Configuration Selection for Reconfigurable Manufacturing Systems by Means of Characteristic State Space

The configuration selection for reconfigurable manufacturing systems（RMS） have been tackled in a number of studies by using analytical or simulation models. The simulation models are usually based on fewer assumptions than the analytical models and therefore are more wildly used in modeling complex RMS. But in the absence of an efficient gradient analysis method of the objective function, it is time-consuming in solving large-scale problems by using a simulation model coupled with a meta-heuristics algorithm. In this paper, a new approach by means of characteristic state space is presented to improve the efficiency of the configuration selection for an RMS. First, a characteristic state equation is set up to represent the input and the output resources of each basic activity in an RMS. A production process model in terms of matrix equations is established by iterating the equations of basic activities according to the resource flows. This model introduces the production process into a characteristic state space for further analysis. Second, the properties of the characteristic state space are presented. On the basis of these properties, the configuration selection in an RMS is considered as a path-planning problem, and the gradient of the objective function is computed. Modified simulated annealing（SA） is also presented, in which neighborhood generation is guided by the gradient to accelerate convergence and reduce the run time of the optimization procedure. Finally, several case studies on the configuration selection for some actual reconfigurable assembly job-shops are presented and compared to the classical SA. The comparison shows relatively positive results. This study provides a more efficient configuration selection approach by using the gradient of the objective function and presents the relevant theories on which it is based.

A Reconfigurable Manufacturing System for In-site Machining Component with Large Scale

To cope with various unpredictable changes in large scale parts,the concept of reconfigurable manufacturing system (RMS) for machining these components is presented.Considering with large-size space measurement and the fixed-free manufacture mode,an automatically localizing machining method for large scale part is studied in this paper,and the architecture of the RMS for machining large scale parts is proposed.According to the method and structure,the automatically localizing model is established.The theoretical analysis and simulation examples demonstrate the feasibility and validity of the proposed method,and the results indicate that the method is suitable and effective for machining large scale components in significant scientific projects.

Reconfigurable manufacturing systems： Principles, design, and future trends

Reconfigurable manufacturing systems （RMSs）, which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are cost- effective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.

Cooperative Optimization of Reconfigurable Machine Tool Configurations and Production Process Plan

The production process plan design and configurations of reconfigurable machine tool （RMT） interact with each other. Reasonable process plans with suitable configurations of RMT help to improve product quality and reduce production cost. Therefore, a cooperative strategy is needed to concurrently solve the above issue. In this paper, the cooperative optimization model for RMT configurations and production process plan is presented. Its objectives take into account both impacts of process and configuration. Moreover, a novel genetic algorithm is also developed to provide optimal or near-optimal solutions： firstly, its chromosome is redesigned which is composed of three parts, operations, process plan and configurations of RMTs, respectively; secondly, its new selection, crossover and mutation operators are also developed to deal with the process constraints from operation processes （OP） graph, otherwise these operators could generate illegal solutions violating the limits; eventually the optimal configurations for RMT under optimal process plan design can be obtained. At last, a manufacturing line case is applied which is composed of three RMTs. It is shown from the case that the optimal process plan and configurations of RMT are concurrently obtained, and the production cost decreases 6.28% and nonmonetary performance increases 22%. The proposed method can figure out both RMT configurations and production process, improve production capacity, functions and equipment utilization for RMT.

Genetic-algorithm-based balanced distribution of functional characteristics for machines

In order to make reconfigurable manufacturing system （RMS） adapt to the fluctuations of production demand with the minimum number of reconflgurations in its full life cycle, we presented a method to design RMS based on the balanced distribution of functional characteristics for ma- chines. With this method, functional characteristics were classified based on machining functions of cutting-tools and machining accuracy of machines. Then the optimization objective was set as the to- tal shortest mobile distance that all the workpieces are moved from one machine to another, and an improved genetic algorithm （GA） was proposed to optimize the configuration. The elitist strategy was used to enhance the global optimization ability of GA, and excellent gene pool was designed to maintain the diversity of population. Software Matlab was used to realize the algorithm, and a case study of simulation was used to evaluate the method.

The Realization of Mass Customization Using Modularization Method

In the view of the comparison of Mass Customization ( MC) with Mass Production and Customization Production, the objectives of MC are analyzed. It is pointed out that the core objectives of MC are to realize in dividuation customization, low cost, quick response to market demands. The modul arization theory is simply introduced. Based on the characteristics of modular ization, the mechanism of realizing MC with modularization is analyzed. The in dividuation customization can be realized with the different combinations of mod ules. The low cost can be realized with the scale economy and the category econo my of modules. The quick response can be realized with standard modules and its interfaces. So, the modularization is a kind of effective method in realizing MC . The modularization for MC is a systems engineering. With product modularized, production organization and management and manufacturing equipment will be chang ed. In addition, the paper also proposes a Mass Customization production model w hich is based on modularization. This Mass Customization production model is con sisted of modularization of product design, specialization of manufacturing, Vir tual Enterprises based on modularizing enterprises, and modularizing manufacturi ng equipment. The module design for MC, modularizing enterprises, and reconfigur able automation manufacturing equipment are discussed, and it is pointed out tha t they are the important supports for MC.

A novel approach for part family formation using K-means algorithm

The reconfigurable manufacturing system （RMS） is the next step in manufacturing, allowing the production of any quantity of highly customized and complex parts together with the benefits of mass production. In RMSs, parts are grouped into families, each of which requires a specific system configuration. Initially system is configured to produce the first family of parts. Once it is finished, the system will be reconfigured in order to produce the second family, and so forth. The effectiveness of a RMS depends on the formation of the optimum set of part families addressing various recon figurability issues. The aim of this work is to establish a methodology for grouping parts into families for effective working of RMS. The methodology carried out in two phases. In the first phase, the correlation matrix is used as similarity coefficient matrix. In the second phase, agglomerative hier archical Kmeans algorithm is used for the parts family for mation resulting in an optimum set of part families for reconfigurable manufacturing system.