Liping Zhao

A system framework of inter-enterprise machining quality control based on fractal theory

In order to meet the quality control requirement of dynamic and complicated product machining processes among enterprises, a system framework of inter-enterprise machining quality control based on fractal was proposed. In this system framework, the fractal-specific characteristic of inter-enterprise machining quality control function was analysed, and the model of inter-enterprise machining quality control was constructed by the nature of fractal structures. Furthermore, the goal-driven strategy of inter-enterprise quality control and the dynamic organisation strategy of inter-enterprise quality improvement were constructed by the characteristic analysis on this model. In addition, the architecture of inter-enterprise machining quality control based on fractal was established by means of Web service. Finally, a case study for application was presented. The result showed that the proposed method was available, and could provide guidance for quality control and support for product reliability in inter-enterprise machining processes.

A variance change point estimation method based on intelligent ensemble model for quality fluctuation analysis

For multivariable production process, knowing the first time of process really changes (change point) will help to accelerate the location of assignable causes and make measures for process adjustment. So effective estimating the change point is an important way to analyse the quality fluctuation of process. In the present study, an intelligent ensemble model for quality fluctuation analysis is proposed to estimate the variance change point in multivariable process. With the method, the process is decomposed based on moving window analysis, then different types of kernel functions are combined together to form the multi-kernel support vector machine model, which has combined the feature mapping capability of each basic kernel in the new feature space. The particle swarm optimisation is considered to search the optimised multi-kernel parameters. After that, each sub-characteristic is regarded as a pattern to be recognised to determine the change point by using the optimised intelligent ensemble model. Finally, a case study is conducted to evaluate the performance of proposed approach. It reveals that the method could estimate the time of variance change point in continuous production process accurately.

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.

Quality Tracing and Control Information System for Extended Enterprise

With economies tending towards global uniformity, the traditional quality control in a single enterprise has to be innovated. Core competencies or leadership capabilities in quality control have to be strengthened by the complementary capabilities of partners for extended enterprise (E2). So how to establish an economic and effective quality tracing and control information system to trace and control quality of inter-enterprise has becoming one of the studying hotspots currently. In this paper, a quality tracing and control information system for extended enterprise (E2-QTCIS) is explored based on J2EE, and the quality information model of E2 by using product BOM and template technology is established. On this basis, the workflow model of quality tracing and control process and the workflow execution logic of tracing and control quality information nodes are constructed. Finally, a four-layer architecture of E2-QTCIS with network transparency and easy expandability is established. E2-QTCIS can capture all quality information quickly, accurately and wholly, and realize quality tracing and control of E2 dynamically.

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.

A Support Vector Machine Based Multi-kernel Method for Change Point Estimation on Control Chart

Despite the abnormal patterns recognition and mean shift size estimation of control chart signals could provide some evidence for statistical process diagnostics, it do not reveal the real time of the process changes, which is essential for identifying assignable causes and ultimately ensure stability of process. In this paper, a support vector machine based multi-kernel (MK-SVM) method for change point estimation on control chart is proposed. For this purpose, the moving window analysis is introduced to decompose the whole process sequence features into multiple time sub-sequences, and different types of kernel functions are combined together by using kernel method, which is mapped into a new feature space to form the multi-kernel function of SVM. Then each characteristic of the sub-sequences is regarded as a determined pattern to be recognized through the proposed model. We use the cross-validation method to search the optimized parameters of MK-SVM. Multiple sets of experiments are used to verify this method. Finally, a case study about the coating process of production lines is conducted to evaluate the performance of the proposed approach, results reveal that the proposed scheme is able to effectively estimating the time of change-point and outperform the commonly used approaches.

Research about Deformations Identification for Globoidal Cam Machine Based on Multi-Body System Theory

Deformation error caused by cutting heart and external force is one of the key factors influencing machining precision in machining process. Aiming to identify the machine deformation which has the most important influence on machining precision, an identification method for sensitive error and deformation based on multi-body system theory has been proposed, which lay the basis of stiffness distribution of a machine tool and structure optimization of parts. At the end of this paper, the effective of this method is verified by using the machine tool for globoidal cam.

Research about Dynamic Performance Prediction for Layout Structure of Globoidal Cam Machine

Aiming to the layout structure design and performance prediction for globoidal cam machine, a dynamic performance prediction method for machine layout structure is proposed in this paper. With the method, the motion transmission and layout structure are determined based on the mapping rules between function and structure. The prediction model for dynamic performance is established based on BP neural network, which is used to optimize the dynamic performance of layout structure for globoibal cam machine.