Rong Seng CHANG^*, Dong Ru CHIANG, Sha-Wei WANG¹, and Ching Huang LIN²

Department of Optics and Photonics, National Central University, No. 300 Chung-Da Rd., Chung-Li, 32001, Taiwan, R.O.C.
¹Department of Optometry, Jen-Teh Junior College of Medicine, Nursing and Management, No. 79-9 Sha-Luen-Hu, Xi-Zhou Li, Hou-Loung Town, Miaoli County 35664, Taiwan, R.O.C.
²Department of Electronic Engineering, National Yunlin University of Science and Technology, No. 123, Sec. 3, University Rd., Douliu, Yunlin 64002, Taiwan, R.O.C.

(Received November 7, 2008; Accepted March 2, 2009)

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 L₁₈ 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 +7.00 D are robust in desired quality targets.

Key words: Taguchi method, neural network, lens grinding

^*E-mail address: rschang2000@hotmail.com