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Multi-models associated with process information-driven process autonomous digital twin for multi-variety production of intelligent machines

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Science China Technological Sciences 2024年第12期67卷 3825-3842页

作者： ZHANG Lu WANG Xiao HE SongPing MAO XinYong LI Bin LIU HongQi State Engineering Research Center of Digital Manufacturing and Equipment Huazhong University of Science and Technology Wuhan Heavy Duty Machine Tool Group Corporation National NC System Engineering Research Center Huazhong University of Science and Technology

The intelligent development of machines, as one of the essential research components of smart manufacturing, is urgently needed to improve the intelligent production of product manufacturing processes within product families. The uncertainty of the process requirements within a product family and the time-varying nature of the processing performance of machines pose significant challenges for intelligent manufacturing. Digital twins(DTs) have proven to be very effective architectures for intelligent manufacturing; DTs support dynamic modeling capabilities in time and space and can provide effective technical support for manufacturing intelligence under multi-variety production. Therefore, a process autonomous digital twin(PADT) framework driven by characterization, prediction, and evaluation-based multi-models(CPEM) associated with process information is constructed to realize intelligent production. First, the multidimensional information contained in the machining process is analyzed to accurately locate the features of machining precision, and a machine performance characterization model is ***, a machine capacity prediction model based on time-scale information fusion is established to predict the processing capacities of machines under different production demands. Finally, a product distribution evaluation model based on the 3σ rule is established, which provides an effective evaluation index for the optimization of processing parameters. The application of CPEM-driven PADT in a commutator machining machine provides predictive manufacturing intelligence within a product family.

关键词： process autonomous digital twin precision performance characterization performance prediction model distribution evaluation model manufacturing intelligence

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An FFT performance model for Optimizing General-Purpose Processor Architecture

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Journal of Computer Science & Technology 2011年第5期26卷 875-889页

作者：李玲陈云霁刘道福钱诚胡伟武 Institute of Computing Technology Chinese Academy of Sciences Loongson Technologies Corporation Limited

General-purpose processor （GPP） is an important platform for fast Fourier transform （FFT）,due to its flexibility,reliability and *** is a representative application intensive in both computation and memory access,optimizing the FFT performance of a GPP also benefits the performances of many other *** facilitate the analysis of FFT,this paper proposes a theoretical model of the FFT *** model gives out a tight lower bound of the runtime of FFT on a GPP,and guides the architecture optimization for GPP as *** on the model,two theorems on optimization of architecture parameters are deduced,which refer to the lower bounds of register number and memory *** results on different processor architectures （including Intel Core i7 and Godson-3B） validate the performance *** above investigations were adopted in the development of Godson-3B,which is an industrial *** optimization techniques deduced from our performance model improve the FFT performance by about 40%,while incurring only 0.8% additional area ***,Godson-3B solves the 1024-point single-precision complex FFT in 0.368 μs with about 40 Watt power consumption,and has the highest performance-per-watt in complex FFT among processors as far as we *** work could benefit optimization of other GPPs as well.

关键词： fast Fourier transform (FFT) general-purpose processor (GPP) performance prediction model vector unit DMA

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Tunnelling performance prediction of cantilever boring machine in sedimentary hard-rock tunnel using deep belief network

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Journal of Mountain Science 2023年第7期20卷 2029-2040页

作者： SONG Zhan-ping CHENG Yun ZHANG Ze-kun YANG Teng-tian School of Civil Engineering Xi'an University of Architecture and TechnologyXi'an 710055China School of Civil Engineering Yancheng Institute of TechnologyYancheng 224051China Shaanxi Key Laboratory of Geotechnical and Underground Space Engineering Xi'an 710055China School of Highway Chang’an UniversityXi’an 710064China China Railway Construction Bridge Engineering Bureau Group Co. Ltd.Tianjin 300300China

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 *** 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 *** 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 *** 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 *** 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 > *** 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 *** 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 *** research results have theoretical reference significance for the applicability analysis and mechanical selection of cantilever boring machine for hard rock tunnel.

关键词： Urban metro tunnel Cantilever boring machine Hard rock tunnel performance prediction model Linear regression Deep belief network

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Knowledge-driven material design platform based on the whole-process simulation and modeling

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International Journal of modeling, Simulation, and Scientific Computing 2022年第2期13卷 1-15页

作者： Gongzhuang Peng Tie Li Xiang Zhai Wenzheng Liu Heming Zhang National Engineering Research Center for Advanced Rolling Technology University of Science and Technology Beijing Beijing 100083 P.R.China Shanghai Electro-Mechanical Engineering Institute Shanghai 201109 P.R.China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing 100854 P.R.China Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing 100854P.R.China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854P.R.China National CIMS Engineering Research Center Tsinghua University Beijing 100084P.R.China

In order to realize the agility,collaboration and visualization of alloy material devel-opment process,a product development platform based on simulation and modeling technologies is established in this *** this platform,the whole-process simulation module builds multi-level simulation models based on metallurgical mechanisms from the production line level,the thermo-mechanical coupling field level and the microstructure evolution *** design knowledge management module represents the multi-source heterogeneous material design knowledge through ontology model,including customers’requirement knowledge,material component knowledge,process design knowledge and quality inspection knowledge,and utilizes the case-based reasoning approach to reuse the *** data-driven modeling module applies machine learning algorithms to mine the relationships between product mechanical properties,material components,and process parameters from historical samples,and utilizes multi-objective optimiza-tion algorithms to find the optimal combination of process *** of the developed platform in actual steel mills shows that the proposed method helps to improve the efficiency of product design process.

关键词： Product development knowledge-based engineering multi-scale simulation performance prediction model

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Application of Rough Sets and Artificial Neural Network in Core Enterprise performance prediction

Application of Rough Sets and Artificial Neural Network in C...

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第二十七届中国控制会议

作者： Hu Jian 1,Shi Chengdong 1,2 1.School of Electric and Electronic Engineering,Shandong University of Technology,Zibo 255049,P.R.China 2.School of Business Administration,Xi ’ an University of Technology,Xi ’ an 710048,P.R.China

A predication model of core enterprise performance was proposed based on rough sets and artificial neural network from Knowledge Discovery and Data Mining perspective at ***,the calculation and analysis process of the model were given and *** performance decision-making table and discernable matrix were designed,and the artificial neural network and Back Propagation algorithm（BP network） were put ***,the model was applied into a practical prediction example *** the Balanced Scorecard Index System was reduced and the reduction index was input to the artificial neural network for intelligent training,the predicted sample was input to the trained network,the prediction value of the core enterprise performance was *** prediction result is consistent with the actual result.

关键词： Rough sets Artificial neural network Discernable matrix Core enterprise performance prediction model

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