Prediction of Surface Roughness for Development of Smart Milling Machine

Abstract

Abstract The primary aim of manufacturing industry is to reduce manufacturing costs to the level that is as low as possible without reducing the quality of products. In the machining process of metal parts and features, smart milling process is being projected for materials removal process of both metal and non-metal with complex forms and finishing well. Measuring a quality of an efficient milling process is normally to be a base on the quality of the finished surface roughness. The affect several attributes of components of the surface roughness, such as ability to distribute the lubricant, load bearing capacity, heat transmission, light reflection, the contact causing surface friction, load bearing capacity, light reflection, coating or resisting fatigue with the aim to increase the quality of machined products and to reduce the cost of production in manufacturing. These can be achieved by determining the parameters of the metal cutting and machining process correctly and accurately. In this review paper, a comparative study is proposed in the form of regression method that is able to predict the surface roughness at several cutting parameters such as spindle revolution, cutting speed, feeding speed, depth of cut (DOC), length of cut (LOC), helix angle and nose radius of cut.