Modelling and controlling product manufacturing systems using bond-graphs and state equations: Continuous systems and discrete systems which can be represented by continuous models

2000 ◽  
Vol 11 (1) ◽  
pp. 7-19 ◽  
Author(s):  
Michel Ferney
Keyword(s):  
Manufacturing Systems ◽  
Discrete Systems ◽  
Continuous Systems ◽  
Bond Graphs ◽  
State Equations ◽  
Continuous Models ◽  
Product Manufacturing

scholarly journals Hidden Attractors in Discrete Dynamical Systems

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 616
Author(s):  
Marek Berezowski ◽  
Marcin Lawnik
Keyword(s):  
Dynamical Systems ◽  
Chaos Theory ◽  
Scientific Literature ◽  
Discrete Systems ◽  
Discrete Dynamical Systems ◽  
Continuous Systems ◽  
Hidden Attractors ◽  
Negative Effects ◽  
Chemical Reactors ◽  
Points Of View

Research using chaos theory allows for a better understanding of many phenomena modeled by means of dynamical systems. The appearance of chaos in a given process can lead to very negative effects, e.g., in the construction of bridges or in systems based on chemical reactors. This problem is important, especially when in a given dynamic process there are so-called hidden attractors. In the scientific literature, we can find many works that deal with this issue from both the theoretical and practical points of view. The vast majority of these works concern multidimensional continuous systems. Our work shows these attractors in discrete systems. They can occur in Newton’s recursion and in numerical integration.

Simulation of Multiple Cell Population Dynamics Using a 3-D Cellular Automata Model for Tissue Growth

2012 ◽  
pp. 201-220
Author(s):  
Belgacem Ben Youssef ◽  
Lenny Tang
Keyword(s):  
Population Dynamics ◽  
Cellular Automata ◽  
Temporal Dynamics ◽  
Discrete Systems ◽  
Tissue Growth ◽  
State Transitions ◽  
Extended Model ◽  
Alternative Approach ◽  
Continuous Models ◽  
Multiple Cell

In this paper, the authors describe a computational model for the growth of multicellular tissues using a discrete approach based on cellular automata to simulate the tissue growth rates and population dynamics of multiple populations of proliferating and migrating cells. Each population of cells has its own division, motion, collision, and aggregation characteristics. These random dynamic processes can be modeled by appropriately choosing the governing rules of the state transitions of each computational site. This extended model contains a number of system parameters that allow their effects on the volume coverage, the overall tissue growth rate, and some other aspects of cell behavior like the average speed of locomotion to be explored. These discrete systems provide an alternative approach to continuous models for the purpose of describing the temporal dynamics of complex systems.

A Logical Procedure for the Construction of Bond Graphs in Systems Modeling

1972 ◽  
Vol 94 (3) ◽  
pp. 183-188 ◽  
Author(s):  
H. R. Martens ◽  
A. C. Bell
Keyword(s):  
Complex Model ◽  
Bond Graphs ◽  
State Equations ◽  
Final Model ◽  
Resonance Behavior ◽  
Modeling Process ◽  
Step Procedure ◽  
Internal Dissipation ◽  
Linear State ◽  
Experimental Values

The problem of deriving a suitable mathematical model for complex devices is discussed. A small vibratory air pump is used as the medium of presentation. The modeling process begins with the basic coupling structure of the device. In a logical step-by-step procedure the initial model is built up to satisfy a number of functional considerations inherent to the device, such as the resonance behavior, input impedance, output impedance, and internal dissipation. At each step in the modeling process the completeness and suitability of the model is examined. Bond graphs drawn for the successively larger and more complex model clearly predict the shortcomings of the partial model and point the way to the next step. It is evident that the principle of causal relations forms a most important guiding element in the modeling process. The final model is in the form of a set of linear state equations, and scaling of the A-matrix indicates the relative importance of parameters when experimental values are substituted for literals.

Modeling and Analysis of Multi-Product Manufacturing Systems With Two Machines and One Buffer

2019 ◽  
Author(s):  
Jing Huang ◽  
Qing Chang ◽  
Jorge Arinez
Keyword(s):  
System Performance ◽  
Manufacturing Systems ◽  
Performance Measure ◽  
Buffer System ◽  
Product System ◽  
Modeling And Analysis ◽  
Product Manufacturing ◽  
Production Orders ◽  
System Performance Measure ◽  
Flow Conservation

Abstract The ability to process multiple product types is an important criterion for evaluating the flexibility of a manufacturing system. The system dynamics of a multi-product system is quite distinct from that of a single-product system. A modeling method for the multi-product system is proposed based on dynamic systems and flow conservation. Based on the model, this paper places its emphasis on the analysis of a two-machine-one-buffer system with two product variants. The system performance measure of a multi-product system is proposed based on production orders. The system performance of two-machine-one-buffer systems is discussed in full details. The conditions for the system achieving the best performance are derived. Finally, several numerical experiments are conducted to validate the propositions on two-machine-one-buffer system.

scholarly journals Modeling Product Manufacturing Reliability with Quality Variations Centered on the Multilayered Coupling Operational Characteristics of Intelligent Manufacturing Systems

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5677
Author(s):  
Anqi Zhang ◽  
Yihai He ◽  
Xiao Han ◽  
Yao Li ◽  
Xiuzhen Yang ◽  
...  
Keyword(s):  
Product Quality ◽  
Manufacturing Process ◽  
Manufacturing Systems ◽  
Coupling Effect ◽  
Intelligent Manufacturing ◽  
Intelligent Manufacturing Systems ◽  
Operational Characteristics ◽  
Product Manufacturing ◽  
Quality Variation ◽  
Variation Model

For intelligent manufacturing systems, there are many deviations in operational characteristics, and the coupling effect of harmful operational characteristics leads to the variations in quality of the work-in-process (WIP) and the degradation of the reliability of the finished product, which is reflected as a loss of product manufacturing reliability. However, few studies on the modeling of product manufacturing reliability and mechanism analysis consider the operating mechanism and the coupling of characteristics. Thus, a novel modeling approach based on quality variations centered on the coupling of operational characteristics is proposed to analyze the formation mechanism of product manufacturing reliability. First, the PQR chain containing the co-effects among the manufacturing system performance (P), the manufacturing process quality (Q), and the product manufacturing reliability (R) is elaborated. The connotation of product manufacturing reliability is defined, multilayered operational characteristics are determined, and operational data are collected by smart sensors. Second, on the basis of the coupling effect in the PQR chain, a multilayered product quality variation model is proposed by mining operational characteristic data obtained from sensors. Third, an integrated product manufacturing reliability model is presented on the basis of the variation propagation mechanism of the multilayered product quality variation model. Finally, a camshaft manufacturing reliability analysis is conducted to verify the validity of the proposed method. The method proposed in this paper proved to be effective for evaluating and predicting the product reliability in the smart manufacturing process.

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