Laser additive manufacturing(LAM)of titanium(Ti)alloys has emerged as a transformative technology with vast potential across multiple industries.To recap the state of the art,Ti alloys processed by two essential LAM t...Laser additive manufacturing(LAM)of titanium(Ti)alloys has emerged as a transformative technology with vast potential across multiple industries.To recap the state of the art,Ti alloys processed by two essential LAM techniques(i.e.,laser powder bed fusion and laser-directed energy deposition)will be reviewed,covering the aspects of processes,materials and post-processing.The impacts of process parameters and strategies for optimizing parameters will be elucidated.Various types of Ti alloys processed by LAM,includingα-Ti,(α+β)-Ti,andβ-Ti alloys,will be overviewed in terms of micro structures and benchmarking properties.Furthermore,the post-processing methods for improving the performance of L AM-processed Ti alloys,including conventional and novel heat treatment,hot isostatic pressing,and surface processing(e.g.,ultrasonic and laser shot peening),will be systematically reviewed and discussed.The review summarizes the process windows,properties,and performance envelopes and benchmarks the research achievements in LAM of Ti alloys.The outlooks of further trends in LAM of Ti alloys are also highlighted at the end of the review.This comprehensive review could serve as a valuable resource for researchers and practitioners,promoting further advancements in LAM-built Ti alloys and their applications.展开更多
The present work examined the anisotropy magnitudes obtained from different elastic models of cubic metals(Cu,5383 Al alloy,FCC austenite steel and BCC steel)to explore the origin of strain anisotropy.The results show...The present work examined the anisotropy magnitudes obtained from different elastic models of cubic metals(Cu,5383 Al alloy,FCC austenite steel and BCC steel)to explore the origin of strain anisotropy.The results showed that stable intersections were observed from the modeled and experimental plots of the reciprocal elastic modulus(1/Ehkl)and orientation parameter(Γ).The effectiveness of quasi elasto-plastic model based method in correcting strain anisotropy was further verified in cold-worked specimens.For the important input parameters in dislocation model based diffraction line profile analysis methods,the average diffraction contrast factors(■)of dislocations were observed to depend on elastic constants.Interesting intersections were found from linear dependence of■onΓ.The conventional input■values indicated distinct dependencies on given elastic constants in diffraction line profile analysis.Accordingly,a refined approach was proposed by adopting the optimized intersections as input values,by which more reliable results could be obtained in practical applications.展开更多
The AlSi20/8009 aluminum alloy was heated to high temperatures near the melting point and cooled to investigate the effect of external Si addition on the phase evolution of Al12(Fe,V)3 Si dispersion. Differential scan...The AlSi20/8009 aluminum alloy was heated to high temperatures near the melting point and cooled to investigate the effect of external Si addition on the phase evolution of Al12(Fe,V)3 Si dispersion. Differential scanning calorimeter, scanning electron microscope, energy dispersive spectrometer and X-ray diffractometer were employed.The results showed that Al12(Fe,V)3 Si and Si phases evolved into a needle-like Al4.5 Fe Si phase and a nano-sized V-rich phase during holding the alloy at 580-600℃. With increasing holding temperature to 620-640℃, Al4.5 Fe Si and nano-sized V-rich phases evolved reversibly into Al12(Fe,V)3 Si and Si phases, of which Al12(Fe,V)3 Si occupied a coarse and hexagonal morphology. During the alloy(after holding at 640 ℃) furnace cooling to 570 ℃ or lower, Si and Al12(Fe,V)3 Si phases evolved into strip-like Al4.5 Fe Si and the V-rich phases, which is a novel formation route for Al4.5 Fe Si phase different from Al-Fe-Si ternary system.展开更多
An Al-Cu-Mg-Zr alloy,which obtained different homogenization cooling rates by changing the heattreated sample size,was compressed to various strains at the deformation temperature of 300℃ and strain rate of 0.01 s^(-...An Al-Cu-Mg-Zr alloy,which obtained different homogenization cooling rates by changing the heattreated sample size,was compressed to various strains at the deformation temperature of 300℃ and strain rate of 0.01 s^(-1).The results showed that the homogenization cooling rate had strong effects on the hot deformation behavior of the alloy.The flow stress and relative dynamic softening rate of the alloy were significantly higher under a high cooling rate(HCR) than those under a low cooling rate(LCR).Furthermore,based on X-ray diffraction,scanning electron microscopy,transmission electron microscopy,and thermodynamic equilibrium phase calculation,the substructure evolution in the grain interior,morphology,and spatial distribution of the precipitates were studied to determine the differences in the flow softening mechanism.The main softening mechanism could be summarized as dynamic recovery and precipitation coarsening for the LCR alloy and dynamic precipitation for the HCR alloy.展开更多
BACKGROUND Thalidomide is an effective treatment for refractory Crohn’s disease(CD).However,thalidomide-induced peripheral neuropathy(TiPN),which has a large individual variation,is a major cause of treatment failure...BACKGROUND Thalidomide is an effective treatment for refractory Crohn’s disease(CD).However,thalidomide-induced peripheral neuropathy(TiPN),which has a large individual variation,is a major cause of treatment failure.TiPN is rarely predictable and recognized,especially in CD.It is necessary to develop a risk model to predict TiPN occurrence.AIM To develop and compare a predictive model of TiPN using machine learning based on comprehensive clinical and genetic variables.METHODS A retrospective cohort of 164 CD patients from January 2016 to June 2022 was used to establish the model.The National Cancer Institute Common Toxicity Criteria Sensory Scale(version 4.0)was used to assess TiPN.With 18 clinical features and 150 genetic variables,five predictive models were established and evaluated by the confusion matrix receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),specificity,sensitivity(recall rate),precision,accuracy,and F1 score.RESULTS The top-ranking five risk variables associated with TiPN were interleukin-12 rs1353248[P=0.0004,odds ratio(OR):8.983,95%confidence interval(CI):2.497-30.90],dose(mg/d,P=0.002),brainderived neurotrophic factor(BDNF)rs2030324(P=0.001,OR:3.164,95%CI:1.561-6.434),BDNF rs6265(P=0.001,OR:3.150,95%CI:1.546-6.073)and BDNF rs11030104(P=0.001,OR:3.091,95%CI:1.525-5.960).In the training set,gradient boosting decision tree(GBDT),extremely random trees(ET),random forest,logistic regression and extreme gradient boosting(XGBoost)obtained AUROC values>0.90 and AUPRC>0.87.Among these models,XGBoost and GBDT obtained the first two highest AUROC(0.90 and 1),AUPRC(0.98 and 1),accuracy(0.96 and 0.98),precision(0.90 and 0.95),F1 score(0.95 and 0.98),specificity(0.94 and 0.97),and sensitivity(1).In the validation set,XGBoost algorithm exhibited the best predictive performance with the highest specificity(0.857),accuracy(0.818),AUPRC(0.86)and AUROC(0.89).ET and GBDT obtained the highest sensitivity(1)and F1 score(0.8).Overall,compared with other state-of-the-art classifiers such as ET,GBDT and RF,XGBoost algorithm not only showed a more stable performance,but also yielded higher ROC-AUC and PRC-AUC scores,demonstrating its high accuracy in prediction of TiPN occurrence.CONCLUSION The powerful XGBoost algorithm accurately predicts TiPN using 18 clinical features and 14 genetic variables.With the ability to identify high-risk patients using single nucleotide polymorphisms,it offers a feasible option for improving thalidomide efficacy in CD patients.展开更多
基金financially supported by the 2022 MTC Young Individual Research Grants under Singapore Research,Innovation and Enterprise(RIE)2025 Plan(No.M22K3c0097)the Natural Science Foundation of US(No.DMR-2104933)the sponsorship of the China Scholarship Council(No.202106130051)。
文摘Laser additive manufacturing(LAM)of titanium(Ti)alloys has emerged as a transformative technology with vast potential across multiple industries.To recap the state of the art,Ti alloys processed by two essential LAM techniques(i.e.,laser powder bed fusion and laser-directed energy deposition)will be reviewed,covering the aspects of processes,materials and post-processing.The impacts of process parameters and strategies for optimizing parameters will be elucidated.Various types of Ti alloys processed by LAM,includingα-Ti,(α+β)-Ti,andβ-Ti alloys,will be overviewed in terms of micro structures and benchmarking properties.Furthermore,the post-processing methods for improving the performance of L AM-processed Ti alloys,including conventional and novel heat treatment,hot isostatic pressing,and surface processing(e.g.,ultrasonic and laser shot peening),will be systematically reviewed and discussed.The review summarizes the process windows,properties,and performance envelopes and benchmarks the research achievements in LAM of Ti alloys.The outlooks of further trends in LAM of Ti alloys are also highlighted at the end of the review.This comprehensive review could serve as a valuable resource for researchers and practitioners,promoting further advancements in LAM-built Ti alloys and their applications.
基金Project(51904099)supported by the National Natural Science Foundation of ChinaProject(531118010353)supported by the Fundamental Research Funds for the Central Universities,China。
文摘The present work examined the anisotropy magnitudes obtained from different elastic models of cubic metals(Cu,5383 Al alloy,FCC austenite steel and BCC steel)to explore the origin of strain anisotropy.The results showed that stable intersections were observed from the modeled and experimental plots of the reciprocal elastic modulus(1/Ehkl)and orientation parameter(Γ).The effectiveness of quasi elasto-plastic model based method in correcting strain anisotropy was further verified in cold-worked specimens.For the important input parameters in dislocation model based diffraction line profile analysis methods,the average diffraction contrast factors(■)of dislocations were observed to depend on elastic constants.Interesting intersections were found from linear dependence of■onΓ.The conventional input■values indicated distinct dependencies on given elastic constants in diffraction line profile analysis.Accordingly,a refined approach was proposed by adopting the optimized intersections as input values,by which more reliable results could be obtained in practical applications.
基金Project(CX20190310)supported by the Hunan Provincial Innovation Foundation for Postgraduate,ChinaProject(51574118)supported by the National Natural Science Foundation of China+1 种基金Project(2016GK4056)supported by Key Technologies R&D in Strategic Emerging Industries and Transformation in High-tech Achievements Program of Hunan Province,ChinaProject(2018GK5068)supported by Innovation and Entrepreneurship Technology Investment Project of Hunan Province,China。
文摘The AlSi20/8009 aluminum alloy was heated to high temperatures near the melting point and cooled to investigate the effect of external Si addition on the phase evolution of Al12(Fe,V)3 Si dispersion. Differential scanning calorimeter, scanning electron microscope, energy dispersive spectrometer and X-ray diffractometer were employed.The results showed that Al12(Fe,V)3 Si and Si phases evolved into a needle-like Al4.5 Fe Si phase and a nano-sized V-rich phase during holding the alloy at 580-600℃. With increasing holding temperature to 620-640℃, Al4.5 Fe Si and nano-sized V-rich phases evolved reversibly into Al12(Fe,V)3 Si and Si phases, of which Al12(Fe,V)3 Si occupied a coarse and hexagonal morphology. During the alloy(after holding at 640 ℃) furnace cooling to 570 ℃ or lower, Si and Al12(Fe,V)3 Si phases evolved into strip-like Al4.5 Fe Si and the V-rich phases, which is a novel formation route for Al4.5 Fe Si phase different from Al-Fe-Si ternary system.
基金financially supported by the National Natural Science Foundation of China(Nos.51674111 and51605234)the Research Fund for the Doctoral Program of Higher Education of China(No.20130161110007)。
文摘An Al-Cu-Mg-Zr alloy,which obtained different homogenization cooling rates by changing the heattreated sample size,was compressed to various strains at the deformation temperature of 300℃ and strain rate of 0.01 s^(-1).The results showed that the homogenization cooling rate had strong effects on the hot deformation behavior of the alloy.The flow stress and relative dynamic softening rate of the alloy were significantly higher under a high cooling rate(HCR) than those under a low cooling rate(LCR).Furthermore,based on X-ray diffraction,scanning electron microscopy,transmission electron microscopy,and thermodynamic equilibrium phase calculation,the substructure evolution in the grain interior,morphology,and spatial distribution of the precipitates were studied to determine the differences in the flow softening mechanism.The main softening mechanism could be summarized as dynamic recovery and precipitation coarsening for the LCR alloy and dynamic precipitation for the HCR alloy.
基金National Natural Science Foundation of China,No.81973398,No.81730103,No.81573507 and No.82020108031The National Key Research and Development Program,No.2017YFC0909300 and No.2016YFC0905001+5 种基金Guangdong Provincial Key Laboratory of Construction Foundation,No.2017B030314030 and No.2020B1212060034Science and Technology Program of Guangzhou,No.201607020031National Engineering and Technology Research Center for New Drug Druggability Evaluation(Seed Program of Guangdong Province),No.2017B090903004The 111 Project,No.B16047China Postdoctoral Science Foundation,No.2019M66324,No.2020M683140 and No.2020M683139Natural Science Foundation of Guangdong Province,No.2022A1515012549 and No.2023A1515012667.
文摘BACKGROUND Thalidomide is an effective treatment for refractory Crohn’s disease(CD).However,thalidomide-induced peripheral neuropathy(TiPN),which has a large individual variation,is a major cause of treatment failure.TiPN is rarely predictable and recognized,especially in CD.It is necessary to develop a risk model to predict TiPN occurrence.AIM To develop and compare a predictive model of TiPN using machine learning based on comprehensive clinical and genetic variables.METHODS A retrospective cohort of 164 CD patients from January 2016 to June 2022 was used to establish the model.The National Cancer Institute Common Toxicity Criteria Sensory Scale(version 4.0)was used to assess TiPN.With 18 clinical features and 150 genetic variables,five predictive models were established and evaluated by the confusion matrix receiver operating characteristic curve(AUROC),area under the precision-recall curve(AUPRC),specificity,sensitivity(recall rate),precision,accuracy,and F1 score.RESULTS The top-ranking five risk variables associated with TiPN were interleukin-12 rs1353248[P=0.0004,odds ratio(OR):8.983,95%confidence interval(CI):2.497-30.90],dose(mg/d,P=0.002),brainderived neurotrophic factor(BDNF)rs2030324(P=0.001,OR:3.164,95%CI:1.561-6.434),BDNF rs6265(P=0.001,OR:3.150,95%CI:1.546-6.073)and BDNF rs11030104(P=0.001,OR:3.091,95%CI:1.525-5.960).In the training set,gradient boosting decision tree(GBDT),extremely random trees(ET),random forest,logistic regression and extreme gradient boosting(XGBoost)obtained AUROC values>0.90 and AUPRC>0.87.Among these models,XGBoost and GBDT obtained the first two highest AUROC(0.90 and 1),AUPRC(0.98 and 1),accuracy(0.96 and 0.98),precision(0.90 and 0.95),F1 score(0.95 and 0.98),specificity(0.94 and 0.97),and sensitivity(1).In the validation set,XGBoost algorithm exhibited the best predictive performance with the highest specificity(0.857),accuracy(0.818),AUPRC(0.86)and AUROC(0.89).ET and GBDT obtained the highest sensitivity(1)and F1 score(0.8).Overall,compared with other state-of-the-art classifiers such as ET,GBDT and RF,XGBoost algorithm not only showed a more stable performance,but also yielded higher ROC-AUC and PRC-AUC scores,demonstrating its high accuracy in prediction of TiPN occurrence.CONCLUSION The powerful XGBoost algorithm accurately predicts TiPN using 18 clinical features and 14 genetic variables.With the ability to identify high-risk patients using single nucleotide polymorphisms,it offers a feasible option for improving thalidomide efficacy in CD patients.
