This study investigated enhancing the wear resistance of Ti6Al4V alloys for medical applications by incorporating Ti C nanoreinforcements using advanced spark plasma sintering(SPS). The addition of up to 2.5wt% Ti C s...This study investigated enhancing the wear resistance of Ti6Al4V alloys for medical applications by incorporating Ti C nanoreinforcements using advanced spark plasma sintering(SPS). The addition of up to 2.5wt% Ti C significantly improved the mechanical properties, including a notable 18.2% increase in hardness(HV 332). Fretting wear tests against 316L stainless steel(SS316L) balls demonstrated a 20wt%–22wt% reduction in wear volume in the Ti6Al4V/Ti C composites compared with the monolithic alloy. Microstructural analysis revealed that Ti C reinforcement controlled the grain orientation and reduced the β-phase content, which contributed to enhanced mechanical properties. The monolithic alloy exhibited a Widmanstätten lamellar microstructure, while increasing the Ti C content modified the wear mechanisms from ploughing and adhesion(0–0.5wt%) to pitting and abrasion(1wt%–2.5wt%). At higher reinforcement levels, the formation of a robust oxide layer through tribo-oxide treatment effectively reduced the wear volume by minimizing the abrasive effects and plastic deformation. This study highlights the potential of SPS-mediated Ti C reinforcement as a transformative approach for improving the performance of Ti6Al4V alloys, paving the way for advanced medical applications.展开更多
Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley a...Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.展开更多
高精度且鲁棒的预测模型建立高度依赖于样本数据的大小、多样性和分布;日益积累的文献数据为获得大量的多样性样本数据提供了可能。以SLM-ed IN 718合金的相对密度(RD)为研究对象,针对从文献中挖掘的激光功率P、扫描速度V、扫描间距HS...高精度且鲁棒的预测模型建立高度依赖于样本数据的大小、多样性和分布;日益积累的文献数据为获得大量的多样性样本数据提供了可能。以SLM-ed IN 718合金的相对密度(RD)为研究对象,针对从文献中挖掘的激光功率P、扫描速度V、扫描间距HS和铺粉厚度LT与RD样本数据存在缺失参数和分布不均问题,采用最大期望化(EM)算法对缺失参数进行补齐;采用带有梯度惩罚的WGAN算法(WGAN-GP)对数据稀疏的低RD区间生成虚拟样本数据。然后,分别基于补齐文献数据和补充虚拟数据,采用常青藤算法优化的随机森林(IVYA-RF)构建了RD预测模型,并对模型预测精度进行了定量评估和实验验证。结果表明:基于补充虚拟数据集构建的IVYA-RF模型II比基于补齐文献数据集构建的IVYA-RF模型I具有更好的预测精度,其原因主要来源于在低RD区间生成虚拟数据后,使建模样本数据的分布均匀性得到改善,这也是数据增强与机器学习相结合的意义所在。对新实验数据的验证取得了满意的预测精度,其中,IVYA-RF模型I验证结果的统计学参数R2(决定系数)、RMSE(均方根误差)、MAE(平均绝对误差)和MRE(平均相对误差)分别达到了0.891、1.352%、0.915%和0.98%;IVYA-RF模型II验证结果的R2增大至0.956,RMSE、MAE和MRE分别减小至0.833%、0.687%和0.71%,同样显示出后者比前者具有更好的预测精度。实验验证结果表明,所构建的RD预测模型具有较好的鲁棒性,从而具备了较好的工程应用价值。展开更多
文摘This study investigated enhancing the wear resistance of Ti6Al4V alloys for medical applications by incorporating Ti C nanoreinforcements using advanced spark plasma sintering(SPS). The addition of up to 2.5wt% Ti C significantly improved the mechanical properties, including a notable 18.2% increase in hardness(HV 332). Fretting wear tests against 316L stainless steel(SS316L) balls demonstrated a 20wt%–22wt% reduction in wear volume in the Ti6Al4V/Ti C composites compared with the monolithic alloy. Microstructural analysis revealed that Ti C reinforcement controlled the grain orientation and reduced the β-phase content, which contributed to enhanced mechanical properties. The monolithic alloy exhibited a Widmanstätten lamellar microstructure, while increasing the Ti C content modified the wear mechanisms from ploughing and adhesion(0–0.5wt%) to pitting and abrasion(1wt%–2.5wt%). At higher reinforcement levels, the formation of a robust oxide layer through tribo-oxide treatment effectively reduced the wear volume by minimizing the abrasive effects and plastic deformation. This study highlights the potential of SPS-mediated Ti C reinforcement as a transformative approach for improving the performance of Ti6Al4V alloys, paving the way for advanced medical applications.
基金supported by the General Program of the National Natural Science Foundation of China(No.52274326)the China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202109)the Seventh Batch of Ten Thousand Talents Plan of China(No.ZX20220553).
文摘Sinter is the core raw material for blast furnaces.Flue pressure,which is an important state parameter,affects sinter quality.In this paper,flue pressure prediction and optimization were studied based on the shapley additive explanation(SHAP)to predict the flue pressure and take targeted adjustment measures.First,the sintering process data were collected and processed.A flue pressure prediction model was then constructed after comparing different feature selection methods and model algorithms using SHAP+extremely random-ized trees(ET).The prediction accuracy of the model within the error range of±0.25 kPa was 92.63%.SHAP analysis was employed to improve the interpretability of the prediction model.The effects of various sintering operation parameters on flue pressure,the relation-ship between the numerical range of key operation parameters and flue pressure,the effect of operation parameter combinations on flue pressure,and the prediction process of the flue pressure prediction model on a single sample were analyzed.A flue pressure optimization module was also constructed and analyzed when the prediction satisfied the judgment conditions.The operating parameter combination was then pushed.The flue pressure was increased by 5.87%during the verification process,achieving a good optimization effect.
文摘高精度且鲁棒的预测模型建立高度依赖于样本数据的大小、多样性和分布;日益积累的文献数据为获得大量的多样性样本数据提供了可能。以SLM-ed IN 718合金的相对密度(RD)为研究对象,针对从文献中挖掘的激光功率P、扫描速度V、扫描间距HS和铺粉厚度LT与RD样本数据存在缺失参数和分布不均问题,采用最大期望化(EM)算法对缺失参数进行补齐;采用带有梯度惩罚的WGAN算法(WGAN-GP)对数据稀疏的低RD区间生成虚拟样本数据。然后,分别基于补齐文献数据和补充虚拟数据,采用常青藤算法优化的随机森林(IVYA-RF)构建了RD预测模型,并对模型预测精度进行了定量评估和实验验证。结果表明:基于补充虚拟数据集构建的IVYA-RF模型II比基于补齐文献数据集构建的IVYA-RF模型I具有更好的预测精度,其原因主要来源于在低RD区间生成虚拟数据后,使建模样本数据的分布均匀性得到改善,这也是数据增强与机器学习相结合的意义所在。对新实验数据的验证取得了满意的预测精度,其中,IVYA-RF模型I验证结果的统计学参数R2(决定系数)、RMSE(均方根误差)、MAE(平均绝对误差)和MRE(平均相对误差)分别达到了0.891、1.352%、0.915%和0.98%;IVYA-RF模型II验证结果的R2增大至0.956,RMSE、MAE和MRE分别减小至0.833%、0.687%和0.71%,同样显示出后者比前者具有更好的预测精度。实验验证结果表明,所构建的RD预测模型具有较好的鲁棒性,从而具备了较好的工程应用价值。
