Dong Juan Xue

Inventory Classification and Management Strategy of Components and Parts in Assembly Workshops

Material inventory management plays an increasingly important role in modern operations management within manufacturing enterprises. And a multi-attribute classification model has been put up based on the application of the decide tree model and fuzzy artificial neural network. First the material inventory styles are classified. Then a decision tree model is defined based on inventory classification result. The value of the node is decided by Fuzzy Neural Network if multi-attribute decision is needed and material inventory strategy can be decided with the classification tree and inventory strategy table. In the end, the implementation of the model in a manufacturing enterprise resource plan system is presented.

Research of Knowledge Management during Products Design Process

To improve the knowledge management during product design process, a new model of knowledge composition and knowledge activity is constructed within or between organizations and persons. And based on the analysis of knowledge acquisition, transfer and innovation, the strategy for the implement of knowledge management project is proposed. At last the functions and structures of a software system are provided that supporting the knowledge management for product design in an manufacturing enterprise.

An adaptive Genetic Algorithm for the Flexible Job-shop Scheduling Problem

Aiming at the stage-related characteristics in solving process of the Flexible Job-shop Scheduling Problem (FJSP) and the evolution characteristics of Genetic Algorithm (GA), an Adaptive Genetic Algorithm (AGA) is presented in this paper, combined with existing GA problems for solving FJSP. In the solving process, AGA automatically adjusts selection rate Ps, crossover rate Pc, mutation rate Pm and other operation parameters according to the genetic generation. Furthermore, it automatically changes scope of action of crossover operator and mutation operator on the chromosome. In the AGA solution scheme, linear interpolation method is used to realize automatic change of operation parameters from initial stage to middle stage; extended power function implements adaptive adjustment in the function scope of operators. Meanwhile, coding method is adjusted correspondingly for realizing operator adaptation. Instance simulation verifies that the FJSPs own characteristics are utilized in its solution by using AGA, which overcomes traditional GAs limitation. Such a method has a relatively high search power during the whole solution process, especially in the end of the process. And solving efficiency and precision are improved greatly.

Research on Quality Control of Motor Shell Coating Process

SPC method is a process controlling tool with the aid of mathematical method. Define, Measure, Analysis, Improvement and Control is the implementation processes of quality management. Combined with the two methods, the quality fluctuation in the production process could be controlled effectively and qualified products rate could be enhanced excellently. In this paper, the film thickness problem of motor shell coating production line has been studied based on statistical process control and DMAIC processes. The Pareto diagram, cause and effect diagram, operation chart are used to inspect production defects in the process. At same time, the factors leading to product defects are determined. Then, DOE method is applied to find the best parameters of electrophoresis coating process. SPC method is adopted to monitor the improved film thickness. The results show that quality level of coating process has improved greatly.

Research on Multi-Level Context-Aware Material Data Model within Manufacturing Enterprises

Context-aware applications can sense and explore the context of production processes to provide proper and useful services to managers in workshop. And materials data integration and control are one of core competences concerning with the development of mass data application in manufacturing enterprises. In this paper, a context-aware materials data integration meta model is presented which is incorporating an advanced materials demand mapping and decomposition mechanism to support fine-grained materials data in the right time for the provision of materials services, based on the analysis of the application context involving the key points and relationships in their manufacturing processes. In this model, materials data integration is controlled by the demand state transitions at different levels including workflow levels, control levels and data levels, which incorporates dynamic context information by checking the context constraint associated with each application task at the time of the specific request submission. Finally, a case is given which is implemented in a complex equipments enterprise.

Research of Parts Inventory Classification and Management Strategy in Manufacturing Enterprises

The correct inventory strategy is important to control reasonable inventory within manufacturing enterprises. And a new material inventory strategy is proposed based on the classification scheme according to the prosperities. First the material inventory styles are classified. Then a decision tree model is defined based on inventory classification result. The value of the node is decided by Fuzzy Neural Network if multi-attribute decision is needed and material inventory strategy can be decided with the classification tree and inventory strategy table. In the end, the implementation of the model in a manufacturing enterprise resource plan system is presented.

Material Data Integration Model Based on Context-Aware within Manufacturing Enterprises

Materials data integration is one of core competence concerning with the development of mass data application in manufacture enterprises. In this paper, a context-aware materials data integration meta model is presented which is incorporating an advanced materials demand mapping and decomposition mechanism to support fine-grained materials data in the right time for the provision of materials services, based on the analysis of the application context involving the key points and relationships in their manufacturing processes. In this model, materials data integration is controlled by the demand state transitions at different levels including workflow levels, control levels and data levels, which incorporates dynamic context information by checking the context constraint associated with each application task at the time of the specific request submission. Finally, a case is given which is implemented in a complex equipments enterprise.

Dynamic Context-Aware System Based on Multi-Level Material Data Control Model

Context-aware applications can sense and explore the context of production processes to provide proper and useful services to managers in workshop. Firstly, a multi-level ontology model of materials is found which is expressed in OWL as an ontology collection of describing places ages and events and associated properties among them. Secondly, a multi-level context-aware control model and its mechanism are produced. In this model, data integration is controlled by the demand state transitions at different levels including workflow levels, control levels and data levels, which incorporates dynamic context information. And context is classified based on the analysis of demand into five main categories, which is further categorized as simple condition and composite condition to form a multi-level context. And the data integration model is implemented dynamically by checking the context constraint associated with each application task at the time of the specific request submission. Finally, a case study is developed within a manufacturing enterprise to show the benefits of the model.

A Modified Adaptive Genetic Algorithm for the Flexible Job-Shop Scheduling Problem

Combined with the stage-related characteristics in solving process of the Flexible Job-shop Scheduling Problem (FJSP) and the evolution characteristics of Genetic Algorithm (GA), a modified Adaptive Genetic Algorithm based on iterative generation and analysis of fitness values distribution is presented in this paper, which has both methods advantages. Instance simulation verifies that the FJSPs own characteristics are utilized in its solution by using the modified AGA, which overcomes traditional GAs limitation that initial stage of evolution is early and random search of medium-late stage is slow. Such methods are verified to accelerate convergence process, enhance searching efficiency and solving precision as well as avoid low efficiency and local optimum.

Modeling and Scheme Generation of Dynamic Flexible Job-Shop Scheduling

Aiming at uncertain information and dynamic characteristic during flexible job-shop scheduling process, some kind of dynamic scheduling method for flexible job-shop scheduling problem (FJSP) is put forward based on real-time adjustment. A dynamic simulation solution mode framework is presented for FJSP. This framework is inspired by adaptive control, combined with the robust scheduling and foreseeing scheduling. It has both advantages of such two scheduling methods, and its stable and highly efficient. Preliminary scheme generation method based on foreseeing dynamics scheduling is introduced then. Foreseeing function is realized by fault-handling algorithm and dynamic simulation solver on the basis of Adaptive Genetic Algorithm (AGA).