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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >Prediction of the quality of pulsed metal inert gas welding using statistical parameters of arc signals in artificial neural network
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Prediction of the quality of pulsed metal inert gas welding using statistical parameters of arc signals in artificial neural network

机译：基于人工神经网络中电弧信号统计参数的脉冲金属惰性气体保护焊质量预测

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摘要

One of the big challenges in welding is the prediction of weld-quality without destructive test. This work introduces an intelligent system for weld-quality prediction in a pulsed metal inert gas welding process based on the statistical parameters of the acquired current and voltage signals. Six process parameters and 10 statistical parameters of arc signals are used to describe various welding conditions. These process features obtained from a set of experiments are employed as input patterns to back propagation neural network and radial basis function network models to predict the corresponding weld qualities. The prediction errors show that the neural network model, which has been trained with the statistical parameters of arc signals along with the process parameters, gives superior prediction of weld quality as compared to that from a model developed with only the process parameters as its inputs.
机译：焊接中的最大挑战之一是如何在不进行破坏性测试的情况下预测焊接质量。这项工作介绍了一种智能系统，用于基于所采集的电流和电压信号的统计参数，在脉冲金属惰性气体保护焊接过程中预测焊接质量。电弧信号的六个过程参数和十个统计参数用于描述各种焊接条件。从一组实验中获得的这些过程特征用作反向传播神经网络和径向基函数网络模型的输入模式，以预测相应的焊接质量。预测误差表明，与仅以过程参数为输入的模型相比，已用电弧信号的统计参数以及过程参数进行训练的神经网络模型对焊​​接质量具有更好的预测。

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