This paper is concerned with obtaining physical-based low-order approximations of linear physical systems. Low-order models possess some advantages, including the reduction of computational di culty and understanding of the physics of the original system in a simpler manner. Previously, a number of methods have been suggested to develop suitable low-order approximations. However, most of these approaches do not re ect the relation between the mathematical model and the physical subsystems. Speci cally, these techniques do not indicate which of the physical subsystems should be retained or eliminated in the reduced-order model. The proposed model reduction method is based on identifying subsystem types of a physical system using the bond graph method. These subsystems are then removed or retained based on the information from the physical system decomposition procedures and partial fraction expansion residues to obtain a reduced-order model. The physical model reduction procedure is veri ed on physical linear systems.
Today, the competitive environment of the knowledge age has been replaced with the competitive environment of the industrial age as the rules of the business world are changing completely. The focused issue is to reach to the knowledge as soon as possible, and to process and apply it rapidly. In this context, one of the important goals that the companies try to reach in order to achieve acceleration is the six sigma philosophy. Six sigma is a fundamental continuous improvement methodology aimed at reducing the variation and waste on processes by utilizing statistical methods and techniques efficiently. Global companies are making noteworthy profits by using this method in their processes. The six sigma methodology focuses on process excellence advantages for companies that apply it, to reach profit, to increase productivity and to have larger market share. In this paper, an application of six sigma methodology for reducing the quantity of rework parts for robotic arc welding process is given. The phases of six sigma and their results are indicated in detail. Furthermore, it is also shown how various techniques of six sigma methodology are applied to achieve financial benefits.
This paper is concerned with obtaining physical-based low-order approximations of linear physical systems. Low-order models possess some advantages, including the reduction of computational di culty and understanding of the physics of the original system in a simpler manner. Previously, a number of methods have been suggested to develop suitable low-order approximations. However, most of these approaches do not re ect the relation between the mathematical model and the physical subsystems. Speci cally, these techniques do not indicate which of the physical subsystems should be retained or eliminated in the reduced-order model. The proposed model reduction method is based on identifying subsystem types of a physical system using the bond graph method. These subsystems are then removed or retained based on the information from the physical system decomposition procedures and partial fraction expansion residues to obtain a reduced-order model. The physical model reduction procedure is veri ed on physical linear systems.
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