A method for grinding removal control of a robot belt grinding system
- 597 Downloads
As a kind of manufacturing system with a flexible grinder, the material removal of a robot belt grinding system is related to a variety of factors, such as workpiece shape, contact force, robot velocity, and belt wear. Some factors of the grinding process are time-variant. Therefore, it is a challenge to control grinding removal precisely for free-formed surfaces. To develop a high-quality robot grinding system, an off-line planning method for the control parameters of the grinding robot based on an adaptive modeling method is proposed in this paper. First, we built an adaptive model based on statistic machine learning. By transferring the old samples into the new samples space formed by the in-situ measurement data, the adaptive model can track the dynamic working conditions more rapidly. Based on the adaptive model the robot control parameters are calculated using the cooperative particle swarm optimization in this paper. The optimization method aims to smoothen the trajectories of the control parameters of the robot and shorten the response time in the transition process. The results of the blade grinding experiments demonstrate that this approach can control the material removal of the grinding system effectively.
KeywordsRobot belt grinding Adaptive modeling Support vector regression Cooperative particle swarm optimization Trajectory optimization
Unable to display preview. Download preview PDF.
- Caydas, U., & Ekici, S. (2010). Support vector machines models for surface roughness prediction in CNC turning of AISI 304 austenitic stainless steel. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-010-0415-2
- Chen, X., Gong, Z., & Huang, H. (2000). Development of robotic system for 3D profile grinding and polishing. SIMTech Technical Report: AT/00/012/AMP.Google Scholar
- Cortes C., Vapnik V. (1995) Support vector network. Machine Learning 20: 273–297Google Scholar
- Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. Proceedings of the IEEE International Conference on Neural Networks, 1942–1948.Google Scholar
- Sedighi, M., & Afshari, D. (2010). Creep feed grinding optimization by an integrated GA-NN system. Journal of Intelligent Manufacturing. doi: 10.1007/s10845-009-0243-4
- Sun, Y. (2004). Development of a unified flexible grinding process. Doctoral Dissertation, The University of Connecticut.Google Scholar
- Takahashi, J., & Sagayama, S. (1995). Vector-field-smoothed Bayesian learning for incremental speaker adaptation. In Proceedings of the IEEE internaltional conference on acoustics, speech, and signal processing (pp. 696–699).Google Scholar
- Vapnik V. (1998) Statistical leaning theory. Springer, New YorkGoogle Scholar
- Zhang X., Cabaravdic M., Kneupner K., Kuhlenkoetter B. (2004) Real-time simulation of robot controlled belt grinding processes of sculptured surfaces. International Journal of Advanced Robotic Systems 1: 109–114Google Scholar
- Zhao, Y., Zhao, J., Zhang, L., & Qi, L. Z. (2008). Development of a robotic 3D scanning system for reverse engineering of freeform part. In Proceedings of the international conference on advanced computer theory and engineering (pp. 246–250).Google Scholar