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首页> 外文期刊>The International Journal of Advanced Manufacturing Technology >Improving precision in the prediction of laser texturing and surface interference of 316L assessed by neural network and adaptive neuro-fuzzy inference models
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Improving precision in the prediction of laser texturing and surface interference of 316L assessed by neural network and adaptive neuro-fuzzy inference models

机译：通过神经网络和自适应神经模糊推理模型预测316L的激光纹理和表面干扰的预测精度

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摘要

Laser-based surface texturing provides highly controlled interference fit between two parts. In this work, artificial intelligence-based models were used to predict the surface properties of laser processed stainless steel 316 samples. Artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict the characteristics of laser surface texturing. The models based on feedforward neural network (FFNN) were developed to examine the effect of the laser process parameters for surface texturing on 316L cylindrical pins. The accuracy of the models was measured by calculating the root mean square error and mean absolute error. The reliability of the ANFIS and FFNN models for the output prediction of the laser surface texturing (LST) system were investigated by using the data measured from experiments based on a 3<^>3 factorial design, with main processing parameters set as laser power, pulse repetition frequency, and percentage of laser spot overlap. The relative assessment of the models was performed by comparing percentage error prediction. Finally, the impact of input data was examined using predicted response surface plots. Results showed that ANFIS prediction was 48% more accurate compared with that provided by the FFNN model.
机译：基于激光的表面纹理提供了两部分之间的高度控制的干扰。在这项工作中，基于人工智能的模型用于预测激光加工不锈钢316样品的表面性质。人工神经网络（ANN）和自适应神经模糊推理系统（ANFIS）用于预测激光表面纹理的特征。开发了基于前馈神经网络（FFNN）的模型，以检查激光工艺参数在316L圆柱形销上纹理化的效果。通过计算均方根误差和平均绝对误差来测量模型的准确性。通过使用基于3 <^> 3因子设计的实验测量的数据来研究用于激光表面纹理（LST）系统的输出预​​测的ANFI和FFNN模型的可靠性，主处理参数设置为激光功率，脉冲重复频率，激光光斑重叠的百分比。通过比较百分比误差预测来执行模型的相对评估。最后，使用预测的响应表面图检查输入数据的影响。结果表明，与FFNN模型提供的，ANFIS预测更准确地为48％。

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