Hard and brittle materials, such as silicon, SiC, and optical glasses, are widely used in aerospace, military, integrated circuit, and other fields because of their excellent physical and chemical properties. However,...Hard and brittle materials, such as silicon, SiC, and optical glasses, are widely used in aerospace, military, integrated circuit, and other fields because of their excellent physical and chemical properties. However, these materials display poor machinability because of their hard and brittle properties. Damages such as surface micro-crack and subsurface damage often occur during machining of hard and brittle materials. Ultra-precision machining is widely used in processing hard and brittle materials to obtain nanoscale machining quality. However, the theoretical mechanism underlying this method remains unclear. This paper provides a review of present research on the molecular dynamics simulation of ultra-precision machining of hard and brittle materials. The future trends in this field are also discussed.展开更多
Based on impulse and vibration machining theories,a mathematical model of cutting force for the electroplated diamond ultrasonic wire saw was established using superposition principle.The differences between the cutti...Based on impulse and vibration machining theories,a mathematical model of cutting force for the electroplated diamond ultrasonic wire saw was established using superposition principle.The differences between the cutting forces with and without ultrasonic effect were analyzed theoretically and experimentally.The results indicate that the cutting force of diamond wire increases along with the spindle speed decrease and the lateral pressure increase.The force in ultrasonic vibration cutting is about 20% to 30% less than that in conventional cutting.Also,the cutting trajectory of single diamond grit in sawing process is simulated,and the reason that the ultrasonic vibration can reduce the cutting force is explained further.展开更多
Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via hi...Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and resource-consuming.In contrast,prediction at the classical level is highly efficient but less accurate.We deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical level.Using 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and hardness.With the trained model,we predicted 775,947 data in search of materials with ultrahigh hardness.We further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in nature.Among those,two super hard materials are completely new and have not been reported in literature so far.We further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first principles.Our results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.展开更多
基金Acknowledgements The authors would like to acknowledge the financial support from the National Natural Science of China （General Program） （Grant No. 51575083）, the Major Research plan of the National Natural Science Foundation of China （Grant No. 91323302）, the Science Fund for Creative Research Groups （Grant No. 51621064）, and the Young Scientists Fund of the National Natural Science Foundation of China （Grant No. 51505063）.
文摘Hard and brittle materials, such as silicon, SiC, and optical glasses, are widely used in aerospace, military, integrated circuit, and other fields because of their excellent physical and chemical properties. However, these materials display poor machinability because of their hard and brittle properties. Damages such as surface micro-crack and subsurface damage often occur during machining of hard and brittle materials. Ultra-precision machining is widely used in processing hard and brittle materials to obtain nanoscale machining quality. However, the theoretical mechanism underlying this method remains unclear. This paper provides a review of present research on the molecular dynamics simulation of ultra-precision machining of hard and brittle materials. The future trends in this field are also discussed.
基金Sponsored by Liaoning Innovation Team Fundation(2008T164)
文摘Based on impulse and vibration machining theories,a mathematical model of cutting force for the electroplated diamond ultrasonic wire saw was established using superposition principle.The differences between the cutting forces with and without ultrasonic effect were analyzed theoretically and experimentally.The results indicate that the cutting force of diamond wire increases along with the spindle speed decrease and the lateral pressure increase.The force in ultrasonic vibration cutting is about 20% to 30% less than that in conventional cutting.Also,the cutting trajectory of single diamond grit in sawing process is simulated,and the reason that the ultrasonic vibration can reduce the cutting force is explained further.
基金This work was supported by the NSF(award number 2030128,2110033)NASA SC Space Grant Consortium REAP Program(Award No.:521383-RP-SC004)+1 种基金SC EPSCoR/IDeA Program under NSF OIA-1655740(23-GC01)ASPIRE grant from the Office of the Vice President for Research at the University of South Carolina(project 80005046).
文摘Mechanical and thermal properties of materials are extremely important for various engineering and scientific fields such as energy conversion and energy storage.However,the characterization of these properties via high throughput screening at the quantum level,although highly accurate,is inefficient and very time-and resource-consuming.In contrast,prediction at the classical level is highly efficient but less accurate.We deploy scalable global attention graph neural network for accurate prediction of mechanical properties which bridge the gap between the accuracy at the quantum level and efficiency at the classical level.Using 10,158 elastic constants as training data,we trained the models on 5 mechanical properties,namely bulk modulus,shear modulus,Young’s modulus,Poisson’s ratio,and hardness.With the trained model,we predicted 775,947 data in search of materials with ultrahigh hardness.We further verify the recommended ultrahigh hardness materials by high precision first principles calculations,and we finally identify 20 structures with extreme hardness close to diamond,the hardest material in nature.Among those,two super hard materials are completely new and have not been reported in literature so far.We further recommend potential materials from bulk modulus prediction to search low lattice thermal conductivity,and we verify the thermal conductivity of 338 structures with first principles.Our results demonstrate that one can find materials with extreme mechanical properties recommended by graph neural network and low thermal conductivity material from bulk modulus prediction with minimal first principles calculations of the structures(only 0.04%)in the large-scale materials pool.
