A utility-based integrated process simulation system

In this paper, a milling process simulation system was constructed and ME Z-map (Moving Edge node Z-map) model was developed to elevate the performance of this system. The milling process simulation system computes the cutting configuration and then the cutting forces are predicted using these calculated configurations. In this system, an improved cutting force model which is independent of cutting conditions is used to more precisely predict the cutting forces. In the process, the ME Z-map model was used for more accurate computing of cutting configuration. Due to the edge node, ME Z-map model produces more accurate cutting configuration than the conventional Z-map models even with five to ten times larger grid size, which reduces the computing time dramatically. The superiority of the ME Z-map model was confirmed through comparison with the conventional Z-map.

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Anisotropic-etching process simulation system MICROCAD analyzing complete 3D etching profiles of single crystal silicon

Proceedings IEEE The Tenth Annual International Workshop on Micro Electro Mechanical Systems An Investigation of Micro Structures Sensors Actuators Machines and Robots MEMSYS-97 ◽

10.1109/memsys.1997.581877 ◽

2002 ◽

Cited By ~ 18

Author(s):

K. Asaumi ◽

Y. Iriye ◽

K. Sato

Keyword(s):

Single Crystal ◽

Process Simulation ◽

Single Crystal Silicon ◽

Anisotropic Etching ◽

Simulation System ◽

Etching Process ◽

Crystal Silicon

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Simulation Platform for Fault Diagnosis

Intelligent Control and Learning Systems - Data-Driven Fault Detection and Reasoning for Industrial Monitoring ◽

10.1007/978-981-16-8044-1_4 ◽

2022 ◽

pp. 45-58

Author(s):

Jing Wang ◽

Jinglin Zhou ◽

Xiaolu Chen

Keyword(s):

Fault Diagnosis ◽

Process Simulation ◽

Fermentation Process ◽

Industrial Process ◽

Simulation System ◽

Fault Classification ◽

Simulation Platform ◽

Multivariate Statistical ◽

Fault Monitoring ◽

Statistical Analysis Methods

AbstractThe previous chapters have described the mathematical principles and algorithms of multivariate statistical methods, as well as the monitoring processes when used for fault diagnosis. In order to validate the effectiveness of data-driven multivariate statistical analysis methods in the field of fault diagnosis, it is necessary to conduct the corresponding fault monitoring experiments. Therefore this chapter introduces two kinds of simulation platform, Tennessee Eastman (TE) process simulation system and fed-batch Penicillin Fermentation Process simulation system. They are widely used as test platforms for the process monitoring, fault classification, and identification of industrial process. The related experiments based on PCA, CCA, PLS, and FDA are completed on the TE simulation platforms.

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