摘要
机器人焊接过程中熔池实时控制系统是焊接柔性加工单元 (WFMC)中保证良好焊接质量的一个重要子系统。文中建立了WFMC中焊接质量实时控制子系统并实现了该子系统与WFMC中央监控计算机的实时可靠通讯。在获得了焊接熔池特征参数的基础上 ,建立了焊接过程熔池正面参数和焊缝背面参数的神经网络模型。模型根据熔池正面参数可实时预测焊缝背面宽度。并设计了神经元自学习比例求和微分(PSD)控制器 ,通过调整脉冲峰值电流 ,实现了机器人脉冲钨极气体保护焊 (GTAW )过程中通过正面熔池传感对焊接焊缝背面宽度的实时控制。
Control system of weld quality in real time, which can ensure an excellent weld, is an important sub-system of welding flexible manufacturing cell (WFMC). A sub-system for weld quality control in real time and reliable communication between penetration control sub-system and the center computer of WFMC were investigated in this paper. Based on the measurement of weld pools'characteristics parameters, artificial neural network models for topside and backside of welding pool were established. With the models, backside weld width could be deduced from topside parameters of welding pool in real time. A neuron self-learning proportional summational differential (PSD) controller was designed, which could control backside weld width though sensing topside parameters of welding pool and adjusting welding peak current during robotic pulsed GTA welding. Controlling experiments verified that the controllers were effective.
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2002年第4期1-5,共5页
Transactions of The China Welding Institution
基金
国家自然科学基金重点资助项目 (596351 60 )
