Quenched and Tempered(Q&T) steels are widely used in the construction of military vehicles due to its high strength to weight ratio and high hardness. These steels are prone to hydrogen induced cracking(HIC) in th...Quenched and Tempered(Q&T) steels are widely used in the construction of military vehicles due to its high strength to weight ratio and high hardness. These steels are prone to hydrogen induced cracking(HIC) in the heat affected zone(HAZ) after welding. The use of austenitic stainless steel(ASS) consumables to weld the above steel was the only available remedy because of higher solubility for hydrogen in austenitic phase. The use of stainless steel consumables for a non-stainless steel base metal is not economical. Hence, alternate consumables for welding Q&T steels and their vulnerability to HIC need to be explored. Recent studies proved that low hydrogen ferritic steel(LHF) consumables can be used to weld Q&T steels, which can give very low hydrogen levels in the weld deposits. The use of ASS and LHF consumables will lead to distinct microstructures in their respective welds. This microstructural heterogeneity will have a drastic influence in the fatigue crack growth resistance of armour grade Q&T steel welds. Hence, in this investigation an attempt has been made to study the influence of welding consumables and welding processes on fatigue crack growth behaviour of armour grade Q&T Steel joints. Shielded metal arc welding(SMAW) and Flux cored arc welding(FCAW) were used for fabrication of joints using ASS and LHF consumables. The joints fabricated by SMAW process using LHF consumable exhibited superior fatigue crack growth resistance than all other joints.展开更多
隐蔽火区地表碳通量监测对评估其温室效应及火区范围圈定十分重要。针对碳通量影响因素多、难预测等问题,提出了一种基于灰狼优化算法(Grey Wolf Optimization,GWO)-改进高斯过程回归(Gaussian Process Regression,GPR)-核密度估计(Kern...隐蔽火区地表碳通量监测对评估其温室效应及火区范围圈定十分重要。针对碳通量影响因素多、难预测等问题,提出了一种基于灰狼优化算法(Grey Wolf Optimization,GWO)-改进高斯过程回归(Gaussian Process Regression,GPR)-核密度估计(Kernel Density Estimation,KDE)预测模型。采用格拉布斯检验法剔除异常值,运用GWO优化GPR算法中的超参数,以提高预测精度。使用验证集预测误差并进行KDE建模,得到碳通量的区间预测值,进而针对组合模型的泛化能力及参数敏感性分析进行评估。结果显示:GWO-GPR-KDE模型的平均绝对误差、均方根误差、决定系数、80%置信区间宽度和95%置信区间宽度分别为0.95386、1.2663、0.92656、0.387和0.823,这些评估指标均优于随机森林(Random Forest,RF)、反向传播神经网络(Back Propagation Neural Network,BPNN)、结合多层感知器的支持向量机(Multilayer Perceptron-Support Vector Machine,MLP-SVM)、高斯过程回归(Gaussian Process Regression,GPR)等经典模型。GWO-GPR-KDE模型对隐蔽火区地表碳通量预测具有较好的准确性和泛化性,为煤田火区防控和温室效应评估提供了新思路。展开更多
基金New Delhi for funding this project work(Project No MAA/03/41)
文摘Quenched and Tempered(Q&T) steels are widely used in the construction of military vehicles due to its high strength to weight ratio and high hardness. These steels are prone to hydrogen induced cracking(HIC) in the heat affected zone(HAZ) after welding. The use of austenitic stainless steel(ASS) consumables to weld the above steel was the only available remedy because of higher solubility for hydrogen in austenitic phase. The use of stainless steel consumables for a non-stainless steel base metal is not economical. Hence, alternate consumables for welding Q&T steels and their vulnerability to HIC need to be explored. Recent studies proved that low hydrogen ferritic steel(LHF) consumables can be used to weld Q&T steels, which can give very low hydrogen levels in the weld deposits. The use of ASS and LHF consumables will lead to distinct microstructures in their respective welds. This microstructural heterogeneity will have a drastic influence in the fatigue crack growth resistance of armour grade Q&T steel welds. Hence, in this investigation an attempt has been made to study the influence of welding consumables and welding processes on fatigue crack growth behaviour of armour grade Q&T Steel joints. Shielded metal arc welding(SMAW) and Flux cored arc welding(FCAW) were used for fabrication of joints using ASS and LHF consumables. The joints fabricated by SMAW process using LHF consumable exhibited superior fatigue crack growth resistance than all other joints.
文摘隐蔽火区地表碳通量监测对评估其温室效应及火区范围圈定十分重要。针对碳通量影响因素多、难预测等问题,提出了一种基于灰狼优化算法(Grey Wolf Optimization,GWO)-改进高斯过程回归(Gaussian Process Regression,GPR)-核密度估计(Kernel Density Estimation,KDE)预测模型。采用格拉布斯检验法剔除异常值,运用GWO优化GPR算法中的超参数,以提高预测精度。使用验证集预测误差并进行KDE建模,得到碳通量的区间预测值,进而针对组合模型的泛化能力及参数敏感性分析进行评估。结果显示:GWO-GPR-KDE模型的平均绝对误差、均方根误差、决定系数、80%置信区间宽度和95%置信区间宽度分别为0.95386、1.2663、0.92656、0.387和0.823,这些评估指标均优于随机森林(Random Forest,RF)、反向传播神经网络(Back Propagation Neural Network,BPNN)、结合多层感知器的支持向量机(Multilayer Perceptron-Support Vector Machine,MLP-SVM)、高斯过程回归(Gaussian Process Regression,GPR)等经典模型。GWO-GPR-KDE模型对隐蔽火区地表碳通量预测具有较好的准确性和泛化性,为煤田火区防控和温室效应评估提供了新思路。
