Optimistic and pessimistic reliability performance assessment approaches for reconfigurable manufacturing systems

Reconfigurable manufacturing systems can change the structure of the systems to cope with manufacturing market requirements. Reconfigurability brings about new challenges for reconfigurable manufacturing systems’ development. In order to describe, analyze, and verify the reconfiguration of reconfigurable manufacturing systems, a reconfigurable manufacturing system formal model is proposed from the perspective of multi-agent systems, in which two complementary formalisms, namely, object-oriented Petri nets and π-calculus, are employed as formalisms. The object-oriented Petri nets are utilized to model the initial structure as well as system behaviors of reconfigurable manufacturing systems, while π-calculus is adopted to describe the reconfiguration of reconfigurable manufacturing systems. Some supporting tools of Petri nets and π-calculus can be used to analyze, verify, and validate the reconfigurable manufacturing system formal model. The reconfigurability mechanism and consistency of reconfigurable manufacturing systems can also be analyzed by π-calculus.

Reconfigurable manufacturing systems are complex systems that are prone to malfunctions and performance decay. Thus, such systems need to be safeguarded against quality issues and decline in production efficiency to ensure the optimal health of machines. The product quality and health of a reconfigurable manufacturing system can be analyzed by using the diagnosability characteristic. This study examines the diagnosability characteristic in a multi-stage reconfigurable manufacturing system. The aim is to understand the impact of time-based diagnostics on the functionality performance of a reconfigurable manufacturing system and the level of inventory used during production. The diagnosability is analyzed regarding product variation and system diagnosability. A mathematical model is proposed, and it is subsequently applied in deterministic and stochastic settings. The deterministic setting is examined through a set of two problem-specific heuristics. The stochastic setting, subject to the gamma process, is examined by using a simulation-based optimization approach. The results suggest that the use of line replacement units can restore a reconfigurable system to optimal functionality, reduce the level of inventory, and complete production in a minimum time at the expense of additional cost. These findings apply to the context of healthcare emergency response systems, reconfigurable supply chains, reconfigurable integrated manufacturing systems, etc. Finally, a conclusion and future research avenues are provided.

A reconfigurable manufacturing system (RMS) is designed for rapid adjustment of production capacity and functionality in response to new market conditions and new process technology. It has several distinct characteristics including modularity, integrability, customisation, convertibility and diagnosability. There are a number of key interrelated technologies that should be developed and implemented to achieve these characteristics. This paper examines and identifies these technologies. After a brief description of the RMSs and their goals, aspects of reconfiguration (reconfigurable system, software, controller, machine, and process) are explained; this provides one with a better understanding of the enabling technologies of RMSs. Some of the issues related to the technology requirements of RMSs at the system and machine design levels, and ramp -up time reduction are then explained. The paper concludes with descriptions of some of the future research directions for RMSs.

An Overview of Performance Measures in Reconfigurable Manufacturing System

A multi-objective reliability optimization for reconfigurable systems considering components degradation

Modularity and Convertibility Characteristics of Reconfigurable Manufacturing System : An Approach for Operations Management

Globalization and mass customization are demanding a higher level of productivity. The relevance of modelling approaches to the study and design of reconfigurable manufacturing system (RMS) is widely claimed to achieve the highest productivity. Principally, reconfigurability in manufacturing systems should support the changeability with precisely the production capacity and functionality needed and exactly when needed. Simulation of such reconfigurable systems has become more and more difficult with the increasing complexity of system requirements. In spite of the promising methodology for designing RMS, an effective framework that bridges the gap between conceptual modelling level process and simulation level process is still a major challenge for Scientist. For this reason, we propose in this paper a generic framework especially designed for building and running executable agent-based models of RMS. This framework relies on SysML (Systems Modelling Language) models specifications, the holonic system techniques and multi-agent system in order to generate executable models of RMS. The considered case study for this paper is based on a steel converter process. Results showed an increase in the productivity rate after simulating the reconfigurability test cases through the developed agent-based models.

A systematic approach for responsiveness assessment for product and material flow in reconfigurable manufacturing system (RMS)

Reconfigurable manufacturing systems (RMS) is a new manufacturing paradigm aiming at providing exactly functionality and capacity needed and exactly when needed. Reconfiguration is the main method to achieve this goal. But, the reconfiguration is an interruption to production activities causing production loss and system ramp-up problem and the ‘exact functionality’ may increase the reconfiguration efforts and aggravate the production loss and the ramp-up time. Therefore, a special RMS – delayed reconfigurable manufacturing system (D-RMS) is proposed to promote the practicality of RMS. Starting from the RMS built around part family with the characteristic of delayed differentiation, whose reconfiguration activities mainly occur in the latter stages of manufacturing system and the former stages have the potential to maintain partial production activities to reduce production loss during reconfiguration. Inspired from this, the basic structure of RMS is divided into two subsystems, subsystem 1 is capable of maintain partial production with a certain more functionality than needed, subsystem 2 reconfigure to provide exactly functionality and capacity of a specific part exactly when needed. And then, the benefits of D-RMS are analysed from inventory and ramp-up time aspects. Finally, a case study is presented to show the implementation process of D-RMS and validates the practicability of D-RMS.

A process-driven computing model for reconfigurable semiconductor manufacturing

Reliability has always been an important factor for any manufacturing companies. An appropriate level of reliability in a manufacturing system could mean less maintenance cost, higher efficiency and steadier production state. Because each machine in a manufacturing system has its individual level of reliability, reliability on the system level would depend largely on how the machines are configured. In traditional efforts on reconfigurable manufacturing system (RMS) reliability assessments, mean time between failure (MTBF) is usually adopted as reference index of reliability. Also, in existing research efforts of applying reliability analysis in manufacturing system, reliability is merely a single and over-simplified index in the framework. But there exist various forms of reliability inside a RMS, and the complexity of this concept would keep increasing with the development of new technology in manufacturing industry. To analyze reliability in RMS in a more comprehensive way, we built conceptual maps as first step for research agenda -- from the perspective of general layer, reliability analysis and RMS.

Digital twin monitoring and simulation integrated platform for reconfigurable manufacturing systems

To address the problem of how to identify the best time to implement reconfiguration for the reconfigurable manufacturing system (RMS), a dynamic complexity-based RMS reconfiguration point decision method is proposed. This method first identifies factors that affect RMS dynamic complexity (including both positive and negative complexity) at the machine tool and manufacturing cell levels. Next, based on information entropy theory, a quantitative model for RMS dynamic complexity is created, which is solved via state probability analysis for processing capability and the processing function. This model is combined with cusp catastrophe theory to establish an RMS reconfiguration decision model. Both positive and negative complexity are control variables for cusp catastrophe. Cusp catastrophe’s state condition is used to identify RMS state catastrophe at the final stage of production. This catastrophe point is the RMS reconfiguration point. Finally, the case study result shows that this method can effectively identify the RMS state catastrophe moment so that system reconfiguration is implemented promptly to improve RMS’s responsiveness to the market.

Layout evolution effort for product family in Reconfigurable Manufacturing System design

Analysis of a linear walking worker line using a combination of computer simulation and mathematical modeling approaches