摘要:
介绍了一种针对短路过渡的C02焊接过程的电流波形在线自适应控制系统。它通过以短路过渡频率和电弧声能量,作为表征和传感焊接过程稳定性和飞溅的参数,建立了焊接规范和波形控制参数的神经网络优化模型,采用神经网络自适应调整PID参数的双闭环波形控制算法,实现对焊接过程的在线自优化实时控制。试验结果表明,采用该控制系统的焊机,实现了焊接规范及波形控制参数的自动匹配和优化,同时降低了焊接过程飞溅,提高了熔滴过渡的稳定性。
Abstract:
Aiming at CO2 arc welding process of short-circuit trans-fer,an on-line adaptive control system of welding current waveform is introduced.In this system,a neural networks model is set up for optimizing welding parameters and waveform control parameters,by which shot-cir-cuiting frequency and arc sound energy that indirectly indicates welding process stability and spatter number are made use of characteristic parameters of network import.At the same time,a double-closed circle wave formeontrnl algorithm is put forward,which abstains PID parameters by means of a neural network self-adjusted model.Experimental result shows,this control system is available to matching and optimizing welding parameter sand waveform contrnl parameters,and that,it can remarkably make the welding spatter decreased and metal transfer stability improved.
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