Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to t...Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to the existence of multiple variation streams, it is challenging to model and analyze variation propagation in a multi-station system. Current approaches to error modeling for multistation machining process are not explicit enough for error control and ensuring final product quality. In this paper, a mathematic model to depict the part dimensional variation of the complex multistation manufacturing process is formulated. A linear state space dimensional error propagation equation is established through kinematics analysis of the influence of locating parameter variations and locating datum variations on dimensional errors, so the dimensional error accumulation and transformation within the multistation process are quantitatively described. A systematic procedure to build the model is presented, which enhances the way to determine the variation sources in complex machining systems. A simple two-dimensional example is used to illustrate the proposed procedures. Finally, an industrial case of multistation machining part in a manufacturing shop is given to testify the validation and practicability of the method. The proposed analytical model is essential to quality control and improvement for multistation systems in machining quality forecasting and design optimization.展开更多
As an important application research topic of the intelligent aviation multi-station, collaborative detecting must overcome the problem of scouting measurement with status of 'fragmentation', and the NP-hardne...As an important application research topic of the intelligent aviation multi-station, collaborative detecting must overcome the problem of scouting measurement with status of 'fragmentation', and the NP-hardness problem of matching association between target and measurement in the process of scouting to data-link, which has complicated technical architecture of network construction. In this paper, taking advantage of cooperation mechanism on signal level in the aviation multi-station sympathetic network, a method of obtaining target time difference of arrival (TDOA) measurement using multi-station collaborative detecting based on time-frequency association is proposed. The method can not only achieve matching between target and its measurement, but also obtain TDOA measurement by further evolutionary transaction through refreshing sequential pulse time of arrival (TOA) measurement matrix for matching and correlating. Simulation results show that the accuracy of TDOA measurement has significant superiority over TOA, and detection probability of false TDOA measurement introduced by noise and fake measurement can be reduced effectively.展开更多
On February 16,2021,an artificial object moving slowly over the Mediterranean was recorded by the Spanish Meteor Network(SPMN).Based on astrometric measurements,we identified this event as the reentry engine burn of a...On February 16,2021,an artificial object moving slowly over the Mediterranean was recorded by the Spanish Meteor Network(SPMN).Based on astrometric measurements,we identified this event as the reentry engine burn of a SpaceX Falcon 9 launch vehicle’s upper stage.To study this event in detail,we adapted the plane intersection method for near-straight meteoroid trajectories to analyze the slow and curved orbits associated with artificial objects.To corroborate our results,we approximated the orbital elements of the upper stage using four pieces of“debris”cataloged by the U.S.Government’s Combined Space Operations Center.Based on these calculations,we also estimated the possible deorbit hazard zone using the MSISE90 model atmosphere.We provide guidance regarding the interference that these artificial bolides may generate in fireball studies.Additionally,because artificial bolides will likely become more frequent in the future,we point out the new role that ground-based detection networks can play in the monitoring of potentially hazardous artificial objects in near-Earth space and in determining the strewn fields of artificial space debris.展开更多
Dimensional variation analysis in multistation manufacturing processes(MMPs)is a challenging research topic with great practical significance.Researchers have been focused on constructing various mathematical models t...Dimensional variation analysis in multistation manufacturing processes(MMPs)is a challenging research topic with great practical significance.Researchers have been focused on constructing various mathematical models to identify the correlations among the huge amounts of collected production data.However,current models have achieved insufficient insights into the variation correlation laws due to the complexity of the data’s mutual relations.In this study,a data-driven modeling method is developed for deep data-mining and dimensional variation analysis.The proposed initial mathematical expression originates from practical engineering knowledge.Through a mathematical treatment,the mathematical expression is transformed into a first-order AR(1)model format,which contains multiple dimensional variations’interstation and temporal correlating information.To obtain this information,the estimation of the proposed model is discussed in detail.A simulation case involving two key product characteristics of a grinding process is used to demonstrate the effectiveness and accuracy of the proposed method for dimensional variation analysis in MMPs.展开更多
基金supported by National Department Fundamental Research Foundation of China (Grant No. B222090014)National Department Technology Fundatmental Foundaiton of China (Grant No. C172009C001)
文摘Multistation machining process is widely applied in contemporary manufacturing environment. Modeling of variation propagation in multistation machining process is one of the most important research scenarios. Due to the existence of multiple variation streams, it is challenging to model and analyze variation propagation in a multi-station system. Current approaches to error modeling for multistation machining process are not explicit enough for error control and ensuring final product quality. In this paper, a mathematic model to depict the part dimensional variation of the complex multistation manufacturing process is formulated. A linear state space dimensional error propagation equation is established through kinematics analysis of the influence of locating parameter variations and locating datum variations on dimensional errors, so the dimensional error accumulation and transformation within the multistation process are quantitatively described. A systematic procedure to build the model is presented, which enhances the way to determine the variation sources in complex machining systems. A simple two-dimensional example is used to illustrate the proposed procedures. Finally, an industrial case of multistation machining part in a manufacturing shop is given to testify the validation and practicability of the method. The proposed analytical model is essential to quality control and improvement for multistation systems in machining quality forecasting and design optimization.
基金supported by the National Natural Science Foundation of China(61472443)the Basic Research Priorities Program of Shaanxi Province Natural Science Foundation of China(2013JQ8042)
文摘As an important application research topic of the intelligent aviation multi-station, collaborative detecting must overcome the problem of scouting measurement with status of 'fragmentation', and the NP-hardness problem of matching association between target and measurement in the process of scouting to data-link, which has complicated technical architecture of network construction. In this paper, taking advantage of cooperation mechanism on signal level in the aviation multi-station sympathetic network, a method of obtaining target time difference of arrival (TDOA) measurement using multi-station collaborative detecting based on time-frequency association is proposed. The method can not only achieve matching between target and its measurement, but also obtain TDOA measurement by further evolutionary transaction through refreshing sequential pulse time of arrival (TOA) measurement matrix for matching and correlating. Simulation results show that the accuracy of TDOA measurement has significant superiority over TOA, and detection probability of false TDOA measurement introduced by noise and fake measurement can be reduced effectively.
基金This research was supported by the research project(Grant No.PGC2018-097374-B-I00,PI:JMT-R)which is funded by FEDER/Ministerio de Ciencia e Innovación-Agencia Estatal de Investigación.This project has also received funding from the European Research Council(ERC)under the European Union’s Horizon 2020 Research and Innovation Programme(Grant No.865657)for the project“Quantum Chemistry on Interstellar Grains”(QUANTUMGRAIN)We also express appreciation for the valuable video recordings obtained from Benicàssim(Castellón)by Vicent Ibanez(AVAMET).
文摘On February 16,2021,an artificial object moving slowly over the Mediterranean was recorded by the Spanish Meteor Network(SPMN).Based on astrometric measurements,we identified this event as the reentry engine burn of a SpaceX Falcon 9 launch vehicle’s upper stage.To study this event in detail,we adapted the plane intersection method for near-straight meteoroid trajectories to analyze the slow and curved orbits associated with artificial objects.To corroborate our results,we approximated the orbital elements of the upper stage using four pieces of“debris”cataloged by the U.S.Government’s Combined Space Operations Center.Based on these calculations,we also estimated the possible deorbit hazard zone using the MSISE90 model atmosphere.We provide guidance regarding the interference that these artificial bolides may generate in fireball studies.Additionally,because artificial bolides will likely become more frequent in the future,we point out the new role that ground-based detection networks can play in the monitoring of potentially hazardous artificial objects in near-Earth space and in determining the strewn fields of artificial space debris.
基金The research work was supported by the natural science fund for colleges and universities in Jiangsu province(Nos.15KJB460016 and 14KJB460029)the major industrial technology project in Xuzhou city(No.KC16GZ015)+1 种基金the major industrial technology project in Jiangsu Province(No.BE2016047)the natural science foundation of China(No.71561016).The author would also like to gratefully acknowledge Professor Fugee Tsung and the other colleagues at Hong Kong University of Science and Technology for their valuable comments.
文摘Dimensional variation analysis in multistation manufacturing processes(MMPs)is a challenging research topic with great practical significance.Researchers have been focused on constructing various mathematical models to identify the correlations among the huge amounts of collected production data.However,current models have achieved insufficient insights into the variation correlation laws due to the complexity of the data’s mutual relations.In this study,a data-driven modeling method is developed for deep data-mining and dimensional variation analysis.The proposed initial mathematical expression originates from practical engineering knowledge.Through a mathematical treatment,the mathematical expression is transformed into a first-order AR(1)model format,which contains multiple dimensional variations’interstation and temporal correlating information.To obtain this information,the estimation of the proposed model is discussed in detail.A simulation case involving two key product characteristics of a grinding process is used to demonstrate the effectiveness and accuracy of the proposed method for dimensional variation analysis in MMPs.
