A surface roughness prediction model for the ballnose machining of polypropylene

The advent of personalised product design has brought about the need for more sophisticated manufacturing practices and technology. Personalised manufacture of consumer goods is now being pursued on CNC machining centres as opposed to the alternative of highly expensive rapid prototyping methods. The problem with the manufacture of personalised consumer products is that they can be free formed objects, which require sophisticated machining setups. Ballnose machining is a practice used to create cusp type geometry for such products, and together with CAM and CNC technology now makes it possible to create a wide range of sculptured surfaces. Ballnose machining is not new, however the ability to use this existing technology for personalised products particularly for parts machined out of polypropylene offers new avenues of research. The ability to optimise a process offers distinct advantages in terms of improved part, reduced cutting time and reduced tool degradation. The purpose of this paper is to provide a predictive model using the design of experiments for ballnose machining of polypropylene. The goal being the creation of a new knowledge base which will be used to improve the machining of polypropylene using ballnose tools in order to generate sculptured parts. The paper presents an experimental methodology based on a design of experiments strategy. The final part of the paper illustrates an optimised surface roughness model developed using Genetic Algorithm. This prediction model is tested on a validation data set and the percentage deviation of the result when compared with the measured results is shown to be small, which demonstrates the efficacy ofthe model.