Manufacturing Cell Simulation Research Articles

ICTを活用したものづくり：設備シミュレーション技術

ICTを活用したものづくり：設備シミュレーション技術

Human Error Simulation of Manufacturing Cell Based on Human-Machine Integrated Model

In order to analyze human error how to have effect on manufacturing cell and improve manufacturing efficiency from operators’ point of view, manufacturing cell human error simulation model based on the principle of human-machine integrated is proposed firstly. The realized process of manufacturing cell simulation model is detailed described. After that, human behaviors in manufacturing cell were analyzed and realization mechanisms of main model were introduced. Finally, applied above model and theory in a motorcycle manufacturing cell and simulated various human errors. The simulation result shows that human errors have significant effect on evaluating manufacturing cell performance and the influence degree increased along with the possibility of human make errors. As a result, different production mode should be adopted according to different configuration scheme of organization and personnel stability.

S141023 外観検査向け設備シミュレーションの研究 : 第三報 仮想環境内キャリブレーションの提案([S14102]生産システムの新展開(基礎・理論) (2))

Recently, the automated visual inspections using Robot are getting more important. In our research, the manufacturing cell simulation for the automated visual inspection using Robots has proposed and developed to reduce the lead-time for setting the automated visual inspection. However the usages of the virtual images which are created by the simulation, were not technically cleared. In this paper, the creation process and usage process of the virtual images were investigated and the problems for the difference between the virtual images and the real images through the processes were summarized. The virtual calibration method was proposed to solve the problems. The case studies were carried out to evaluate effectiveness for the proposed calibration method.

211 外観検査向け設備シミュレーションの研究 : 第一報 基本システムの提案(OS2-3 先進システムとシミュレーション)

In this research, we focus on the manufacturing cell simulation for automated visual inspection using robots. In this paper, we firstly describe current problems to establish the automated visual inspection using robots. In order to solve the problems, the manufacturing cell simulation for automated visual inspection using robots is proposed. Seven requirements for the simulation are defined. Five functions to implement the simulation and the fundamental system structure are proposed and implemented. Finally a case study was carried out with cooperation.

Simulation Environment Synchronizing Real Equipment for Manufacturing Cell

Recently, manufacturing industries face various problems such as shorter product life cycle, more diversified customer needs. In this situation, it is very important to reduce lead-time of manufacturing system constructions. At the manufacturing system implementation stage, it is important to make and evaluate facility control programs for a manufacturing cell, such as ladder programs for programmable logical controllers (PLCs) rapidly. However, before the manufacturing systems are implemented, methods to evaluate the facility control programs for the equipment while mixing and synchronizing real equipment and virtual factory models on the computers have not been developed. This difficulty is caused by the complexity of the manufacturing system composed of a great variety of equipment, and stopped precise and rapid support of a manufacturing engineering process. In this paper, a manufacturing engineering environment (MEE) to support manufacturing engineering processes using simulation technologies is proposed. MEE consists of a manufacturing cell simulation environment (MCSE) and a distributed simulation environment (DSE). MCSE, which consists of a manufacturing cell simulator and a soft-wiring system, is emphatically proposed in detail. MCSE realizes making and evaluating facility control programs by using virtual factory models on computers before manufacturing systems are implemented.

On-line scheduling and control of flexible manufacturing cells using automata theory

An effective flexible-manufacturing-cell (FMC) controller can be synthesized using classical automata and Ramadge–Wonham (R–W) supervisory-control theories. Such a discrete-event-system controller/supervisor would enable or disable (controllable) events for maximum deadlock-free, correct behaviour of the FMC. However, in cases of multiple part-routes, machine redundancy, etc. the R–W supervisor could include states with subsequent multiple controllable events. The question of choice arises at such states necessitating the use of an on-line decision-making agent, which, consequently, would determine the overall performance of the FMC. In the above context, this paper presents a novel methodology for the on-line, deadlock-free scheduling and control of FMCs. A two-phased method is proposed. During the off-line phase, the FMC is first modelled using time-augmented automata and a deadlock-free supervisor is synthesized using R–W control theory. Subsequently, an off-line decision-making plan is constructed. During the on-line phase, based on the latest state of the workcell and the off-line plan, the best-possible scheduling decisions are made using a real-time optimization search technique. The proposed novel approach is illustrated through a typical manufacturing-cell simulation example and compared with a random-based decision-making policy. It is clearly shown that significant improvement is achieved when using the proposed approach.

A flexible simulation tool for manufacturing-cell design, I: model structure, operation, and case study

We present a general manufacturing-cell simulation model for evaluating the effects of world-class manufacturing practices on expected cell performance. The modular structure of the simulation provides the flexibility to analyze a wide variety of manufacturing cells. We formulate a comprehensive annualized cost function for evaluation and comparison of alternative cell configurations. A case study involving assembly of printed circuit boards illustrates the potential benefits of using this tool for cell design and analysis. The simulation model is intended for use in a two-phase approach to cell design that is based on simulated experimentation and response surface analysis as detailed in a companion paper.

A flexible simulation tool for manufacturing-cell design, II: response surface analysis and case study

We present a two-phase approach to design and analysis of manufacturing cells based on simulated experimentation and response surface methodology using a general manufacturing-cell simulation model. The first phase involves factor-screening simulation experiments to identify design and operational factors that have a significant effect on cell performance as measured by a comprehensive annual cost function. In the second phase of experimentation, we construct simulation (response surface) meta-models to describe the relationship between the significant cell design and operational factors (the controllable input parameters) and the resulting simulation-based estimate of expected annual cell cost (the output response). We use canonical and ridge analyses of the estimated response surface to estimate the levels of the quantitative input factors that minimize the cell's expected annual cost. We apply this methodology to an assembly cell for printed circuit boards. Compared to the current cell operating policy, the simulation metamodel-based estimate of the optimum operating policy is predicted to yield average annual savings of approximately $425 000, which is a 20% reduction in annual cost. In a companion paper, we detail the structure and operation of the manufacturing-cell simulation model.

Virtual reality software for robotics and manufacturing cell simulation

Virtual reality software (VRS) for robotics and manufacturing cells simulation is a tool to obtain a 3D graphics models for the manufacturing lines. A 3D graphics model will help facility planners to visualize the system before constructing it, make alternative designs, program robot paths, obtain layouts for the systems, obtain data for the discrete event simulation, and develop the cell control program. This paper presents an overview of VRS software, a proposal for the integration of VRS and discrete event simulation, and case study for manufacturing cell development.

Capability study of robotics and manufacturing cell simulation software

In this paper, a capability analysis was developed for comparing Virtual Reality Software (VRS) of robotics and manufacturing cells simulation. The capability analysis included all basic features that should be maintained in a software for simulation of a large scale agile manufacturing system. An algorithm has been developed for this analysis which requires subdividing the attributes into business attribute, cost attributes and other attributes related to automotive industry.

Expert systems for manufacturing cell simulation and design

Computer Integrated Manufacturing (CIM) is approached by means of the application of Computer Aided Design (CAD), Computer Aided Manufacturing (CAM) and other CA techniques, methods and programs/program systems. These programs are often implemented as knowledge-based, or expert systems and in this way they became typical examples of engineering application of artificial intelligence. The production task of CIM systems is solved by using flexible manufacturing systems (FMSs). FMSs built up from smaller, complex units, i.e. from flexible manufacturing cells (FMCs) have several advantages. The design and the operation of manufacturing systems need new, sophisticated methods to utilize all the embedded benefits of the sophisticated and expensive elements installed for production purposes. New methods like knowledge processing technology, cooperative problem-solving techniques, etc., offer wide possibilities to design more reasonable systems. This paper describes prototype expert systems that make use of different knowledge-based tools and techniques to design (configure, reconfigure) and simulate manufacturing cells, taking into consideration technological plans and other relevant information.

Lot-Sizing and Lead-time Performance in a Manufacturing Cell

Many complex multi-item manufacturing shops have high levels of work-in-process because of queueing delays at machines and consequently long manufacturing lead times. These delays are directly related to lot sizes. Two alternative approaches applied to modeling a manufacturing cell—simulation and a novel analytical lot-sizing model (Q-LOTS)—provide very similar results and validate the analytical model.