Modeling and Prediction of Surface Roughness in Ultra-High Precision Diamond Turning of Contact Lens Polymer Using RSM and ANN Methods

Abstract

In this paper, Single point diamond turning tests were carried out on rigid gas permeable contact lens (ONSI-56), using monocrystalline diamond cutting tools. During the tests, the depth of cut, feed rate, and cutting speed were varied. Turning experiments were designed based on Box-Behnken statistical experimental design technique. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness on the contact lens turned part surface. In the development of predictive models, cutting parameters of cutting speed, depth of cut and feed rate were considered as model variables. The required data for predictive models are obtained by conducting a series of turning test and measuring the surface roughness data. Good agreement is observed between the predictive models results and the experimental measurements. The ANN and RSM models for ONSI-56 contact lens turned part surfaces are compared with each other for accuracy and computational cost.