Contact lens production requires high accuracy and good surface integrity. Surface roughness is generally used to measure the index quality of a turning process. It has been 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. In this study, an ONSI-56 (Onsifocon A) contact lens buttons were used to investigate the triboelectric phenomena and the effects of turning parameters on surface finish of the lens materials. ONSI-56 specimens are machined by Precitech Nanoform Ultra-grind 250 precision machine and the roughness values of the diamond turned surfaces are measured by Taylor Hopson PGI Profilometer. Electrostatics values were measured using electrostatic voltmeter. An artificial neural network (ANN) and response surface (RS) model were developed to predict surface roughness and electrostatic discharge (ESD) on the turned ONSI-56. In the development of predictive models, turning parameters of cutting speed, feed rate and depth of cut were considered as model variables. The required data for predictive models were obtained by conducting a series of turning test and measuring the surface roughness and ESD data. Good agreement is observed between the predictive models results and the experimental measurements. The ANN and RSM models for ONSI-56 are compared with each other using mean absolute percentage error (MAPE) for accuracy and computational cost.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:nmmu/vital:28789 |
| Date | January 2017 |
| Creators | Liman, Muhammad Mukhtar |
| Publisher | Nelson Mandela Metropolitan University, Faculty of Engineering, the Built Environment and Information Technology |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Thesis, Masters, MEng |
| Format | xxvi, 154 leaves, pdf |
| Rights | Nelson Mandela Metropolitan University |
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