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The Research on Quality Diagnosis and Adjustment in Processing Based on Artificial Intelligence

Author: ZhangXiangGan
Tutor: LiuChangAn
School: Shandong University
Course: Manufacturing systems engineering
Keywords: BP neural network Control Chart Pattern Recognition Quality diagnostic Quality-adjusted Expert system
CLC: TP311.52
Type: Master's thesis
Year: 2011
Downloads: 48
Quote: 1
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Abstract


Product quality formation and throughout the entire product life cycle, enterprises participate in market competition, upon which the survival and development, and product quality in the process is the cornerstone of the final quality of the product. As \The abnormal product quality in the process, only a fraction of the time for quality monitoring and control, while 80% of the time is used to determine the factors that caused the exception of unusual sources and adjust, so in order to meet the needs of the total quality management process diagnostic technologies and systems used in processing quality many academics and businesses a new hotspot. On the basis of the previous quality control studies based on improved BP neural network control chart pattern recognition, and related research on the quality of diagnosis and adjustment process. Main study included three aspects: (1) based on the activation function of adjustable parameters and dynamic threshold improved BP neural network control chart pattern recognition algorithms, and optimize the Monte Carlo process data simulation method, the sample data more with the actual production data identical to the quality characteristics. Iterative formula based on the improved network parameters, the pretreatment of the sample data as input to the neural network identification training and training results for production process control chart pattern recognition. The simple topology improved BP neural network recognition to ensure the accuracy of the identification of the premise, to improve recognition speed, and improve the generalization ability of the neural network. Finally, through computer simulations to verify the feasibility of the proposed algorithm. (2) diagnosis and adjustment method based on fault tree analysis of the quality of the process. First, the rules related to the quality of the machining process of encoding and production knowledge, the system automatically generates abnormal control chart pattern for the top event of the fault tree, fault tree analysis obtain caused by fluctuations in the quality of major anomaly factor subsets. Then, the monitoring results of each quality factor eigenvalue as a rule matching basis, the expert system of automated reasoning and artificial reasoning. Finally, the system quality adjustment mode control charts abnormal diagnostic results, the optimal adjustment programs to improve production timely feedback to the technical staff. The quality of diagnosis and adjustment of expert systems contribute to the rapid diagnostic quality of the production personnel abnormal factors, implementation of quality adjustment programs, and greatly shorten the production cycle. (3) the development of expert system of the turning quality diagnostic and adjustment. VB6.0 development environment and SQL Server 2003 database software, the establishment of the expert system knowledge base and the man-machine interface of the function module, process control chart pattern recognition, control charts abnormal pattern quality diagnosis and adjustment, the expert knowledge base maintenance and machine-learning function.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Software Engineering > Software Development
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