Y
Y.S. Tarng
Researcher at National Taiwan University of Science and Technology
Publications - 8
Citations - 1845
Y.S. Tarng is an academic researcher from National Taiwan University of Science and Technology. The author has contributed to research in topics: Drill & Welding. The author has an hindex of 8, co-authored 8 publications receiving 1720 citations.
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Journal ArticleDOI
Design optimization of cutting parameters for turning operations based on the Taguchi method
W.H. Yang,Y.S. Tarng +1 more
TL;DR: In this paper, the Taguchi method was used to find the optimal cutting parameters for turning operations, and the main cutting parameters that affect the cutting performance in turning operations were found.
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The use of grey-based Taguchi methods to determine submerged arc welding process parameters in hardfacing
TL;DR: In this paper, the grey relational analysis is adopted to solve the submerged arc welding (SAW) process with multiple weld qualities, and the optimal process parameters are determined by using the parameter design proposed by the Taguchi method.
Journal ArticleDOI
A comparison between the back-propagation and counter-propagation networks in the modeling of the TIG welding process
S.C Juang,Y.S. Tarng,H.R Lii +2 more
TL;DR: In this paper, the use of neural networks to model tungsten inert gas (TIG) welding is explored, where both the back-propagation and counter-progressive networks are used to associate the welding process parameters with the features of the weld-pool geometry.
Journal ArticleDOI
Cutting-parameter selection for maximizing production rate or minimizing production cost in multistage turning operations
Bean Yin Lee,Y.S. Tarng +1 more
TL;DR: In this article, a machining model is constructed based on a polynomial network and an optimization algorithm using a sequential quadratic programming method is used to determine optimal cutting parameters.
Journal ArticleDOI
Modeling and optimization of drilling process
Bean Yin Lee,H.S Liu,Y.S. Tarng +2 more
TL;DR: In this article, the use of an abductive network for modeling drilling processes is described, which is composed of a number of functional nodes, these nodes being self-organized to form an optimal network architecture by using a predicted squared error (PSE) criterion.