Iron alloyed Ni3Al with composition of Ni-18. 8Ab10. 7Fe-0. 5Mn-0. 5Ti-0. 2B in atom percent （NAC alloy） showed attractive tribological properties under unlubrication condition at room temperature. The alloy was pre...Iron alloyed Ni3Al with composition of Ni-18. 8Ab10. 7Fe-0. 5Mn-0. 5Ti-0. 2B in atom percent （NAC alloy） showed attractive tribological properties under unlubrication condition at room temperature. The alloy was prepared by hot isostatic pressing （HIP） process. The wear properties were associated with its intrinsic deformation mechanism. Unfortunately, the single phase NAC-alloy worked inadequately with its counterpart disk, and also showed a poor machinability. In the present work, NAC-alloy matrix composite with 6 % （volume percent） MnS particle addi- tion was studied to improve its wear behaviors and performance on machining. Two metallurgical processes of HIP and vacuum casting were applied to produce the testing materials. Pin-on-disk （POD） measurements were carried out at room temperature. A commercial vermicular graphite cast iron was selected as a reference material. The counter- part disk was made of a grey cast iron as liner material in ship engines. The contact pressures of 2.83 MPa and 5.66 MPa were normally applied in the tests. The investigation indicated that MnS particle addition in the NAC-alloy composites functions as an effective solid lubricant, and improved wear properties and machinability of the materials. Obvi- ously, as-cast NAC-alloy with in-situ formed MnS-phase was working more effectively with the counterpart, compa- ring to the HIPed NAC-alloy composite with MnS particles. At the high contact pressure of 5.66 MPa, the specific wear rate of the as-cast NAC-alloy composite was high. The phenomenon of the negative effect is mostly due to the brittle second NiAl phase as evidenced in the microstructure analysis.展开更多
LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensi...LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensive experimental observations of friction behaviors in the prestiction,some variables were abstracted to depict the rules in the prestiction regime.Based upon the knowledge of friction modeling,a novel friction model including the presliding regime,the gross sliding regime and the prestiction regime was then presented to overcome the shortcomings of the LuGre model.The reason that LuGre model cannot estimate the prestiction friction was analyzed in theory.Feasibility analysis of the proposed model in modeling the prestiction friction was also addressed.A parameter identification method for the proposed model based on multilevel coordinate search algorithm was presented.The proposed friction compensation strategy was composed of a nonlinear friction observer and a feedforward mechanism.The friction observer was designed to estimate the friction force in the presliding and the gross sliding regimes.And the friction force was estimated based on the model in the prestiction regime.The comparative trajectory tracking experiments were conducted on a simulator of inertially stabilization platforms among three control schemes:the single proportional–derivative(PD)control,the PD with LuGre model-based compensation and the PD with compensator based on the presented model.The experimental results reveal that the control scheme based on the proposed model has the best tracking performance.It reduces the peak-to-peak value(PPV)of tracking error to 0.2 mrad,which is improved almost 50%compared with the PD with LuGre model-based compensation.Compared to the single PD control,it reduces the PPV of error by 66.7%.展开更多
The effects of several parameters on the erosive wear were studied using the discrete element method(DEM).The Finnie model was implemented using an open‐source code.Regarding the time integration,the Gear algorithm w...The effects of several parameters on the erosive wear were studied using the discrete element method(DEM).The Finnie model was implemented using an open‐source code.Regarding the time integration,the Gear algorithm was used,and to ensure the accuracy of the DEM results,a time‐step sensitivity analysis was performed.The problem was modeled in two parts:first,the impact of a single particle on a surface was modeled,and then a more general model was prepared to examine the wear of surfaces under the flow of particles.The effects of the surface area,impact angle,speed,particle size,particle density,Young’s modulus,Poisson’s ratio,and restitution coefficient on the wear were studied numerically,and the results are discussed herein.展开更多
The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms.Data-driven methods,including machine learning(ML)algorithms,can yield a better comprehensive understa...The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms.Data-driven methods,including machine learning(ML)algorithms,can yield a better comprehensive understanding of complex problems under the influence of multiple parameters,typically for how tribological performances and material properties correlate.Correlation of friction coefficients and wear rates of copper/aluminum-graphite(Cu/Al-graphite)self-lubricating composites with their inherent material properties(composition,lubricant content,particle size,processing process,and interfacial bonding strength)and the variables related to the testing method(normal load,sliding speed,and sliding distance)were analyzed using traditional approaches,followed by modeling and prediction of tribological properties through five different ML algorithms,namely support vector machine(SVM),K-Nearest neighbor(KNN),random forest(RF),eXtreme gradient boosting(XGBoost),and least-squares boosting(LSBoost),based on the tribology experimental data.Results demonstrated that ML models could satisfactorily predict friction coefficient and wear rate from the material properties and testing method variables data.Herein,the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates,with R^(2) of 0.9219 and 0.9243,respectively.Feature importance analysis also revealed that the content of graphite and the hardness of the matrix have the greatest influence on the friction coefficients,and the normal load,the content of graphite,and the hardness of the matrix influence the wear rates the most.展开更多
The program of auto-identification of wear particles is given using aritficial neural network(ANN)technique,based on a set of debris morphology descriptor that de-scribes the shape characters of wear particles.The tra...The program of auto-identification of wear particles is given using aritficial neural network(ANN)technique,based on a set of debris morphology descriptor that de-scribes the shape characters of wear particles.The train-ing speed of the network with thw fuzzy-factor is muchfaster than that of the traditional methods.For esamale,the speed of training the network in this paper is increased five times in Exclusive OR problem(XORproblem)than other ways,and the debris chassification accuracy is more than 90% by this method,and the idemtification speed is very fast.展开更多
利用HDM-20端面摩擦磨损试验机,对不同塑化烧结温度、保温时间、冷却方式下无铅PTFE 3层复合材料进行摩擦磨损试验分析。结果表明塑化烧结工艺参数的改变对材料的摩擦状态稳定性和耐磨性有着重要的影响,在375℃保温塑化烧结60 m in,并...利用HDM-20端面摩擦磨损试验机,对不同塑化烧结温度、保温时间、冷却方式下无铅PTFE 3层复合材料进行摩擦磨损试验分析。结果表明塑化烧结工艺参数的改变对材料的摩擦状态稳定性和耐磨性有着重要的影响,在375℃保温塑化烧结60 m in,并随炉冷却至300℃出炉,可保证无铅PTFE 3层复合材料的综合摩擦磨损性能最好。展开更多
基金Item Sponsored by Swedish VINNOVA and Chinese MOST for International Colla borative Research Projects(P32737-1,P32737-2)
文摘Iron alloyed Ni3Al with composition of Ni-18. 8Ab10. 7Fe-0. 5Mn-0. 5Ti-0. 2B in atom percent （NAC alloy） showed attractive tribological properties under unlubrication condition at room temperature. The alloy was prepared by hot isostatic pressing （HIP） process. The wear properties were associated with its intrinsic deformation mechanism. Unfortunately, the single phase NAC-alloy worked inadequately with its counterpart disk, and also showed a poor machinability. In the present work, NAC-alloy matrix composite with 6 % （volume percent） MnS particle addi- tion was studied to improve its wear behaviors and performance on machining. Two metallurgical processes of HIP and vacuum casting were applied to produce the testing materials. Pin-on-disk （POD） measurements were carried out at room temperature. A commercial vermicular graphite cast iron was selected as a reference material. The counter- part disk was made of a grey cast iron as liner material in ship engines. The contact pressures of 2.83 MPa and 5.66 MPa were normally applied in the tests. The investigation indicated that MnS particle addition in the NAC-alloy composites functions as an effective solid lubricant, and improved wear properties and machinability of the materials. Obvi- ously, as-cast NAC-alloy with in-situ formed MnS-phase was working more effectively with the counterpart, compa- ring to the HIPed NAC-alloy composite with MnS particles. At the high contact pressure of 5.66 MPa, the specific wear rate of the as-cast NAC-alloy composite was high. The phenomenon of the negative effect is mostly due to the brittle second NiAl phase as evidenced in the microstructure analysis.
基金Projects(51135009,51105371) supported by the National Natural Science Foundation of China
文摘LuGre model has been widely used in friction modeling and compensation.However,the new friction regime,named prestiction regime,cannot be accurately characterized by LuGre model in the latest research.With the extensive experimental observations of friction behaviors in the prestiction,some variables were abstracted to depict the rules in the prestiction regime.Based upon the knowledge of friction modeling,a novel friction model including the presliding regime,the gross sliding regime and the prestiction regime was then presented to overcome the shortcomings of the LuGre model.The reason that LuGre model cannot estimate the prestiction friction was analyzed in theory.Feasibility analysis of the proposed model in modeling the prestiction friction was also addressed.A parameter identification method for the proposed model based on multilevel coordinate search algorithm was presented.The proposed friction compensation strategy was composed of a nonlinear friction observer and a feedforward mechanism.The friction observer was designed to estimate the friction force in the presliding and the gross sliding regimes.And the friction force was estimated based on the model in the prestiction regime.The comparative trajectory tracking experiments were conducted on a simulator of inertially stabilization platforms among three control schemes:the single proportional–derivative(PD)control,the PD with LuGre model-based compensation and the PD with compensator based on the presented model.The experimental results reveal that the control scheme based on the proposed model has the best tracking performance.It reduces the peak-to-peak value(PPV)of tracking error to 0.2 mrad,which is improved almost 50%compared with the PD with LuGre model-based compensation.Compared to the single PD control,it reduces the PPV of error by 66.7%.
基金financially supported by the Iran National Science Foundation(INSF)under Grant No.93038047
文摘The effects of several parameters on the erosive wear were studied using the discrete element method(DEM).The Finnie model was implemented using an open‐source code.Regarding the time integration,the Gear algorithm was used,and to ensure the accuracy of the DEM results,a time‐step sensitivity analysis was performed.The problem was modeled in two parts:first,the impact of a single particle on a surface was modeled,and then a more general model was prepared to examine the wear of surfaces under the flow of particles.The effects of the surface area,impact angle,speed,particle size,particle density,Young’s modulus,Poisson’s ratio,and restitution coefficient on the wear were studied numerically,and the results are discussed herein.
基金the National Key R&D Program of China(Grant No.2022YFB3809000)the Intellectual Property Program of Gansu(Grant No.22ZSCQ043).
文摘The tribological properties of self-lubricating composites are influenced by many variables and complex mechanisms.Data-driven methods,including machine learning(ML)algorithms,can yield a better comprehensive understanding of complex problems under the influence of multiple parameters,typically for how tribological performances and material properties correlate.Correlation of friction coefficients and wear rates of copper/aluminum-graphite(Cu/Al-graphite)self-lubricating composites with their inherent material properties(composition,lubricant content,particle size,processing process,and interfacial bonding strength)and the variables related to the testing method(normal load,sliding speed,and sliding distance)were analyzed using traditional approaches,followed by modeling and prediction of tribological properties through five different ML algorithms,namely support vector machine(SVM),K-Nearest neighbor(KNN),random forest(RF),eXtreme gradient boosting(XGBoost),and least-squares boosting(LSBoost),based on the tribology experimental data.Results demonstrated that ML models could satisfactorily predict friction coefficient and wear rate from the material properties and testing method variables data.Herein,the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates,with R^(2) of 0.9219 and 0.9243,respectively.Feature importance analysis also revealed that the content of graphite and the hardness of the matrix have the greatest influence on the friction coefficients,and the normal load,the content of graphite,and the hardness of the matrix influence the wear rates the most.
文摘The program of auto-identification of wear particles is given using aritficial neural network(ANN)technique,based on a set of debris morphology descriptor that de-scribes the shape characters of wear particles.The train-ing speed of the network with thw fuzzy-factor is muchfaster than that of the traditional methods.For esamale,the speed of training the network in this paper is increased five times in Exclusive OR problem(XORproblem)than other ways,and the debris chassification accuracy is more than 90% by this method,and the idemtification speed is very fast.
文摘利用HDM-20端面摩擦磨损试验机,对不同塑化烧结温度、保温时间、冷却方式下无铅PTFE 3层复合材料进行摩擦磨损试验分析。结果表明塑化烧结工艺参数的改变对材料的摩擦状态稳定性和耐磨性有着重要的影响,在375℃保温塑化烧结60 m in,并随炉冷却至300℃出炉,可保证无铅PTFE 3层复合材料的综合摩擦磨损性能最好。
