Robot Weld Trajectory Planning And Dynamic Weld Pool Control Method Based On Welder’s Intelligence

With the development of industrial production mode in China,welding technology is focusing more on flexibility,refinement and intellectualization.Welding robots are also increasingly used in aerospace,shipbuilding,railway transportation,automobile production and other industries.However,existing welding robots are lack of intelligence and adaptability similar to welder,while manually and off-line programming procedures are required.Under some conditions,robots fail to work because they can not perceive the online welding process like human welder.From the perspective of welder’s intelligence,online welding is studied in this paper to build welder-like robots.Based on the scientific research results of welding process and biological characteristics,our paper has theoretically analyzed welder’s intelligence.A continuous dynamic control model is proposed for the application of welding robot.To avoid short-term optimal decision,a reward function is defined from the global level.The selection method for multiple parameters is built and the driving mode for parameter sequence is defined.The complex identification,decision and behavior process of a welder is simplified to a sequential decision process(WI-SDP)model.Also,the characteristics of stability,periodicity and global are proposed.This model is used to make a robot perform control based on dynamic weld pool similar to a senior welder.The trajectory of welding robot is an important part of WI-SDP model.The welding speed and applied position determined by the trajectory are important welding parameters which directly affect the quality of the weld bead.To solve the motion design problem in the WI-SDP model,considering the stable movement of a welding robot,a solution based on cubic spline interpolation is proposed,in which multi-point smooth welding path in the joint space is generated.To achieve minimum time goal under robot motion constraints,an optimization model is designed.An HGA method made of two-stage-multi-step search is proposed to provide efficient and accurate solution,which outperforms poor convergence rate and poor precision of traditional GA method.This model ensures smooth,stable and efficient motion of a welding robot in WI-SDP model.The near optimal welding action of a robot is designed from the viewpoint of mechanical motion theory.Online observation,identification and analysis of dynamic weld pool is the basis for a welder to adjust the welding parameters,which is also a prerequisite for WI-SDP modeling process.To deal with the problem of strong arc interference in the monitoring process for weld pool,a signal preprocessing method for monitoring signal is proposed,by which the identification process in WI-SDP is optimized.Based on a GTAW weld pool measurement experiment,a filter processing method is designed to improve the quality of dynamic pool characteristic signals and welding parameter signals.The original characteristic signals are denoised first and then smoothed by Kalman filters,while the welding parameter signals are directly smoothed by Kalman filtering.The smoother can improve quality of the signals,which is a precondition for the subsequent WI-SDP modeling.The formation of adequate weld knowledge is through years of learning and training for a welder,which is also the basis for WI-SDP model.To build a precision welding model,a dynamic weld pool modeling method based on Gaussian Process Regression is proposed.One advantage for this method is that a well-trained model can be applied to the new dynamic welding scene by memory and analysis,which aims to enable a robot to form a knowledge base similar to a welder.Our current modeling accuracy results can reach more than 95%.In the test experiment,the ratio of error rate below 5%has also reached 95%.The formation of high precision model is also the precondition of action mode selection in WI-SDP models.Prediction of welding parameters and real-time control of weld pool are the core part of WI-SDP model.In this paper,a WI-SDP action prediction method based on GPR model and Bayesian Optimization Algorithm(BOA).After the welding training,the robot can form experience and memory,predict the SDP action sequence based on the memory,and generate welding parameters form global view by continuous prediction.The advantage of the BOA method is that it takes account of the confidence interval of the estimated value so that it can predict more reliable parameters.Experiments are designed in which the flow of weld pool is controlled continuously.The effectiveness and practicability of the WI-SDP model are verified through the experiments.Our paper has studied the adaptability and intelligent application technology of welding robot,which has a certain meaning in practical applications.