Abouelatta, O. (2013). PREDICTION OF MACHINING OPERATIONS AND SURFACE ROUGHNESS USING ARTIFICIAL NEURAL NETWORK. JES. Journal of Engineering Sciences, 41(No 3), 1021-1044. doi: 10.21608/jesaun.2013.114779
O. B. Abouelatta. "PREDICTION OF MACHINING OPERATIONS AND SURFACE ROUGHNESS USING ARTIFICIAL NEURAL NETWORK". JES. Journal of Engineering Sciences, 41, No 3, 2013, 1021-1044. doi: 10.21608/jesaun.2013.114779
Abouelatta, O. (2013). 'PREDICTION OF MACHINING OPERATIONS AND SURFACE ROUGHNESS USING ARTIFICIAL NEURAL NETWORK', JES. Journal of Engineering Sciences, 41(No 3), pp. 1021-1044. doi: 10.21608/jesaun.2013.114779
Abouelatta, O. PREDICTION OF MACHINING OPERATIONS AND SURFACE ROUGHNESS USING ARTIFICIAL NEURAL NETWORK. JES. Journal of Engineering Sciences, 2013; 41(No 3): 1021-1044. doi: 10.21608/jesaun.2013.114779
PREDICTION OF MACHINING OPERATIONS AND SURFACE ROUGHNESS USING ARTIFICIAL NEURAL NETWORK
Production Engineering and Mechanical Design Department, Faculty of Engineering, Mansoura University, 35516 Mansoura, Egypt.
Abstract
Surface roughness is considered as one of the most specified customer requirements in machining processes. For efficient use of machine tools, selection of machining process and determination of optimal cutting parameters (speed, feed and depth of cut) are required. Therefore, it is necessary to find a suitable way to select and to find optimal machining process and cutting parameters for a specified surface roughness values. In this work, machining process was carried out on AISI 1040 steel in dry cutting condition in a lathe, milling and grinding machines and surface roughness was measured. Forty five experiments have been conducted using varying speed, feed, and depth of cut in order to find the surface roughness parameters. This data has been divided into two sets on a random basis; 36 training data set and 9 testing data set. The training data set has been used to train different artificial neural network (ANN) models in order to predict machining processes and surface roughness parameter values through back propagation network. Experimental data collected from tests were used as input parameters of a neural network to identify the sensitivity among machining operations, cutting parameters and surface roughness. Selected indexes were used to design a suitable algorithm for the prediction of machining processes. A software was developed and implemented to predict the machining processes and surface roughness values. The results showed that the proposed models are capable of predicting machining operations, cutting parameters and surface roughness.
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