American Journal of Mechanical Engineering, 2015, Vol. 3, No. 1, 1-6 Available online at http://pubs.sciepub.com/ajme/3/1/1 © Science and Education Publishing DOI:10.12691/ajme-3-1-1 Prediction of Surface Roughness and Feed Force in Milling for Some Materials at High Speeds Omar Monir Koura 1,* , Tamer Hassan Sayed 2 1 Mechanical Department, Faculty of Engineering, Modern University for Technology & Information, Egypt 2 Design & Prod. Eng. Department, Faculty of Engineering, Ain Shams University, Egypt *Corresponding author: koura_omar@yahoo.com Received December 09, 2014; Revised January 09, 2015; Accepted January 18, 2015 Abstract Machining at relatively high speed perform differently than when traditionally cutting speeds are used. High speed machining affects to a great extent the quality of the manufactured products. The effects differ from material to another. The aim of the present paper is to compare the quality of the machined parts at different cutting speed ranges. The study covers several engineering materials. Neural Network techniques was applied in the prediction of both the resulted surface roughness and the developed feed forces. Keywords: high speed cutting, cutting conditions, roughness and tool wear, artificial neural network Cite This Article: Omar Monir Koura, and Tamer Hassan Sayed, “Prediction of Surface Roughness and Feed Force in Milling for Some Materials at High Speeds.” American Journal of Mechanical Engineering, vol. 3, no. 1 (2015): 1-6. doi: 10.12691/ajme-3-1-1. 1. Introduction Milling is currently the most effective and productive manufacturing method for roughing and semi-finishing large surfaces of metallic parts. High speed milling is sometime necessary to mill materials with special characteristics. Milling performance, accuracy and surface texture are tied up with the operating cutting conditions. Reference [1], built a model to predict surface roughness of milling surface based on cutting speed, feed and depth of cut of end milling operations. The model is based on Genetic expression programming (GEP) which is a solution method that makes a global function search for the problem, developed as a resultant genetic algorithm (GA) and genetic programming (GP) algorithms. The tests were carried out on Aluminum 6061 T8 using a 10 mm diameter HSS end mill. The range of speed used was 23 to 47 m/min, range of feed 135 to 650 mm/min and range of depth of cut 0.25 to 1.27 mm. The model gave the relation between cutting parameters and surface roughness with accuracy of about 91%. Reference [2], proposed a method for determination of the best cutting parameters leading to minimum surface roughness in end milling mold surfaces of an ortez part used in biomedical applications by coupling neural network and genetic algorithm. A series of cutting experiments for mold surfaces in one component of ortez part are conducted to obtain surface roughness values. The tests were carried out on Aluminum 7075 T6 using a 10 mm diameter Sandvik end mill. The range of speed used was 100 to 300 m/min, range of feed 0.32 to 0.52 mm/rev, range of axial depth of cut 0.30 to 0.7 mm and range of radial depth of cut 1 to 2 mm. A feed forward neural network model is developed exploiting experimental measurements from the surfaces in the mold cavity. Genetic algorithm coupled with neural network is employed to find optimum cutting parameters leading to minimum surface roughness without any constraint. Reference [3], developed a mathematical model for determining the optimal machining conditions, so as to obtain a surface with specified properties, taking account of the technological constraints on the following parameters: the residual stress; the roughness and micro- hardness (cold working) of the machined surface; the structural–phase composition of the surface layer (the temperature), the tool life; and the standard machine-tool data. It is found that the surface roughness declines with increase in cutting speed and decrease in the feed and depth. Also, the cutting speed and depth have the greatest influence on the surface micro-hardness. Increasing the cutting speed made the surface properties become more uniform in high-speed end milling. Reference [4], presented an artificial neural network (ANN) model for predicting the surface roughness performance measure in the machining process. Matlab ANN toolbox was used for the modelling purpose. The tests were carried out on Titanium Alloy (Ti-6A1-4V) using un-coated, TiAIN coated and SNTR tools. The range of speed used was 124 to 167 m/min and range of feed 0.025 to 0.083 mm/tooth. The study concluded that the model for surface roughness in the milling process could be improved by modifying the number of layers and nodes in the hidden layers of the ANN network structure, particularly for predicting the value of the surface roughness performance measure. As a result of the prediction, the recommended combination of cutting conditions to obtain the best surface roughness value is a high speed with a low feed rate and radial rake angle.