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首页> 外文期刊>Journal of surface engineered materials and advanced technology >A Predictive Modeling Based on Regression and Artificial Neural Network Analysis of Laser Transformation Hardening for Cylindrical Steel Workpieces
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A Predictive Modeling Based on Regression and Artificial Neural Network Analysis of Laser Transformation Hardening for Cylindrical Steel Workpieces

机译：基于回归和人工神经网络分析的圆柱钢工件激光转变硬化的预测建模

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摘要

Laser surface hardening is a very promising hardening process for ferrous alloys where transformations occur during cooling after laser heating in the solid state. The characteristics of the hardened surface depend on the physicochemical properties of the material as well as the heating system parameters. To exploit the benefits presented by the laser hardening process, it is necessary to develop an integrated strategy to control the process parameters in order to produce desired hardened surface attributes without being forced to use the traditional and fastidious trial and error procedures. This study presents a comprehensive modelling approach for predicting the hardened surface physical and geometrical attributes. The laser surface transformation hardening of cylindrical AISI 4340 steel workpieces is modeled using the conventional regression equation method as well as artificial neural network method. The process parameters included in the study are laser power, beam scanning speed, and the workpiece rotational speed. The upper and the lower limits for each parameter are chosen considering the start of the transformation hardening and the maximum hardened zone without surface melting. The resulting models are able to predict the depths representing the maximum hardness zone, the hardness drop zone, and the overheated zone without martensite transformation. Because of its ability to model highly nonlinear problems, the ANN based model presents the best modelling results and can predict the hardness profile with good accuracy.
机译：激光表面硬化是一种非常有希望的硬化工艺，适用于黑色金属，其中固态加热后的冷却过程中会发生转变。硬化表面的特性取决于材料的物理化学特性以及加热系统参数。为了利用激光淬火工艺带来的好处，有必要开发一种综合策略来控制工艺参数，以产生所需的淬火表面属性，而不必强迫使用传统且严格的试验和错误程序。这项研究提出了一种用于预测硬化表面物理和几何属性的综合建模方法。使用常规回归方程方法和人工神经网络方法对圆柱AISI 4340钢工件的激光表面转变硬化进行建模。研究中包括的工艺参数是激光功率，光束扫描速度和工件转速。选择每个参数的上限和下限时要考虑相变硬化的开始以及没有表面熔化的最大硬化区域。生成的模型能够预测代表最大硬度区，硬度下降区和没有马氏体转变的过热区的深度。由于其具有对高度非线性问题进行建模的能力，因此基于ANN的模型可以提供最佳的建模结果，并且可以准确地预测硬度分布。

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