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Inverse input prediction model for robotic belt grinding

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Abstract

Robotic abrasive belt grinding can be widely used to improve the surface quality of complex workpieces. Due to the elastic characteristics in the grinding process, feasible processing parameters cannot be fully predicted by existing cutting models given a desired cutting depth. Thus, abrasive belt grinding in industrial production still relies mainly on operator experience and intelligence. Robust theoretical guidance is provided for input parameters setting of robotic belt grinding. A new method called Inverse Input Prediction Model (IIPM) is proposed. IIPM has two processes to calculate the optimal input parameters for robotic abrasive belt grinding. First, the forward process is to establish the correspondence between known processing parameters and the unknown grinding depth. Support Vectors Regression (ε-SVR) is an application of support vector machine (SVM) to regress process parameters. The forward process use ε-SVR method to construct a nonlinear regression of grinding parameters and grinding depth. Second, the inverse process of dynamic grid search (DGS) method is proposed to predict the optimal processing parameters according to the required grinding depth. The experimental results in the forward process demonstrate that the ε-SVR model has the lowest prediction error compared with BP neural network and polynomial regression. DGS and particle swarm optimization (PSO) search methods are used in the inverse process to predict grinding parameters at the required grinding depth. Experiment results of the inverse model prediction show that the predicted depth is very closely fit the actual grinding depth. The proposed method can be used to find the stable and reliable sequence of continuous path grinding parameters by given the desired grinding depth.

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References

  • Chen, F., Zhao, H., Li, D., et al.: Robotic grinding of a blisk with two degrees of freedom contact force control. Int. J. Adv. Manuf. Technol. 101, 461–474 (2019)

    Article  Google Scholar 

  • Cheng, C., Li, J., Liu, Y., et al.: Deep convolutional neural network-based in-process tool condition monitoring in abrasive belt grinding. Comput. Ind. 106, 1–13 (2019)

    Article  Google Scholar 

  • Domroes F., Rieger M. and Kuhlenkoetter B.: Towards autonomous robot machining. In: 41st International Symposium on Robotics, January 2014, paper no. 267777401, pp. 448–453. Conference ISR ROBOTIK 2014

  • Endo, H., Marui, E.: Deformation simulation of ground materials by coated abrasives (wear test). Ind. Lubrication Tribol. 59, 172–177 (2007)

    Article  Google Scholar 

  • Finn C., Levine S.: Deep visual foresight for planning robot motion. 13 Mar 2017. arXiv:1610.00696

  • García, Á., Anjos, O., Iglesias, C., et al.: Prediction of mechanical strength of cork under compression using machine learning techniques. Mater. Des. 82, 304–311 (2015)

    Article  Google Scholar 

  • Gu S., Holly E., Lillicrap T., et al: Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates. 23 Nov 2016. arXiv:1610.00633, 2016

  • Klocke, F., Soo, S., Karpuschewski, B., et al.: Abrasive machining of advanced aerospace alloys and composites. CIRP Ann. Manuf. Technol. 64, 581–604 (2015)

    Article  Google Scholar 

  • Liang W., Song Y., Lv H., et al A novel control method for robotic belt grinding based on SVM and PSO method. In: International Conference on Intelligent Computation Technology and Automation, pp. 258–261. 2010 International Conference on Intelligent Computation Technology and Automation.

  • Lv H., Song Y., Jia P., et al: An adaptive modeling approach based on ESN for robotic belt grinding. In: IEEE International Conference on Information and Automation, Harbin, China, 20–23 June 2010, paper no. 5512461, pp.787–792. Proceedings of the 2010 IEEE

  • Pandiyan, V., Caesarendra, W., Tjahjowidodo, T.: Predictive modelling and analysis of process parameters on material removal characteristics in abrasive belt grinding process. Appl. Sci. 7, 363–366 (2017)

    Article  Google Scholar 

  • Pandiyan, V., Murugan, P., Tjahjowidodo, T., et al.: In-process virtual verification of weld seam removal in robotic abrasive belt grinding process using deep learning. Robot. Comput.-Integrated Manuf. 57, 477–487 (2019)

    Article  Google Scholar 

  • Park, J., Chen, Z., Kiliaris, L., et al.: Intelligent vehicle power control based on machine learning of optimal control parameters and prediction of road type and traffic congestion. IEEE Trans. Veh. Technol. 58, 4741–4756 (2009)

    Article  Google Scholar 

  • Platt, J. Sequential minimal optimization: a fast algorithm for training support vector machines. Advances in Kernel Methods-Support Vector Learning. 208 (1998)

  • Qi, J., Chen, B., Zhang, D., et al.: Multi-information fusion-based belt condition monitoring in grinding process using the improved-Mahalanobis distance and convolutional neural networks. J. Manuf. Process. 59, 302–315 (2020)

    Article  Google Scholar 

  • Ren, X.: Kuhlernkotter B: Real-time simulation and visualization of robotic belt grinding processes. Int. J. Adv. Manuf. Technol. 35, 1090–1099 (2008)

    Article  Google Scholar 

  • Ren, X., Cabaravdic, M., Zhang, X., et al.: A local process model for simulation of robotic belt grinding. Int. J. Mach. Tools Manuf 47, 962–970 (2007)

    Article  Google Scholar 

  • Song, Y., Yang, H., Lv, H.: Intelligent control for a robot belt grinding system. IEEE Trans. Control Syst. Technol. 21, 716–724 (2013)

    Article  Google Scholar 

  • Tahvilian, M., Liu, Z., Champliaud, H.: Experimental and Finite element analysis of Temperature and Energy Partition to the Workpiece During Grinding Process with Flexible Robot. J. Material Process. Technol. 213, 2292–2303 (2013)

    Article  Google Scholar 

  • Vapnik, V.N.: Statistical learning theory. Encyclopedia Sci Learn. 41, 3185–3185 (2012)

    Google Scholar 

  • Wang, Y.J., Huang, Y., Chen, Y.X., et al.: Model of an abrasive belt grinding surface removal contour and its application. Int. J. Adv. Manuf. Technol. 82, 2113–2122 (2016)

    Article  Google Scholar 

  • Wang, W., Liu, F., Liu, Z., et al.: Prediction of depth of cut for robotic belt grinding. Int. J. Adv. Manuf. Technol. 91, 699–708 (2017)

    Article  Google Scholar 

  • Weinert, K., Blum, H., Kuhlenkötter, B., et al.: New methods for calculating the force distribution within belt grinding processes. Prod. Eng. Res. Devel. 1, 285–289 (2007)

    Article  Google Scholar 

  • Wu, S., Kazem, K., Gan, Z., et al.: A material removal model for robotic belt grinding process. Mach. Sci. Technol. 18, 15–30 (2014)

    Article  Google Scholar 

  • Yang, Z., Chu, Y., Xu, X., et al.: Prediction and analysis of material removal characteristics for robotic belt grinding based on single spherical abrasive grain model. Int. J. Mech. Sci. 190, 106005 (2021)

    Article  Google Scholar 

  • Zhao, H., Zhu, L., Jia, Y., et al.: Focused section on advanced robotic systems for industrial automation. Int. J. Intell. Robot. Appl. (2020). https://doi.org/10.1007/s41315-020-00139-y

    Article  Google Scholar 

  • Zhu, D., Luo, S., Yang, L., et al.: On energetic assessment of cutting mechanisms in robot-assisted belt grinding of titanium alloys. Tribol. Int. 90, 55–59 (2015)

    Article  Google Scholar 

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Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant No. 52075533, 62003346).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Tsinghua University, Beijing, China

    Yi Yue

  2. Beijing Spacecrafts, China Academy of Space Technology, Beijing, China

    Yi Yue, Jiabo Zhang, Yinhao Zhou, Ke Wen, Jizhi Yang, Qintao Chen & Xiaopeng Bai

Authors
  1. Yi Yue
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  2. Jiabo Zhang
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  3. Yinhao Zhou
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  4. Ke Wen
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  5. Jizhi Yang
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  6. Qintao Chen
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  7. Xiaopeng Bai
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Contributions

Yi Yue: completed the main work of this manuscript and most of the data analysis work. Jiabo Zhang: he provided necessary experimental equipment and site, and participated in the formulation of the overall scheme of this manuscript. Finally, He wrote a part of this manuscript. Yinhao Zhou: she participated in the design and improvement of the ε-SVR model. Wen Ke: he completed the theoretical analysis of parameter selection, RKHS interpretation and invertibility of function. Jizhi Yang: he was the designer of DGS algorithm and assisted in method programming. Qintao Chen: he is the main person of algorithm programming, and has completed the MATLAB programming of PSO and DGS. He also completed algorithm comparison of BP, ε-SVR and polynomial. Xiaopeng Bai: he completed revision of grammatical errors and new experimental data processing.

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Correspondence to Yi Yue.

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This manuscript does not involve code, The program is compiled by MathWorks® MATLAB Toolbox.

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Yue, Y., Zhang, J., Zhou, Y. et al. Inverse input prediction model for robotic belt grinding. Int J Intell Robot Appl 5, 465–476 (2021). https://doi.org/10.1007/s41315-021-00165-4

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