sorting genetic algorithm. Finally, we calculated the effect of each crossbeam dimension on the mass, deformation, and frequency in a sensitivity analysis. After optimizing the crossbeam dimensions with respect to deformation, modal frequency, and mass, the structural characteristics of the original and optimized crossbeams were compared. After optimization, the mass and deformation were reduced by 7.45% and 3.08%, respectively, and the modal frequency was increased by 0.42%. These results confirm that the optimization improved the performance of the crossbeam structure.
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Ahmadi, K. (2017). Finite strip modeling of the varying dynamics of thin-walled pocket structures during machining. The International Journal of Advanced Manufacturing Technology, 89(9–12), 2691–2699.
Li, Y., Daniel, W. J. T., & Meehan, P. A. (2017). Deformation analysis in single-point incremental forming through finite element simulation. The International Journal of Advanced Manufacturing Technology, 88(1–4), 255–267.
Ye, B., Xiao, W., Mao, K., et al. (2017). Hybrid analytic-experimental modeling for machine tool structural dynamics. The International Journal of Advanced Manufacturing Technology, 90(5–8), 1679–1691.
Qu, S., Zhao, J., & Wang, T. (2017). Experimental study and machining parameter optimization in milling thin-walled plates based on NSGA-II. The International Journal of Advanced Manufacturing Technology, 89(5–8), 2399–2409.
Torabi, S. H. R., Alibabaei, S., Barooghi Bonab, B., et al. (2017). Design and optimization of turbine blade preform forging using RSM and NSGA II. Journal of Intelligent Manufacturing, 28(6), 1409–1419.
Zhang, C., Li, W., Jiang, P., et al. (2017). Experimental investigation and multi-objective optimization approach for low-carbon milling operation of aluminum. Proceedings of the Institution of Mechanical Engineers, 231(15), 2753–2772.
Yang, S.-H., Lee, H.-H., & Lee, K.-I. I. (2019). Identification of inherent position-independent geometric errors for three-axis machine tools using a double ballbar with an extension fixture. The International Journal of Advanced Manufacturing Technology, 102(9–12), 2967–2976.
Cai, K., & Wang, D. (2017). Optimizing the design of automotive S-rail using grey relational analysis coupled with grey entropy measurement to improve crashworthiness. Structural and Multidisciplinary Optimization, 56(6), 1539–1553.
Cao, W. D., Yan, C. P., Wu, D. J., & Tuo, J. B. (2017). A novel multi-objective optimization approach of machining parameters with small sample problem in gear hobbing. The International Journal of Advanced Manufacturing Technology, 93(9–12), 4099–4110.
Cui, K., & Qin, X. (2018). Virtual reality research of the dynamic characteristics of soft soil under metro vibration loads based on BP neural networks. Neural Computing and Applications, 29, 1233–1242.
Ma, Y., Tan, J., Wang, D., et al. (2018). Light-weight design method for force performance structure of complex structural part based co-operative optimization. Chinese Journal of Mechanical Engineering, 31(1), 1–9.
Makaremi, Y., Haghighi, A., & Ghafouri, H. R. (2017). Optimization of pump scheduling program in water supply systems using a self-adaptive NSGA-II; A review of theory to real application. Water Resources Management, 31(4), 1283–1304.
Zhou, M., Kong, L., Xie, L., et al. (2017). Design and optimization of non-circular mortar nozzles using finite volume method and Taguchi method. The International Journal of Advanced Manufacturing Technology, 90(9–12), 3543–3553.
Cheng, Q., Zhao, H., Zhao, Y., et al. (2016). Machining accuracy reliability analysis of multi-axis machine tool based on Monte Carlo simulation. The International Journal of Advanced Manufacturing Technology, 84(9–12), 2301–2318.
Ghasemian, E., & Ehyaei, M. A. (2018). Evaluation and optimization of organic Rankine cycle (ORC) with algorithms NSGA-II, MOPSO, and MOEA for eight coolant fluids. International Journal of Energy and Environmental Engineering, 9(1), 39–57.
Guo, S., Jiang, G., & Mei, X. (2017). Investigation of sensitivity analysis and compensation parameter optimization of geometric error for five-axis machine tool. The International Journal of Advanced Manufacturing Technology, 93(9–12), 3229–3243.
Khodaygan, S. (March 2019). An interactive method for computer-aided optimal process tolerance design based on automated decision making. International Journal on Interactive Design and Manufacturing, 13(1), 349–364.
Lin, C. (2012). Simultaneous optimal design of parameters and tolerance of bearing locations for high-speed machine tools using a genetic algorithm and Monte Carlo simulation method. International Journal of Precision Engineering and Manufacturing, 13(11), 1983–1988.
Alami Mchichi, N., & Mayer, J. R. R. (2019). Optimal calibration strategy for a five-axis machine tool accuracy improvement using the D-optimal approach. The International Journal of Advanced Manufacturing Technology, 103(1–4), 251–265.
Sun, L., Ren, M., Hong, H., et al. (2017). Thermal error reduction based on thermodynamics structure optimization method for an ultra-precision machine tool. The International Journal of Advanced Manufacturing Technology, 88(5–8), 1267–1277.
Tian, M., Gong, X., Yin, L., et al. (2017). Multi-objective optimization of injection molding process parameters in two stages for multiple mass characteristics and energy efficiency using Taguchi method and NSGA-II. The International Journal of Advanced Manufacturing Technology, 89(1–4), 241–254.
Khoualdia, T., Hadjadj, A. E., Bouacha, K., et al. (2017). Multi-objective optimization of ANN fault diagnosis model for rotating machinery using grey rational analysis in Taguchi method. The International Journal of Advanced Manufacturing Technology, 89(9–12), 3009–3020.
Ma, C., Zhao, L., Mei, X., Shi, H., et al. (2017). Thermal error compensation of high-speed spindle system based on a modified BP neural network. The International Journal of Advanced Manufacturing Technology, 89(9–12), 3071–3085.
Xie, Y., Tang, W., Zhang, F., et al. (2019). Optimization of variable blank holder force based on a sharing niching RBF neural network and an improved NSGA-II algorithm. International Journal of Precision Engineering and Manufacturing, 20, 285–299.
Wang, J., Niu, W., Ma, Y., et al. (2017). A CAD/CAE-integrated structural design framework for machine tools. The International Journal of Advanced Manufacturing Technology, 91(1–4), 545–568.
Xu, W., & Cao, L. (2019). Optimal maintenance control of machine tools for energy efficient manufacturing. The International Journal of Advanced Manufacturing Technology, 104(9–12), 3303–3311.
Shen, L., Ding, X., Li, T., et al. (2019). Structural dynamic design optimization and experimental verification of a machine tool. The International Journal of Advanced Manufacturing Technology, 104(2), 3773–3786. https://doi.org/10.1007/s00170-019-04049-7.
Liu, S., Du, Y., & Lin, M. (2019). Study on lightweight structural optimization design system for gantry machine tool. Concurrent Engineering, 27(1), 1063293X1983294. https://doi.org/10.1177/1063293x19832940.
Feng, C., & Huang, S. (2020). The analysis of key technologies for sustainable machine tools design. Applied Sciences, 10, 731. https://doi.org/10.3390/app10030731.
Baptista, A. J., Peixoto, D., Ferreira, A. D., Pereira, J. P., et al. (2018). Lean design-for-X methodology: Integrating modular design, structural optimization and ecodesign in a machine tool case study. Procedia CIRP, 69, 722–727.
Yuksel, E., Erturk, A. S., & Budak, E. (2020). A hybrid contact implementation framework for finite element analysis and topology optimization of machine tools. The Journal of Manufacturing Science and Engineering, 142(8), 081001.
Acknowledgements
This work was supported by the Jilin Province Development and Reform Commission, China (Grant Number: 2019C036-3). The authors would also like to thank the anonymous reviewers for their helpful comments.
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Li, ., Li, C., Li, P. et al. Structural Design and Optimization of the Crossbeam of a Computer Numerical Controlled Milling-Machine Tool Using Sensitivity Theory and NSGA-II Algorithm. Int. J. Precis. Eng. Manuf. 22, 287–300 (2021). https://doi.org/10.1007/s12541-020-00435-4
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DOI: https://doi.org/10.1007/s12541-020-00435-4