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

被引:7
|
作者
Borki, Al Khader [1 ]
El Ouafi, Abderrazak [1 ]
Chebak, Ahmed [1 ]
机构
[1] Univ Quebec Rimouski, Dept Engn, PARL Res Team, Rimouski, PQ G5L 3A1, Canada
来源
关键词
laser surface transformation hardening; AISI 4340 alloy steel; spur gears; hardness profile; hardened depth; design of experiments; analysis of variance; ARTIFICIAL NEURAL-NETWORKS; PREDICTION; OPTIMIZATION;
D O I
10.3390/jmmp3030072
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
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.
引用
收藏
页数:16
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