K. Chen’s scientific contributions

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Publications (3)


Machining error source tracing method based on Bayesian networks
  • Article

July 2010

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5 Reads

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1 Citation

L.-J. Li

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J.-M. Gao

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K. Chen

To enhance the ability of methods to interpret and infer uncertain information in the source tracing of machining errors, Bayesian Networks was introduced. The modeling method and inference procedures of machining errors source tracing based on Bayesian Networks were presented. On one hand, actual machining conditions and machining error phenomena were taken as complex evidences; on the other hand, by using Bayesian Networks inference, the probability of each cause was obtained and the maximum probability path was searched; and then a diagnosis method for uncertain information was provided. At last, a case study for the machining error source tracing of the connection holes on the rotor flange was reported to verify the proposed approach.


Method of quality management by objectives for product manufacturing process

June 2009

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66 Reads

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3 Citations

It was difficult to rationally assign Quality Management By Objectives (QMBO) to relevant departments, and it was difficult to conduct evaluation within and after objective execution process. To deal with this problem, the establishment, decomposition and evaluation of quality objectives were studied. Firstly, the product quality objectives were established and selected by using the method of Quality Function Deployment (QFD). Secondly, the product quality objectives were gradually allocated to various departments of enterprise by adopting Analytic Hierarchy Process (AHP) based on difference compensation of relative quality factors. Thirdly, AHP based on fuzzy evaluation was used to deal with the quantitative and qualitative factors and to comprehensively evaluate the realization of the objectives. Finally, to satisfy the practical requirements of an aircraft manufacturing enterprise, based on B/S mode, a computer aided QMBO system oriented to the product manufacturing process was developed. The result of application proved that it was effective to guarantee the realization of enterprise quality objectives.


Research on Quality management process improvement based on workflow mining technology

April 2006

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14 Reads

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2 Citations

L. Chen

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J.-M. Gao

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F.-M. Chen

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[...]

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To satisfy improvement requirements for quality management system, an idea based on workflow-mining technology to support quality management process improvement was presented. This process was divided into three steps, workflow log preprocessing, process activity-control graph reconfiguration and generation of activity transfer conditions. Among them, process activity-control graph reconfiguration was the core step. By modeling the activity execution as an interval, the relationship between two activities' execution durations could be categorized into three kinds, as disjoint, overlapped and contained, and a heuristic algorithm was designed based on this relationship to search activities' sequential relationships contained in workflow log, so that the quality management process model comprising complicated Plan-Do-Check-Action (PDCA) loop structures was reconstructed. Finally, process improvement of external audit reports treatment was used as a case study to illustrate the proposed approach and its performance.

Citations (1)


... In recent years, with the development of deep learning technology, many researchers have applied it to the manufacturing industry, such as online quality detection, error tracing, error prediction, fault diagnosis and other aspects [6,7]. Based on Bayesian network, Li et al. proposed a method for tracing the causes of machining error in mechanical production [8]. Liu et al. proposed an error traceability method based on signal analysis technology, and verified the effectiveness by wavelet transform algorithm [9]. ...

Reference:

Research on Error Tracing of Synchronizer Gear Hub Based on Variable Step Fa-Rbf Neural Network
Machining error source tracing method based on Bayesian networks
  • Citing Article
  • July 2010