On Critical-to-quality Characteristics Identification For Complex Products Using Data Mining

Complex product is a kind of product which featured with complexcustomer demand, complex product composition, complex producttechnology, complex product technology and complex project management.In the quality control process of complex product, the validity ofmonitory points measuring quality characteristic determines thecontrollability of product quality. However, with the increase in thequantity of quality control points, on the one hand, the cost of productquality control will increase dramatically; on the other hand, theefficiency of enterprise quality control will drop significantly. Inorder to improve the efficiency of product quality control, it is of greatsignificance to identify Critical-to-quality Characteristics that havea significant impact on product quality, so as to reduce the quantitycontrol points, improve control efficiency and finally cut the businesscosts.In this dissertation, the study of identification ofCritical-to-quality Characteristics was carried out form twoperspectives: balanced data sets and unbalanced data sets.(1) In the balanced data sets with approximate equal sample size ofcomplex product quality category, the information entropy technology ofinformation theory was introduced and information gain was employed todecide the correlation between quality characteristics and theirrespective categories. Thus, the complex interdependent relationswithin product quality characteristics can be bypassed and the measureof importance of quality characteristics can be conducted form a newperspective, so as to identify true product CTQ. An example testify showsthat this method can effectively reduce the dimension of the qualitycharacteristics, improve the efficiency of quality control and controllevel and save a lot of time and cost in the meanwhile. (2) In the real manufacture of complex product, the difference insample size is large within quality category. And the unbalanced dataset brings about greater difficulty in the identification ofCritical-to-quality Characteristics compared with the balanced data set.In this dissertation, three different CTQ recognition methods have beendeveloped from three angles in unbalanced data set. First, improvementshave been made on ReliefF criterion to make category dividing criterionoff set towards most classes, so as to reduce the risk of minority classdata being removed as outliers. Second, base on the introduction ofmodels and algorithms of feature selection, a kind of Wrapper featureselection algorithm has been proposed on the basis of balancedclassification accuracy, thereby reducing the negative impact of theunbalanced data set on the CTQ identification. Third, improve the EMalgorithm. Filtering out the redundancy samples in the unbalanced dataset by clustering so as to build a balanced data set, based on which therecognition of Critical-to-quality Characteristics have been conducted.By this means, the performance of the quality characteristics has beenimproved effectively and error rate of the second kind has been reducedsignificantly.Based on the investigation of complex product manufacturers, thedissertation carried out a detailed analysis and research on the presentbottleneck in quality control--Critical-to-quality Characteristics,and verification has also been made through an example. The study hasa positive reference value on future study of complex product qualitycontrol.