A variable precision rough set based modeling method for pulsed GTAW
A variable precision rough set based modeling method for pulsed GTAW
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DOI：
10.1007/s00170-006-0922-7
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发表时间：
2008-04
期刊：
影响因子：
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通讯作者：
Wenhang Li;Sun Chen;Bo Wang
中科院分区：
文献类型：
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作者：
Wenhang Li;Sun Chen;Bo Wang
建模是弧焊过程质量控制和成形控制的重要环节。目前的建模方法在弧焊领域取得了很大的进步，但都有一定的局限性。正是基于这些局限性，我们提出了基于变精度粗糙集的建模方法。由于其能够考虑焊接介质的特性，VPRS建模已被证明是一种更有效和更可靠的弧焊过程建模方法。利用该方法建立了脉冲钨极气体保护焊(GTAW)的动态预测模型。结果表明，VPRS建模方法能够在焊接实践中充分获取知识。此外，与经典粗糙集模型和BP神经网络模型进行了比较，结果表明，VPRS模型比经典RS模型更稳定，对未知数据的预测能力更强。此外，VPRS模型具有与神经网络模型相近的精度，但具有更好的可理解性。
Modeling is an important step both for quality and shaping control of the arc welding process. Current modeling methods have made great advances in the field of arc welding, however they all posses certain limitations. It is due to these limitations that we created the variable precision rough set (VPRS) based modeling method. The VPRS modeling has been shown to be both a more efficient and reliable modeling method for the arc welding process due to its ability to account for the character of the welding media. The method was used to produce a dynamic predictive model for pulsed gas tungsten arc welding (GTAW). Results showed that the VPRS modeling method was able to sufficiently acquire knowledge during welding practices. In addition, comparison of VPRS model with classic rough set model and BP neural network model showed that VPRS model was more stable and could predict the unseen data better than classic RS model. Moreover, the VPRS model owns similar precision with neural network model, but has better understandability.