In this article, laser transformation hardening of HT250 material by high speed axis flow CO2 laser was investigated for first time in China. Appropriate laser hardening parameters, such as laser energy power P(W), la...In this article, laser transformation hardening of HT250 material by high speed axis flow CO2 laser was investigated for first time in China. Appropriate laser hardening parameters, such as laser energy power P(W), laser scanning rate V(m/min), were optimized through a number of experiments. The effect of the mentioned parameters on the hardened zone, including its case depth, microhardness distributions etc., were analyzed. Through the factual experiments, it is proved that axial flow CO2laser, which commonly outputs low mode laser beam, can also treat materials as long as the treating parameters used are rational. During the experiments, the surface qualities of some specimens treated by some parameters were found to be enhanced, which does not coincide with the former results. Furthermore in the article, the abnormal phenomenon observed in the experiments is discussed. According to the experimental results, the relationship between laser power density q and scanning rate V is shown in a curve and the corresponding formulation, which have been proved to be valuable for choosing the parameters of laser transformation hardening by axial flow CO2 lasers, was also given.展开更多
Quality assessment and prediction becomes one of the most critical requirements for improving reliability, efficiency and safety of laser surface transformation hardening process (LSTHP). Accurate and efficient model ...Quality assessment and prediction becomes one of the most critical requirements for improving reliability, efficiency and safety of laser surface transformation hardening process (LSTHP). Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. This paper presents a structured and comprehensive approach developed to design an effective artificial neural network (ANN) based model for quality estimation and prediction in LSTHP using a commercial 3 kW Nd:Yag laser. The proposed approach examines laser hardening parameters and conditions known to have an influence on performance characteristics of hardened surface such as hardened bead width (HBW) and hardened depth (HD) and builds a quality prediction model step by step. The modeling procedure begins by examining, through a structured experimental investigations and exhaustive 3D finite element method simulation efforts, the relationships between laser hardening parameters and characteristics of hardened surface and their sensitivity to the process conditions. Using these results and various statistical tools, different quality prediction models are developed and evaluated. The results demonstrate that the ANN based assessment and prediction proposed approach can effectively lead to a consistent model able to accurately and reliably provide an appropriate prediction of hardened surface characteristics under variable hardening parameters and conditions.展开更多
文摘In this article, laser transformation hardening of HT250 material by high speed axis flow CO2 laser was investigated for first time in China. Appropriate laser hardening parameters, such as laser energy power P(W), laser scanning rate V(m/min), were optimized through a number of experiments. The effect of the mentioned parameters on the hardened zone, including its case depth, microhardness distributions etc., were analyzed. Through the factual experiments, it is proved that axial flow CO2laser, which commonly outputs low mode laser beam, can also treat materials as long as the treating parameters used are rational. During the experiments, the surface qualities of some specimens treated by some parameters were found to be enhanced, which does not coincide with the former results. Furthermore in the article, the abnormal phenomenon observed in the experiments is discussed. According to the experimental results, the relationship between laser power density q and scanning rate V is shown in a curve and the corresponding formulation, which have been proved to be valuable for choosing the parameters of laser transformation hardening by axial flow CO2 lasers, was also given.
文摘Quality assessment and prediction becomes one of the most critical requirements for improving reliability, efficiency and safety of laser surface transformation hardening process (LSTHP). Accurate and efficient model to perform non-destructive quality estimation is an essential part of the assessment. This paper presents a structured and comprehensive approach developed to design an effective artificial neural network (ANN) based model for quality estimation and prediction in LSTHP using a commercial 3 kW Nd:Yag laser. The proposed approach examines laser hardening parameters and conditions known to have an influence on performance characteristics of hardened surface such as hardened bead width (HBW) and hardened depth (HD) and builds a quality prediction model step by step. The modeling procedure begins by examining, through a structured experimental investigations and exhaustive 3D finite element method simulation efforts, the relationships between laser hardening parameters and characteristics of hardened surface and their sensitivity to the process conditions. Using these results and various statistical tools, different quality prediction models are developed and evaluated. The results demonstrate that the ANN based assessment and prediction proposed approach can effectively lead to a consistent model able to accurately and reliably provide an appropriate prediction of hardened surface characteristics under variable hardening parameters and conditions.
