ABSTRACT This paper investigates the applicability of two computational intelligence methods for the plasma cutting process modelling: artificial neural networks and adaptive neuro-fuzzy inference systems. After exploring the possibilities of neural networks learning from the experimental data for the prediction of the plasma cutting parameter, the adaptive neuro-fuzzy inference system approach was used in order to compare the prediction properties of neural networks and adaptive neuro-fuzzy models. These two methods resulted in the kerf width prediction models for different combinations of input process parameters: cutting current, cutting speed and material thickness, whose significance was assessed by multi-criteria analysis. Descriptive statistics and a visual exploration of two sets of neural-network and adaptive neuro-fuzzy modelled data showed good agreement with the experimental results and their trends. This was additionally confirmed by the t-test and Analysis of Variance, which also provided the selection of the most favourable method for plasma cutting modelling. Accordingly, the adaptive neuro-fuzzy inference systems showed superior modelling capabilities over artificial neural networks for this particular problem setting.

中文翻译：

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等离子切割过程的计算智能建模

摘要 本文研究了两种计算智能方法在等离子切割过程建模中的适用性：人工神经网络和自适应神经模糊推理系统。在探索了神经网络从实验数据中学习用于预测等离子切割参数的可能性之后，使用自适应神经模糊推理系统方法来比较神经网络和自适应神经模糊模型的预测特性。这两种方法产生了针对不同输入工艺参数组合的切口宽度预测模型：切割电流、切割速度和材料厚度，其重要性通过多标准分析进行评估。两组神经网络和自适应神经模糊建模数据的描述性统计和可视化探索与实验结果及其趋势非常吻合。t 检验和方差分析进一步证实了这一点，这也为等离子切割建模提供了最有利方法的选择。因此，自适应神经模糊推理系统在此特定问题设置中显示出优于人工神经网络的建模能力。