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Experimental Investigation of Laser Surface Transformation Hardening of 4340 Steel Spur Gears

机译:4340钢正齿轮激光表面转变硬化的实验研究。

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This paper presents an experimental investigation of laser surface transformation hardening (LSTH) of 4340 steel spur gears using regression analysis. The experimental work is focused on the effects of various LSTH parameters on the hardness profile shape and the hardened depth variation. The investigations are based on a structured design of experiments and improved statistical analysis tools. The experimentations are carried out on AISI 4340 steel spur gears using a commercial 3 kW Nd:YAG laser system. Laser power, scanning speed, and rotation speed are used as process parameters to evaluate the variation of the hardened depth and to identify the possible relationship between the process parameters and the hardened zone physical and geometrical characteristics. Based on the experimental data and analysis of variance, the direct and interactive contributions of the process parameters on the variation of the hardness profile shape and the hardened depth are analyzed. The main effects and the interaction effects are also evaluated. The results reveal that all the process parameters are relevant. The cumulative contribution of the three parameters in the hardened depth variation represents more than 80% with a clear predominance of laser power. The contribution of the interactions between the parameters represents 12% to 16%. The resulting hardness values are relatively similar for all the experimental tests with about 60 HRC. The evaluation of the produced regression models for hardened depth prediction shows limited performance suggesting that the predictive modeling process can be improved.
机译:本文利用回归分析对4340钢正齿轮进行激光表面相变硬化(LSTH)的实验研究。实验工作集中在各种LSTH参数对硬度轮廓形状和硬化深度变化的影响上。这些调查基于实验的结构设计和改进的统计分析工具。实验是使用商用3 kW Nd:YAG激光系统在AISI 4340钢正齿轮上进行的。激光功率,扫描速度和旋转速度被用作过程参数,以评估硬化深度的变化并确定过程参数与硬化区物理和几何特性之间的可能关系。根据实验数据和方差分析,分析了工艺参数对硬度轮廓形状和硬化深度变化的直接和交互作用。还评估了主要作用和相互作用作用。结果表明所有工艺参数都是相关的。在明显的激光功率优势下,三个参数在硬化深度变化中的累积贡献超过80%。参数之间相互作用的贡献占12%至16%。在约60 HRC的所有实验测试中,所得的硬度值相对相似。所生成的用于深度预测的回归模型的评估显示出有限的性能,这表明可以改进预测建模过程。

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