Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in...Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.展开更多
The objective of this study is to develop an effective approach for product quality prediction in Computer Numerical Control turning of cantilever bars. A systematic predictive modelling procedure based on experimenta...The objective of this study is to develop an effective approach for product quality prediction in Computer Numerical Control turning of cantilever bars. A systematic predictive modelling procedure based on experimental investigations, neural network modelling and various statistical analysis tools is designed to produce the most accurate, practical and cost-effective prediction model. The modeling procedure begins by exploring the relationships between cutting parameters known to have an influence on quality characteristics of machined parts, such as dimensional errors, form errors and surface roughness, as well as their sensitivity to the process conditions. Based on these explorations and using numerous statistical tools, the most relevant variables to include in the prediction model are identified and fused using several artificial neural network architectures. An application on CNC turning of cantilever bars demonstrates that the proposed modeling procedure can be effectively and advantageously applied to quality characteristics prediction due to its simplicity, accuracy and efficiency. The experimental validation reveals that the resulting prediction model can correctly predict the quality characteristics of machined parts under variable machining conditions.展开更多
Nanoscale cantilevers(nanocantilevers)made from carbon nanotubes(CNTs)provide tremendous benefits in sensing and electromagnetic applications.This nanoscale structure is generally fabricated using chemical vapor depos...Nanoscale cantilevers(nanocantilevers)made from carbon nanotubes(CNTs)provide tremendous benefits in sensing and electromagnetic applications.This nanoscale structure is generally fabricated using chemical vapor deposition and/or dielectrophoresis,which contain manual,time-consuming processes such as the placing of additional electrodes and careful observation of single-grown CNTs.Here,we demonstrate a simple and Artificial Intelligence(Al)-assisted method for the effcient fabrication of a massive CNT-based nanocantilever.We used randomly positioned single CNTs on the substrate.The trained deep neural network recognizes the CNTs,measures their positions,and determines the edge of the CNT on which an electrode should be clamped to form a nanocantilever.Our experiments demonstrate that the recognition and measurement processes are automatically completed in 2 s,whereas comparable manual processing requires 12 h.Notwithstanding the small measurement error by the trained network(within 200 nm for 90%of the recognized CNTs),more than 34 nanocantilevers were successfully fabricated in one process.Such high accuracy contributes to the development of a massive field emitter using the CNT-based nanocantilever,in which the output current is obtained with a low applied voltage.We further showed the benefit of fabricating massive CNT-nanocantilever-based field emitters for neuromorphic computing.The activation function,which is a key function in a neural network,was physically realized using an individual CNT-based field emitter.The introduced neural network with the CNT-based field emitters recognized handwritten images successfully.We believe that our method can accelerate the research and development of CNT-based nanocantilevers for realizing promising future applications.展开更多
基金National Natural Science Foundation of China (Grant No.52178393)the Science and Technology Innovation Team of Shaanxi Innovation Capability Support Plan (Grant No.2020TD005)Science and Technology Innovation Project of China Railway Construction Bridge Engineering Bureau Group Co.,Ltd.(Grant No.DQJ-2020-B07)。
文摘Evaluating the adaptability of cantilever boring machine(CBM) through in-depth excavation and analysis of tunnel excavation data and rock mass parameters is the premise of mechanical design and efficient excavation in the field of underground space engineering.This paper presented a case study of tunnelling performance prediction method of CBM in sedimentary hard-rock tunnel of Karst landform type by using tunneling data and surrounding rock parameters.The uniaxial compressive strength(UCS),rock integrity factor(Kv),basic quality index([BQ]),rock quality index RQD,brazilian tensile strength(BTS) and brittleness index(BI) were introduced to construct a performance prediction database based on the hard-rock tunnel of Guiyang Metro Line 1 and Line 3,and then established the performance prediction model of cantilever boring machine.Then the deep belief network(DBN) was introduced into the performance prediction model,and the reliability of performance prediction model was verified by combining with engineering data.The study showed that the influence degree of surrounding rock parameters on the tunneling performance of the cantilever boring machine is UCS > [BQ] > BTS >RQD > Kv > BI.The performance prediction model shows that the instantaneous cutting rate(ICR) has a good correlation with the surrounding rock parameters,and the predicting model accuracy is related to the reliability of construction data.The prediction of limestone and dolomite sections of Line 3 based on the DBN performance prediction model shows that the measured ICR and predicted ICR is consistent and the built performance prediction model is reliable.The research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.
文摘The objective of this study is to develop an effective approach for product quality prediction in Computer Numerical Control turning of cantilever bars. A systematic predictive modelling procedure based on experimental investigations, neural network modelling and various statistical analysis tools is designed to produce the most accurate, practical and cost-effective prediction model. The modeling procedure begins by exploring the relationships between cutting parameters known to have an influence on quality characteristics of machined parts, such as dimensional errors, form errors and surface roughness, as well as their sensitivity to the process conditions. Based on these explorations and using numerous statistical tools, the most relevant variables to include in the prediction model are identified and fused using several artificial neural network architectures. An application on CNC turning of cantilever bars demonstrates that the proposed modeling procedure can be effectively and advantageously applied to quality characteristics prediction due to its simplicity, accuracy and efficiency. The experimental validation reveals that the resulting prediction model can correctly predict the quality characteristics of machined parts under variable machining conditions.
基金A part of this work was supported by Nagoya University Microstructural Characterization Platform as a program of the"Nanotechnology Platform"of the Ministry of Education,Culture,Sports,Science and Technology(MEXT),Japan.
文摘Nanoscale cantilevers(nanocantilevers)made from carbon nanotubes(CNTs)provide tremendous benefits in sensing and electromagnetic applications.This nanoscale structure is generally fabricated using chemical vapor deposition and/or dielectrophoresis,which contain manual,time-consuming processes such as the placing of additional electrodes and careful observation of single-grown CNTs.Here,we demonstrate a simple and Artificial Intelligence(Al)-assisted method for the effcient fabrication of a massive CNT-based nanocantilever.We used randomly positioned single CNTs on the substrate.The trained deep neural network recognizes the CNTs,measures their positions,and determines the edge of the CNT on which an electrode should be clamped to form a nanocantilever.Our experiments demonstrate that the recognition and measurement processes are automatically completed in 2 s,whereas comparable manual processing requires 12 h.Notwithstanding the small measurement error by the trained network(within 200 nm for 90%of the recognized CNTs),more than 34 nanocantilevers were successfully fabricated in one process.Such high accuracy contributes to the development of a massive field emitter using the CNT-based nanocantilever,in which the output current is obtained with a low applied voltage.We further showed the benefit of fabricating massive CNT-nanocantilever-based field emitters for neuromorphic computing.The activation function,which is a key function in a neural network,was physically realized using an individual CNT-based field emitter.The introduced neural network with the CNT-based field emitters recognized handwritten images successfully.We believe that our method can accelerate the research and development of CNT-based nanocantilevers for realizing promising future applications.
