随着国内冶金、机械等领域的不断发展,对钢铁材料性能的要求越来越严格,汽车用钢不仅要求减重而且要有足够高的抗冲击性能保证汽车安全性,耐磨材料不仅要保证硬度还要有良好的韧性。合金钢淬火-配分(quenching and partitioning,简称Q&a...随着国内冶金、机械等领域的不断发展,对钢铁材料性能的要求越来越严格,汽车用钢不仅要求减重而且要有足够高的抗冲击性能保证汽车安全性,耐磨材料不仅要保证硬度还要有良好的韧性。合金钢淬火-配分(quenching and partitioning,简称Q&P)工艺是由美国Speer J G教授在2003年受Trip钢启发提出的,最终目的是在硬相基体上获得一定量的软相残余奥氏体,从而提高钢的塑韧性,马氏体、贝氏体保证了强度,残余奥氏体可以提高韧性,两相结合拥有良好的综合力学性能。和传统工艺淬火-回火(QT)抑制碳化物析出不同,钢中的碳没有分解为碳化物,而是在保温过程中重新扩散至奥氏体,提高了奥氏体的稳定性。Q&P钢具有成本低、性能好、工艺相对简单等特点,最初应用到汽车高强钢上,可以很大程度减轻汽车的质量、提高防撞能力、减小变形程度;陆续有研究者将Q&P工艺应用到耐磨材料上,发现可以在耐磨性损失很小或者不损失的情况下大幅度提高韧性。为了进一步提高性能,国内外许多学者做了大量的研究,在Q&P热处理工艺优化方面,发现奥氏体化温度、淬火工艺、配分工艺等参数对Q&P钢组织和性能都有较大影响;在合金元素调控方面,不仅C、Mn、Si等常规合金元素对Q&P钢的性能有重要的影响,Nb、Mo等微合金元素也对Q&P钢组织和性能有较大的影响。主要阐述了Q&P工艺的发展、国内外的研究现状以及Q&P工艺的应用,最后对Q&P工艺未来的发展进行了展望和总结。展开更多
通过研究在Q&P(quenching and partitioning,淬火-配分)工艺下3种不同Si含量Q&P钢的组织与性能,并与常规调质工艺进行了对比。利用XRD计算了不同Q&P工艺下钢中残留奥氏体含量,表明添加适量的Si以及利用Q&P工艺能保留较...通过研究在Q&P(quenching and partitioning,淬火-配分)工艺下3种不同Si含量Q&P钢的组织与性能,并与常规调质工艺进行了对比。利用XRD计算了不同Q&P工艺下钢中残留奥氏体含量,表明添加适量的Si以及利用Q&P工艺能保留较多的奥氏体至室温。配分的温度与时间对力学性能有着显著地影响。在320℃至460℃,随着配分温度的升高,强度逐渐降低,伸长率逐渐升高;在380℃的配分温度下,随着分配时间的延长,强度略有降低,伸长率有显著提高。展开更多
The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(V_(RA))is challengeable to directly pr...The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(V_(RA))is challengeable to directly predict due to the complicated relationships between the chemical composition and process(like quenching temperature(Qr)).A Gaussian process regression model in machine learning was developed to predict V_(RA),and the model accuracy was further improved by introducing a metallurgical parameter of martensite fraction(fo)to accurately predict V_(RA) in Q&P steels.The developed machine learning model combined with Bayesian global optimization can serve as another selection strategy for the quenching temperature,and this strategy is very effcient as it found the"optimum"Qr with the maximum V_(RA) using only seven consecutive iterations.The benchmark experiment also reveals that the developed machine learning model predicts V_(RA) more accurately than the popular constrained carbon equilibrium thermodynamic model,even better than a thermo-kinetic quenching-partitioning-tempering-local equilibrium model.展开更多
文摘随着国内冶金、机械等领域的不断发展,对钢铁材料性能的要求越来越严格,汽车用钢不仅要求减重而且要有足够高的抗冲击性能保证汽车安全性,耐磨材料不仅要保证硬度还要有良好的韧性。合金钢淬火-配分(quenching and partitioning,简称Q&P)工艺是由美国Speer J G教授在2003年受Trip钢启发提出的,最终目的是在硬相基体上获得一定量的软相残余奥氏体,从而提高钢的塑韧性,马氏体、贝氏体保证了强度,残余奥氏体可以提高韧性,两相结合拥有良好的综合力学性能。和传统工艺淬火-回火(QT)抑制碳化物析出不同,钢中的碳没有分解为碳化物,而是在保温过程中重新扩散至奥氏体,提高了奥氏体的稳定性。Q&P钢具有成本低、性能好、工艺相对简单等特点,最初应用到汽车高强钢上,可以很大程度减轻汽车的质量、提高防撞能力、减小变形程度;陆续有研究者将Q&P工艺应用到耐磨材料上,发现可以在耐磨性损失很小或者不损失的情况下大幅度提高韧性。为了进一步提高性能,国内外许多学者做了大量的研究,在Q&P热处理工艺优化方面,发现奥氏体化温度、淬火工艺、配分工艺等参数对Q&P钢组织和性能都有较大影响;在合金元素调控方面,不仅C、Mn、Si等常规合金元素对Q&P钢的性能有重要的影响,Nb、Mo等微合金元素也对Q&P钢组织和性能有较大的影响。主要阐述了Q&P工艺的发展、国内外的研究现状以及Q&P工艺的应用,最后对Q&P工艺未来的发展进行了展望和总结。
文摘通过研究在Q&P(quenching and partitioning,淬火-配分)工艺下3种不同Si含量Q&P钢的组织与性能,并与常规调质工艺进行了对比。利用XRD计算了不同Q&P工艺下钢中残留奥氏体含量,表明添加适量的Si以及利用Q&P工艺能保留较多的奥氏体至室温。配分的温度与时间对力学性能有着显著地影响。在320℃至460℃,随着配分温度的升高,强度逐渐降低,伸长率逐渐升高;在380℃的配分温度下,随着分配时间的延长,强度略有降低,伸长率有显著提高。
基金The authors acknowledge financial support from the National Natural Science Foundation of China(Grant Nos.51771114 and 51371117).
文摘The metastable retained austenite(RA)plays a significant role in the excellent mechanical performance of quenching and partitioning(Q&P)steels,while the volume fraction of RA(V_(RA))is challengeable to directly predict due to the complicated relationships between the chemical composition and process(like quenching temperature(Qr)).A Gaussian process regression model in machine learning was developed to predict V_(RA),and the model accuracy was further improved by introducing a metallurgical parameter of martensite fraction(fo)to accurately predict V_(RA) in Q&P steels.The developed machine learning model combined with Bayesian global optimization can serve as another selection strategy for the quenching temperature,and this strategy is very effcient as it found the"optimum"Qr with the maximum V_(RA) using only seven consecutive iterations.The benchmark experiment also reveals that the developed machine learning model predicts V_(RA) more accurately than the popular constrained carbon equilibrium thermodynamic model,even better than a thermo-kinetic quenching-partitioning-tempering-local equilibrium model.
