821 15 th International Research/Expert Conference ”Trends in the Development of Machinery and Associated Technology” TMT 2011, Prague, Czech Republic, 12-18 September 2011 MODELING THE SURFACE ROUGHNESS DURING MILLING IN OFF - LINE MONITORING Dražen Bajić, Sonja Jozić, Luka Celent Faculty of Electrical Engineering, Mechanical Engineering and Naval Architecture, University of Split, Ruđera Boškovića 32, 21 000 Split Croatia ABSTRACT Off-line process control improves process efficiency. This paper examines the influence of three cutting parameters on the surface roughness in face milling as part of the off-line process control. The experiments were carried out in order to define model for process planning. Cutting speed, the feed per tooth and the depth of cut were taken as influential factors. Two modeling methodologies, namely regression analysis and neural networks have been applied to experimentally determined data. Results obtained by the models have been compared. Both models have the relative prediction error below 10%. The research has shown that when training dataset is small neural networks modeling methodologies are comparable with regression analysis methodology and furthermore it can even offer better result, in this case average relative error of 6,64%. Advantages of off-line process control which utilizes process models by using this two modeling methodologies were explained in theory. Keywords: Off-line process control; Surface roughness; Regression Analysis; Radial basis function neural network 1. INTRODUCTION Process control is the manipulation of process variables motivated by process regulation and process optimization. The adaptation of process variables therefore has the purpose of reduction production cost or cycle time. Usually it is done through adjusting three impact factors: the cutting speed, the feed and the depth of cut and employing parameter estimation to adapt the model to changing process conditions. Process control can be performed as an on-line or off-line process. Off-line process control refers to preliminary defining process variables as a part of process planning stage. Selection of variables is usually based on a machine book or the operator’s experience therefore computer aided process planning is a step forward and provides better results in production. Complex manufacturing and technological processes nowadays claim implementation of control systems using sophisticated mathematical and other methods for the purpose of their efficient control. Thus a research is needed to get the mathematical approximations of machining processes and appearing phenomena as better as possible. Engineers face in manufacturing two main practical problems. The first is to determine the values of the process parameters that will allow achieving expected product quality and the second is to optimize manufacturing system performance with available resources. The decisions made by manufacturing engineers are based not only on their experience and expertise but also on understanding the machining principles and mathematical relations among influential parameters. Machining process is determined by the mutual relationship of the input values and its efficiency can be measured through output values. The great number of input values, as well as a fact that they have quantitative and qualitative nature contributes to the large expands of possible interactions and their complexity. The aim of this research is to find mathematical models which relate the surface roughness with three cutting parameters, the cutting speed (v c ), the feed per tooth (f t ) and the depth of cut (a p ), in face milling. In this research two different approaches have been used in order to get the mathematical models. The first approach is a design of experiment (DOE) together with an analysis of variance