Research On Intelligent Method For Robotic Profile Belt Grinding

The robotic profile belt grinding (RPBG) system is often employed for machiningcomplex surface whose removal rates need to be controlled accurately. Because theabrasive belt and the contact wheel are flexible, and the industrial robot is not rigidenough, RPBG is non-rigid and its removal rate is affected by a large amount of factors.So far, empirical formulas are applied to calculate the removal rate in the RPBGsystems mostly. However, they are low accuracy and lack of adaptability. Artificialintelligence is effective for solving this problem. The central topic of this thesis is tocontrol the accuracy of the RPBG system using artificial intelligence technology. Itincludes the method framework, the methods of adaptive modeling and how to put theminto use. The contributions of this dissertation consist of the following parts:1. To solve the problem of adapting to dynamic factors, an intelligent methodframework is presented. The keys of the framework include the knowledge database, in-situ measurement, adaptive modeling and parameters' selection. The in-situmeasurement gets some new samples for the knowledge database in each grinding. So itis possible for the model to adapt the dynamic factors.2. In order to solve the adaptive problem, methods based on support vectorregression and echo state network are firstly applied to model the removal rate on quasi-steady state which means meeting the i.i.d. condition approximatively in this thesis.And the accuracies of these two methods meet the need. Basing on the quasi-steadylearners, samples in the database can be organized and the adaptive learning problemcan be formulized.3. An adaptive modeling method based on transfer learning is proposed for theRPBG system. This method uses some new samples to measure the difference betweenthe old learner and the new target function. With same monotonicity, the most similarold learner is transferred to predict the target points. The normalized Euclidean distancefor removal rate is important for calculating the weights. When the new samplesdistribute evenly in the input space, this method is effective.4. Another adaptive method is put forward by incorporating prior knowledge inmachine learning by creating virtual examples based on semi-empirical formula. The method can be applied for adaptive modeling when the new samples in input space areunevenly. In principle, the virtual samples make the model stable and the machinelearning algorithm improve the accurate.5. It is a key step to choose the grinding parameters in the intelligent framework,which is also the purpose of the removal model. And there are more than one variablecan be adjusted online, so the grinding parameters can be optimized. The objectfunction is presented in order to reduce impact of the system response speed on thegrinding accuracy. The particle swarm optimization algorithm is applied to gain theoptimal parameters.