Two-stage optimization of lens grinding parameters for multi-quality target combining Taguchi method and neural network software

Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/28173

Title:	Two-stage optimization of lens grinding parameters for multi-quality target combining Taguchi method and neural network software
Authors:	Chang,RS;Chiang,DR;Wang,SW;Lin,CH
Contributors:	光電科學研究所
Date:	2009
Issue Date:	2010-06-29 19:42:57 (UTC+8)
Publisher:	中央大學
Abstract:	In this paper we present an efficient two-stage method combining the merits of the Taguchi method and neural network software to achieve nonlinear fine optimal lens grinding parameters for both the roughness and the curvature deviation robust over a wide range of lens refraction power. Discrete and rough optimal grinding parameters for roughness and for curvature deviation are first obtained respectively using the Taguchi method with an L-18 orthogonal array. Then all the experimental data of the 18 experiments are used as input training data for neural network software to obtain a set of compromised nonlinear accurate optimal parameters for the roughness and the curvature deviation. Results of confirmation experiments using these final parameters show that lens surfaces ground with polishers ranging in curvature from -7:00 to +7.00D are robust in desired quality targets.
Relation:	OPTICAL REVIEW
Appears in Collections:	[Graduate Institute of Optics and Photonics] journal & Dissertation

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