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Contact lens manufacture requires high accuracy and surface integrity. Surface roughness an important response because it has direct influence toward the part performance and the production cost. Hence, choosing optimal cutting parameters will not only improve the quality measure but also the productivity. This research work is therefore aimed at developing a predictive surface roughness model and investigate a finish cutting conditions of ONSI-56 contact lens polymer with a monocrystalline diamond cutting tool. In this work, a novel surface roughness prediction model, in which the feed rate, cutting speed and depth of cut are considered is developed. This combined process was successfully modeled using a Box–Behnken design (BBD) with response surface methodology (RSM). The effects of feed rate, cutting speed and depth of cut were investigated. Analysis of variance (ANOVA) showed that the proposed quadratic model effectively interpreted the experimental data with coefficients of determination of R2 = 0.89 and adjusted R2 = 0.84. The worse surface value was obtained at high feedrate and low spindle speed.

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.

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