Guangzhou Diao

A dynamic quality control approach by improving dominant factors based on improved principal component analysis

Process variables in manufacturing process are critical to the final quality of product, especially in continuous process. Their abnormal fluctuations may cause many quality problems and lead to poor product quality. Against this background, this paper proposes a dynamic quality control approach by improving dominant factors (DFs) based on improved principal component analysis (iPCA). Firstly, the generation of iPCA is illustrated to identify the DFs which lead to quality problems. Then, a quality prediction model for improving DFs is proposed based on modified support vector machine (SVM). An incremental weight is introduced in SVM to improve its sparsity and increase the accuracy of quality prediction. Thus, the product quality can be guaranteed by controlling the DFs dynamically. Finally, a case study is provided to verify the feasibility and applicability of proposed method. The research is expected to provide some guidance for continuous process.

A weighted-coupled network-based quality control method for improving key features in product manufacturing process

There are some complicated coupling relations among quality features (QFs) in manufacturing process. Generally, the machining errors of one key feature may cause some errors of other features which are coupled with the key one. Considering the roles of key QFs, the weighted-coupled network-based quality control method for improving key features is proposed in this paper. Firstly, the W-CN model is established by defining the mapping rules of network elements (i.e. node, edge, weight). Secondly, some performance indices are introduced to evaluate the properties of W-CN. The influence index of node is calculated to identify the key nodes representing key features. Thirdly, three coupling modes of nodes are discussed and coupling degrees of key nodes are calculated to describe the coupling strengthen. Then, the decoupling method based on small world optimization algorithm is discussed to analyze the status changes of key nodes accurately. Finally, a case of engine cylinder body is presented to illustrate and verify the proposed method. The results show that the method is able to provide guidance for improving product quality in manufacturing process

A Dynamic Process Adjustment Method Based on Residual Prediction for Quality Improvement

Dynamic process adjustment is an important way for improving product quality in industry production process. Focusing on the process monitoring and feedback adjustment, a residual prediction method for quality improvement is proposed in this paper. This method deals with the problem of dynamic process adjustment in three steps: 1) definition of adjustment rules; 2) building of residual series model; and 3) prediction of adjustment amount, respectively. First, the cost function and quality loss are combined to define the adjustment rules, which is used for judging whether the process should be adjusted. Second, a multivariate residual series model is built to illustrate the time series between input variables (technological parameters) and output variables (quality indices). Third, the double-order weights are introduced to support vector machine to build a prediction model for predicting the adjustment amount of controllable variables. In this way, the adjustment decisions can be made and conducted to realize the dynamic process adjustment. At last, to demonstrate the practical usefulness of the proposed method, a case study about coating process of purifier carrier is provided to validate its effectiveness. The result shows that the proposed method has good performance for industry application.

The Process Quality Control Method Based on Coupling Machining Sensor Network

To monitor the dynamic changes of process quality and reduce the quality fluctuation in machining process, a process quality control method based on coupling machining sensor network (CMSN) is proposed to improve product quality. The advantage of CMSN is to combine the complex network with sensor technology. The purpose of this paper is to explore influence of coupling relationships between machining errors on the product quality by analyzing the stability of CMSN. Firstly, the mapping rules between machining process and network elements are provided to construct the topological model of CMSN. Next some performance indices of sensor nodes are defined and calculated to explore the self-organization stability of CMSN so that the appropriate sensor configuration can be selected to ensure the local stability of machining process. On this basis, the whole stability of CMSN is investigated by analyzing the nodes coupling so that the error accumulations are analyzed to improve product quality. Finally, a case study is provided to verify the feasibility of proposed method, in which Monte Carlo simulation is used to produce required quality data. The whole stability of CMSN for blade machining is discussed. It is expected that the proposed method can provide some guidance for machining process.

Research About the Method of Sensitivity Analysis and Quality Control Based on LS-SVM

Focusing on the real-time quality control in machining process, sensitivity analysis method based on LS-SVM was proposed in this paper to control the quality of products. The relationship between machining quality and influence factors is built by LS-SVM (least squares support vector machine). Sensitivity and contribution rate are calculated in sensitivity analysis. The quality of products can be controlled by controllable factors according to the result of sensitivity analysis, and the control effect can be evaluated by quality loss function. The method verification was conducted by a simulation example. At the end of this paper, a case about globoidal cam was presented to verify the feasibility and accuracy.

A Method for Identifying Error Sources in Complex Assembly Process Based on Multi-Level Network

In complex assembly process, there are many error sources which influence the final product quality, so the identification and compensation of error sources are the important ways to improve product quality. The purpose of this paper is to identify the error sources by establishing the relationships between assembly errors and product performance. Firstly, a new concept of multi-level network is proposed to describe the complexity of product structure. Then the multi-level assembly network (MLAN) model is established and its self-organization stability is discussed to explore the influences from assembly errors on product performance. The sensitive fluctuation index (SFI) is defined and calculated to identify the error sources in assembly process. Based on this, the error sensitivity is discussed so that measures for error compensation are made to improve the assembly quality of product. Finally, an assembly experiment about blade rotation is conducted to verify the feasibility of proposed method.Copyright

The Dynamic Quality Control Method Based on Similar Weighted Network and Its Application

To satisfy the requirements of dynamic quality control in machining system, considering the acquisition and analysis of quality data, process statuses assessment and dynamic feedback control, the dynamic quality control method is proposed based on similar weighted network. With the method, the multiple residual analysis is proposed to eliminate the autocorrelation via data, which provides reliable data for statuses assessment. In addition, the process statuses model of work piece is established to analyze the similarity between any two statuses, and the statuses are mapped as nodes to construct similar weighed network for classifying the process statuses. Then the quality control strategy for each classification is formulated in strategy database. By this, the dynamic quality control method of analysis-assessment-adjustment is encapsulated in machining system to improve the quality. At last, a case application of honing process is established to verify the method.