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15 results on '"Hsueh, Yao-Wen"'

1. Integrating Computational Fluid Dynamics and Neural Networks to Predict Temperature Distribution of the Semiconductor Chip with Multi-heat Sources

Author

Kuan, Yean-Der, Hsueh, Yao-Wen, Lien, Hsin-Chung, Chen, Wen-Ping, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Yi, Zhang, editor, Zurada, Jacek M., editor, Lu, Bao-Liang, editor, and Yin, Hujun, editor

Published
2006
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2. An Intelligent System for the Heatsink Design

Author

Hsueh, Yao-Wen, Lien, Hsin-Chung, Hsueh, Ming-Hsien, Hutchison, David, editor, Kanade, Takeo, editor, Kittler, Josef, editor, Kleinberg, Jon M., editor, Mattern, Friedemann, editor, Mitchell, John C., editor, Naor, Moni, editor, Nierstrasz, Oscar, editor, Pandu Rangan, C., editor, Steffen, Bernhard, editor, Sudan, Madhu, editor, Terzopoulos, Demetri, editor, Tygar, Dough, editor, Vardi, Moshe Y., editor, Weikum, Gerhard, editor, Wang, Jun, editor, Yi, Zhang, editor, Zurada, Jacek M., editor, Lu, Bao-Liang, editor, and Yin, Hujun, editor

Published
2006
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8. Automatic selection of cutter orientation for preventing the collision problem on a five-axis machining

Author

Lien Hsin-Chung, Hsueh Yao-Wen, and Hsueh Ming-Hsien

Subjects
Engineering, business.product_category, Cutting tool, business.industry, Mechanical Engineering, Mechanical engineering, Collision, Rotation, Industrial and Manufacturing Engineering, Computer Science Applications, Machine tool, Euler angles, symbols.namesake, Machining, Control and Systems Engineering, Orientation (geometry), symbols, Collision problem, Nuclear Experiment, business, Software, Simulation
Abstract

The rotation joints of a five-axis machine tool can offer freedom and appropriate rotation to prevent interference problem between workpiece and the cutter. However, to a five-axis machine tool, it is quite difficult to determine the collision-free cutter orientation. Over this problem, a two-stage cutting tool collision check method is proposed to prevent the collision problem during the cutting process on a five-axis machine tool. The proposed method is capable of determining the collision free ball-end cutter orientation automatically. The first stage is to obtain the tilting and collision-free angle range in the plane that is normal to the tool path obtained. Next, a checking cone generated from this collision-free tool axis range is used for the second collision check. The collision region is formed by the intersection of the neighboring surfaces. This implies a collision-free yaw angle range. The final cutting tool orientation is determined automatically by referring the original spindle axis and the least angular variation from the spindle axis. Finally, the implementation issue is discussed with example.

Published
2006

9. Integrating Computational Fluid Dynamics and Neural Networks to Predict Temperature Distribution of the Semiconductor Chip with Multi-heat Sources

Author

Yean-Der Kuan, Hsin-Chung Lien, Wen-Ping Chen, and Hsueh Yao-Wen

Subjects
Artificial neural network, Computer science, business.industry, Fluid mechanics, Junction temperature, Computational fluid dynamics, business, Dynamical system, Temperature measurement, Backpropagation, Simulation, Test data
Abstract

In this paper, an artificial intelligent system to predict the temperature distribution of the semiconductor chip with multi-heat sources is presented by integrating the back-propagation neural network (BNN) and the computational fluid dynamics (CFD) techniques. Six randomly generated coordinates of three power sections on the chip die are the inputs and sixty-four temperature monitoring points on the top of the chip die are the outputs. In the present methodology, one hundred sets of training data obtained from the CFD simulations results were sent to the BNN for the intelligent training. There are other sixteen generated input sets to be the test data and compared the results between CFD simulation and BNN, it shows that the BNN model is able to accurately estimate the corresponding temperature distribution as well as the maximum temperature values under different power distribution after well trained.

Published
2006

13. Integrating Computational Fluid Dynamics and Neural Networks to Predict Temperature Distribution of the Semiconductor Chip with Multi-heat Sources.

Author

Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Kuan, Yean-Der, Hsueh, Yao-Wen, Lien, Hsin-Chung, and Chen, Wen-Ping

Abstract

In this paper, an artificial intelligent system to predict the temperature distribution of the semiconductor chip with multi-heat sources is presented by integrating the back-propagation neural network (BNN) and the computational fluid dynamics (CFD) techniques. Six randomly generated coordinates of three power sections on the chip die are the inputs and sixty-four temperature monitoring points on the top of the chip die are the outputs. In the present methodology, one hundred sets of training data obtained from the CFD simulations results were sent to the BNN for the intelligent training. There are other sixteen generated input sets to be the test data and compared the results between CFD simulation and BNN, it shows that the BNN model is able to accurately estimate the corresponding temperature distribution as well as the maximum temperature values under different power distribution after well trained. [ABSTRACT FROM AUTHOR]

Published
2006
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14. An Intelligent System for the Heatsink Design.

Author

Wang, Jun, Yi, Zhang, Zurada, Jacek M., Lu, Bao-Liang, Yin, Hujun, Hsueh, Yao-Wen, Lien, Hsin-Chung, and Hsueh, Ming-Hsien

Abstract

Via two-stage back-propagation neural network (BNN) learning algorithm, this paper establishes the relationship between different heatsink design parameters and performance evaluation, and induces 5 corresponding performance outputs from 6 different heatsink design and operating condition parameters (inlet airflow velocity, heatsink length or width, fin thickness, fin gap, fin height and heatsink base height) by using Computation Fluid Dynamics (CFD). After two stages well-trained, the BNN model with error compensator is able to accurate estimate the output values under different heatsink design and operation conditions. [ABSTRACT FROM AUTHOR]

Published
2006
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