The analysis of humidity factor in cestamide materials on surface roughness with the help of artificial neural network

Abstract

Cestamide, an engineering plastic, is used in many areas of industry today. Apart from its excellent mechanic features; it also has a negative feature, namely dehumidification. It is necessary to detect cutting conditions of dehumidified cestamide materials for metal cutting. After the process of humid and dry cestamides under same cutting conditions, the change of average surface roughness quality is studied by performing some experiments. For this, after keeping materials in humid and dry environment, cestamide materials are processed with same kind of cutting tool in (1, 1.5, 2, 2.5, 3 mm) chip thickness (ap), (90, 110, 130 m/min) cutting speed (Vc) (100, 120, 140,160 mm/min) feed rate (f) and then average difference of surface roughness values are detected. Moreover, an Artificial Neural Network (ANN) modelling is developed with the results obtained from the experiments. For the training of ANN model; material type, cutting speed, cutting rate and depth of cut parameters are used. In this way, average surface roughness values except for the mentioned experiment could be estimated. Experimental results and ANN model results show that, different average surface roughness values are obtained for applying same processing conditions humidity factor on cestamide materials. It is observed that a case which is based on different variables such as average surface roughness can be estimated in acceptable error rate with the help of ANN model. Key words: Artificial neural network (ANN), cestamide materials, average surface roughness.