Chaotic behavior in manufacturing systems (original) (raw)
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The nature and origin of chaos in manufacturing systems
Proceedings of 1994 IEEE/SEMI Advanced Semiconductor Manufacturing Conference and Workshop (ASMC)
In an informal manner, the word chaos frequently comes to the lips of engineers trying to operate manufacturing facilities. From a formal perspective, the discovery and application of the theory of deterministic chaos to natural systems has revolutionized work in many branches of physics, chemistry, and biology. This paper presents initial efforts to demonstrate chaotic behavior in manufacturing systems, and to explore its origins. We characterize chaotic behavior operationally as small changes bringing about large effects.
Chaos detection and control in production systems
Global Production Management, 1999
The growing structural and dynamical complexity oftoday's production systems in combination with nonlinearities causes problems for production control. For analysing the complex production systems a production model is simulated with a production simulator and the dynamic system properties are examined with different methods of the linear and nonlinear time series analysis. The dependence of the stock fluctuations of different parameters and initial conditions are analysed. At the appearance of irregular stock fluctuations, suitable control methods should intrude into sensitive production sectors and lead the system back to a stable range.
Chaotic Behavior in a Flexible Assembly Line of a Manufacturing System
Engineering, Technology & Applied Science Research
The purpose of the present work is to study the chaotic behavior in a flexible assembly line of a manufacturing system. A flexible assembly line can accommodate a variety of product types. Result analysis is performed to obtain time persistent data. The behavior of the system is observed for Work-In-Process, as assembling systems are sensitive during processing. It is found that the average Lyapunov exponent is positive in the considered case, and thus chaotic behavior may be present in flexible assembly lines.
Modelling and Control of Production Systems based on Nonlinear Dynamics Theory
CIRP Annals - Manufacturing Technology, 2002
Today's highly dynamic market with its rapid changing demand requires highly dynamic order processing in very flexible production systems. Most conventional production planning and control methods do not support such fast-moving activities. A dynamical approach is introduced for modelling and control of production systems. It was developed from concepts of the Nonlinear Dynamics Theory. Manufacturing processes as well as planning and control mechanisms are seen as one unit toward the establishment of a dynamical system. The dynamical approach includes an analysis of the dynamic behaviour of the production system as well as the control of the manufacturing process by a continuous adjustment because of changes or disturbances in the environment or in the production system itself.
Production and inventory control with chaotic demands
Omega, 2005
This study explores an e cient approach for identifying chaotic phenomena in demands and develops a production lot-sizing method for chaotic demands. Owing to the butter y e ect of chaotic demands, precise prediction of long-term demands is di cult. The experiments conducted in this study reveal that the maximal Lyapunov exponent is very e ective in classifying chaotic and non-chaotic demands. A computational procedure of the Lyapunov exponent for production systems has been developed and some real world chaotic demands have been identiÿed using the proposed chaos-probing index. This study proposes a modiÿed Wagner-Whitin method that uses a forward focused perspective to make production lot-sizing decision under chaos demands for a single echelon system. The proposed method has been empirically demonstrated to achieve lower total production costs than three commonly used lot-sizing models, namely: lot-for-lot method, periodic ordering quantity, and Silver-Meal discrete lot-size heuristic under a ÿxed production horizon, and the conventional Wagner-Whitin algorithm under chaotic demands. Sensitivity analysis is conducted to compare changes in total cost with variations in look-ahead period, initial demand, setup cost and holding costs.
Deterministic chaos in a model of discrete manufacturing
2009
A natural extension of the bucket brigade model of manufacturing is capable of chaotic behavior in which the product intercompletion times are, in effect, random, even though the model is completely deterministic. This is, we believe, the first proven instance of chaos in discrete manufacturing. Chaotic behavior represents a new challenge to the traditional tools of engineering management to reduce variability in production lines. Fortunately, if configured correctly, a bucket brigade assembly line can avoid such pathologies.
In 21. centuries' modern enterprises, system engineers have started to investigate the chaotic situations in the light of chaos theory by considering them in the earlier stages of the formation of manufacturing information systems. The purpose of this paper is to review chaos theory in order to motivate innovations in manufacturing enterprises and to examine the role that it may have in the discipline of the manufacturing information system and the management of an enterprise. In manufacturing information systems, data driven models based on alternative scenarios are developed according to chaos theory. The use of the chaos theory will contribute to the knowledge enhancement in manufacturing information systems' development and accelerate the transformation from complexity to incomplexity. The application of the chaos theory eases the controlling of the system, shorten manufacturing times, causes positive effect on the decreasing the cost and increasing the quality of the sy...
Chaotic Dynamics of Cutting Processes Applied to Reconfigurable Manufacturing Systems Control
2000
There are already known and enounced into dedicated literature the limits of classic theory concerning cutting processes stability. Starting from this aspect and also from the need of designing an intelligent system to control cutting stability, to enable full use of RMS technological system productivity resources, this paper is a first step to a new approach of cutting process dynamics,
International Journal of Computers Communications & Control, 2012
Currently, it is recognized that manufacturing systems are complex in their structure and dynamics. Management, control and forecasting of such systems are very difficult tasks due to complexity. Numerous variables and signals vary in time with different patterns so that decision makers must be able to predict the behavior of the system. This is a necessary capability in order to keep the system under a safe operation. This also helps to prevent emergencies and the occurrence of critical events that may put in danger human beings and capital resources, such as expensive equipment and valuable production. When dealing with chaotic systems, the management, control, and forecasting are very difficult tasks. In this article an application of neural networks and vector support machines for the forecasting of the time varying average number of parts in a waiting line of a manufacturing system having a chaotic behavior, is presented. The best results were obtained with least square support vector machines and for the neural networks case, the best forecasts, are those with models employing the invariants characterizing the system's dynamics.
Non-stationary models of manufacturing systems: relevance and analysis
Proceedings of Tenth International Symposium on Intelligent Control, 1995
Performance evaluation studies in manufacturing systems have traditionally considered models in which the arrival process and service process are time independent. Real-world manufacturing systems however, are subjected i o highly complex and usually tamedependent input workloads. This motivates the study of performance models of manufacturing systems under non-stationary conditions. In this paper, we present several situations in manufacturing systems where non-stationary models are relevant. For studying such models, transient analysis is more appropriate than steady-state analysis. We explore various techniques for analyzing such models, including numerical and simulation techniques, and present two illustrative examples.