Browsing by Author "Lin, H. L."

Now showing 1 - 2 of 2

Effect of compressive stress on nickel-induced lateral crystallization of amorphous silicon thin films
(2012-01-31)
Huang, S. Y.
;
Lin, H. L.
;
Chao, C. G.
;
Liu, T. F.
;
材料科學與工程學系
The present study describes a method enabling the metal-induced lateral crystallization (MILC) of amorphous silicon (a-Si) films. Glass substrates coated with a-Si films were contacted with ground nickel sheets under compressive stresses ranging from 3.7 to 265.8 MPa at 550 degrees C for 1 h. Subsequently, the nickel sheet and stress were removed and the specimens were annealed at 550 degrees C for 1 to 4 h. The experimental results indicate that the extent of MILC decreased when the preliminary compressive stress was increased, while all specimens exhibited the same rate of lateral crystallization during annealing. The present study indicates that, by applying an appropriate compressive stress (similar to 4 MPa), an effective method to reduce the residual Ni content in polycrystalline silicon (poly-Si) can be obtained. (C) 2011 Published by Elsevier B.V.
Optimizing the Auto-brazing Process Quality via a Taguchi-Neural Network Approach in the Automotive Industry
(2009)
Lin, H. L.
;
Chou, C. P.
;
機械工程學系
;
Department of Mechanical Engineering
Many parameters affect the quality of the auto-brazing process. It is not easy to obtain optimal parameters of this process. This paper applies an integrated approach using the Taguchi method and a neural network (NN) to optimize the lap joint quality of air conditioner parts. The proposed approach consists of two phases. First phase executes initial optimization via Taguchi method to construct a database for the NN. In second phase, we use a NN with the Levenberg-Marquardt back-propagation (LMBP) algorithm to provide the nonlinear relationship between factors and the response based on the experimental data. Then, a well-trained network model is applied to obtain the optimal factor settings. The experimental results showed that the tensile strength of specimens of the optimal parameters via the proposed approach is better than apply Taguchi method only.