Temperature analysis in Computer Numerical Control milling of medical-grade poly(methyl methacrylate)

Abstract

Conventional machining of materials is often characterized by the generation of heat and elevation of cutting point temperatures. This induces thermal transformations and development of residual stresses. Therefore, there is a necessity to evaluate the interaction of machining temperatures with heat generation to identify the optimal values for the least heat generation. This is the main aim of this study, where an analysis of temperature is conducted during the computer numerical control (CNC) milling of medical-grade poly(methyl methacrylate) (PMMA). The samples were obtained from used optical lenses with an average Shore D hardness and transparency of 79.1 and 89.2%, respectively. The experiments were designed using the Taguchi methodology. The milling parameters used were the spindle speed, depth of cut, and feed rate. A special adjustable jig and several fixtures were developed for workpiece holding. During the milling process, in-situ temperature monitoring was undertaken using a FLIR-E63900 Infrared Thermometer. From the analysis, the parameter combination for the least values of maximum and average temperatures was a spindle speed of 1250 rev/min, a depth of cut of 0.3 mm, and a feed rate of 350 mm/min. A one-way analysis of variance depicted that the spindle speed was the most significant parameter toward the maximum and average milling temperatures owing to the generated friction and cutting forces. The depth of cut and feed rate were insignificant to the temperature of the process. It is also reported that the spindle speed exhibited a direct relationship produced by the machining forces whereas the feed rate exhibited an inverse relationship with the machining temperature, which was attributed to the reduced contact time and high chip removal, hence high heat dissipation. Lastly, a regression analysis was conducted, which showed that the maximum ( R2  =  0.7566) and average temperatures ( R2  =  0.7383) during the CNC milling of medical-grade PMMA could be predicted using linear regression models.