Surface roughness after excimer laser ablation using a PMMA model:profilometry and effects on vision

PURPOSE: To show that the limited quality of surfaces produced by one model of excimer laser systems can degrade visual performance with a polymethylmethacrylate (PMMA) model. METHODS: A range of lenses of different powers was ablated in PMMA sheets using five DOS-based Nidek EC-5000 laser systems (Nidek Technologies, Gamagori, Japan) from different clinics. Surface quality was objectively assessed using profilometry. Contrast sensitivity and visual acuity were measured through the lenses when their powers were neutralized with suitable spectacle trial lenses. RESULTS: Average surface roughness was found to increase with lens power, roughness values being higher for negative lenses than for positive lenses. Losses in visual contrast sensitivity and acuity measured in two subjects were found to follow a similar pattern. Findings are similar to those previously published with other excimer laser systems. CONCLUSIONS: Levels of surface roughness produced by some laser systems may be sufficient to degrade visual performance under some circumstances.

A geometrical model for surface roughness prediction when face milling Al 7075-T7351 with square insert tools

Surface quality is important in engineering and a vital aspect of it is surface roughness, since it plays an important role in wear resistance, ductility, tensile, and fatigue strength for machined parts. This paper reports on a research study on the development of a geometrical model for surface roughness prediction when face milling with square inserts. The model is based on a geometrical analysis of the recreation of the tool trail left on the machined surface. The model has been validated with experimental data obtained for high speed milling of aluminum alloy (Al 7075-T7351) when using a wide range of cutting speed, feed per tooth, axial depth of cut and different values of tool nose radius (0.8. mm and 2.5. mm), using the Taguchi method as the design of experiments. The experimental roughness was obtained by measuring the surface roughness of the milled surfaces with a non-contact profilometer. The developed model can be used for any combination of material workpiece and tool, when tool flank wear is not considered and is suitable for using any tool diameter with any number of teeth and tool nose radius. The results show that the developed model achieved an excellent performance with almost 98% accuracy in terms of predicting the surface roughness when compared to the experimental data. © 2014 The Society of Manufacturing Engineers.

Artificial neural networks for surface roughness prediction when face milling Al 7075-T7351

In this work, different artificial neural networks (ANN) are developed for the prediction of surface roughness (R a) values in Al alloy 7075-T7351 after face milling machining process. The radial base (RBNN), feed forward (FFNN), and generalized regression (GRNN) networks were selected, and the data used for training these networks were derived from experiments conducted using a high-speed milling machine. The Taguchi design of experiment was applied to reduce the time and cost of the experiments. From this study, the performance of each ANN used in this research was measured with the mean square error percentage and it was observed that FFNN achieved the best results. Also the Pearson correlation coefficient was calculated to analyze the correlation between the five inputs (cutting speed, feed per tooth, axial depth of cut, chip°s width, and chip°s thickness) selected for the network with the selected output (surface roughness). Results showed a strong correlation between the chip thickness and the surface roughness followed by the cutting speed. © ASM International.