PRELIMINARY INVESTIGATION ON THE EFFECT OF CUTTING PARAMETERS ON SURFACE ROUGHNESS AND FLATNESS IN DRY MILLING OF PMMA

Abstract

Polymethylmethacrylate (PMMA) also known as Plexiglass is a commonly used material for many applications, especially in medical industry. In some application, PMMA parts may be also used as molds for enabling fabrication of final products. Some of these parts are manufactured by injection molding, but in many cases mechanical machining of some surfaces is still required. The cutting of PMMA requires a previous optimization of the cutting parameters combination (feed rate and spindle speed). If this optimization is not carried out, the problems encountered may refer to cutting debris or material melting during machining and, consequently, the material attaches to the tool cutting edge. This will result in a surface with very poor quality, in terms of aspect and surface roughness. This paper reports on the preliminary experimental investigation of cutting parameters on the surface roughness and flatness error in dry milling of PMMA. The cutting experiments were conducted on EMCO MILL 55 CNC drilling and milling machine. The design of experiment (DOE) consists in L27 (33) design, meaning a three factorial experimental plan (3 factors on 3 levels). The cutting parameters, respectively depth of cut, feed rate and spindle speed are taken as inputs and surface roughness and flatness are taken as outputs. The surface flatness was measured with TESA Micro-Hite coordinate measuring machine. Analysis of variance (ANOVA) was adopted to identify the statistical influence of each input parameter and combination of input parameters on surface roughness. From the preliminary results, it can be observed that optimum regime is a combination of low feed rate, low depth of cut and high spindle speed. Moreover, an artificial neural model is proposed for prediction of surface roughness and flatness considering the depth of cut, feed rate and spindle speed as input variables. This approach aims to reveal the possibility of predicting the output parameters using neural network modelling, that can be further used to optimize the cutting regime.