Contributions To Several Issues Of Intelligent Quality Control And Diagnosis Of The Manufacturing Process

Product quality is the foundation of modern enterprises’ survival, development and increase of itscompetitiveness in market competition. Quality control and diagnosis of the manufacturing processplays a more important role in ensuring product quality. Quality control of the manufacturing process isthe starting point in the continuous quality improvement cycle, and while quality diagnosis of themanufacturing process provides direction for continuous quality improvement. By means of qualitydiagnosis, it can substantially help to identify the process abnormalities, further facilitate correctiveaction, and eventually bring the process back in control. With the advance of the modernization andcomplexity of manufacturing process, more stringent and higher requirements are generated for processcontrol and quality diagnosis of manufacturing process. It is very difficult to fulfill these requirementsonly using the traditional technologies of statistical process control and diagnose.In this dissertation, four core problems standing in the way of quality control and diagnosis of themanufacturing process are investigated, which includes a lack of statistical and economic designmethods for Shewhart control charts that are effective and cost-efficient, a lack of effective anduniversal method for intelligent quality control of the manufacturing process, a lack of real-time andaccurate methods for simultaneous recognizing of abnormal process mean and variance control chartpatterns, and a lack of precise and convenient quality diagnosis method for the multistagemanufacturing process with dependent quality characteristic.The main contributions of this dissertation are summarized as follows:(1) A statistical and economic design method for Shewhart control charts is proposed.To address the issue in the design of Shewhart control charts that the statistical design of a controlchart requires high cost inputs, while the economic design provides disappointing statistical properties,and both of them only consider in-control parameters but without taking into account any historicalknowledge related to the process shifts, this research constructs a statistical-economic model todetermine the optimal parameters using previous process shifts, which can be extracted from the fieldoperation. Experimental results show that the proposed method can not only reduce the cost, but alsoinsure statistical properties within the required intervals, meanwhile correctly reflect the actual processconditions. In order to solve the optimization problem in the statistical-economic design of controlcharts, this study proposes particle swarm optimization-based single and multiple objectiveoptimization algorithms. Since the Pareto-optimal set can be extremely large in multi-objective problems, a multi-objective decision support method that combines clustering analysis andPseudo-weight coefficient vector approach is designed for multi-objective economic-statistical designof control charts. Besides, the aforementioned method is successfully applied in the single andmultiple-objective statistical-economic design of control charts for diameters of the bearings.(2) A hybrid intelligent learning model-based method for quantitative quality control ofmanufacturing process is proposed.To address the issue in the implementation of Shewhart control charts that control charts fail toperform quantitative assessment of process behavior and tend to encounter the increasing risk of typeⅠand typeⅡerrors when the process deviates away from the normal distribution or exhibits some degreeof autocorrelation, a hybrid intelligent learning model is developed. The proposed hybrid intelligentlearning model is comprised of two intelligent learning-based sequential Modules: ModuleⅠandModuleⅡ. ModuleⅠemploys a self-organizing map (SOM) based quantization error control chart todetect process mean and/or variance changes and to provide a quantitative assessment of the severity ofthe process abnormalities. ModuleⅡemploys a discrete particle swarm optimization based selectiveback-propagation neural network (DPSOSEN-BPN) to identify the category of signals (i.e., meanabnormality, variance abnormality or mean or variance abnormalities) that are judged as out-of-controlsignals by quantization error control chart of ModuleⅠ. Experimental results show that the developedself-organizing map (SOM) based quantization error control chart of ModuleⅠis better than thecommonly used approaches in literature in detecting process mean and/or variance changes and yetable to make a quantitative assessment of the severity of the process abnormalities, the developedDPSOSEN-BPN of ModuleⅡ has better generalization ability and can effectively recognize not onlysingle mean or variance abnormality but also mixed abnormalities in which mean and varianceabnormality exist concurrently. Besides, the proposed hybrid intelligent learning model is successfullyapplied in detecting process changes and identifying the categories of process abnormality for a whitemillbase dispersion process.(3) A method for the simultaneous recognition of process mean and variance control chart patternsis proposedTo address the issue that process mean and variance control charts are usually implementedtogether and that these two charts are not independent of each other and while a specific pattern of themean and variance charts can be associated separately with different problems, this study proposed aselective neural network ensemble based model for the simultaneous recognition of both mean andvariance control chart patterns. The numerical results indicate that the proposed model can effectivelyrecognize not only single mean or variance control chart patterns but also mixed control chart patterns in which mean and variance control chart patterns exist concurrently. Empirical comparisons also showthat both direct data and selected statistical features extracted from the process that are employed as theinputs of neural network yield better performance than previous works using raw data or statisticalfeatures only. Besides, the proposed method is successfully applied in abnormal control chart patternrecognition for a white millbase dispersion process.(4) A quality diagnosis method for the multistage manufacturing process with dependent qualitycharacteristic is proposedTo address the issue that the Shewhart control chart can show the indication of abnormality, but itcan not tell us what and where the abnormality is, this study proposes a quality control and diagnosismethods for the multistage manufacturing process with dependent quality characteristic. Experimentalresults show that the proposed method can not only diagnose the effect of preceding stage on the nextstage, discriminate the quality responsibility between the preceding and next stages, but also identifywhich stages are key ones for improving the quality of the finished product of a production line.Besides, the proposed method is successfully applied in quality diagnosis for the mechanical processingof boiler roller with multiple dependent process stages.