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. (1393). PREDICTION OF THE TEMPERATURE OF THE HOLE DURING THE DRILLING PROCESS USING ARTIFICIAL NEURAL NETWORKS. , 38(38M1+), 269-274. doi: 10.22099/ijstm.2014.1972
. "PREDICTION OF THE TEMPERATURE OF THE HOLE DURING THE DRILLING PROCESS USING ARTIFICIAL NEURAL NETWORKS". , 38, 38M1+, 1393, 269-274. doi: 10.22099/ijstm.2014.1972
. (1393). 'PREDICTION OF THE TEMPERATURE OF THE HOLE DURING THE DRILLING PROCESS USING ARTIFICIAL NEURAL NETWORKS', , 38(38M1+), pp. 269-274. doi: 10.22099/ijstm.2014.1972
. PREDICTION OF THE TEMPERATURE OF THE HOLE DURING THE DRILLING PROCESS USING ARTIFICIAL NEURAL NETWORKS. , 1393; 38(38M1+): 269-274. doi: 10.22099/ijstm.2014.1972

PREDICTION OF THE TEMPERATURE OF THE HOLE DURING THE DRILLING PROCESS USING ARTIFICIAL NEURAL NETWORKS

Iranian Journal of Science and Technology Transactions of Mechanical Engineering
مقاله 12، دوره 38، 38M1+، مرداد 2014، صفحه 269-274    اصل مقاله (321.32 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22099/ijstm.2014.1972
چکیده
Information about the temperature of drilling hole during the drilling process is
important in work-piece quality and tools life aspects. In this study temperature of the drilling hole
is determined using Artificial Neural Networks according to certain points’ temperature of the
work piece and two parameters, drill diameter and ambient temperature. To achieve this aim, twodimensional
model of work piece is provided; then by Computational Heat Transfer simulations
based on Finite Volume Method, temperature in different nodes of the work piece is specified.
Obtained results are used for training and testing the neural network. Temperature of specified
points, drill diameter and ambient temperature are selected as inputs of the network and
temperature of drilling hole is considered as an output data. Also, for comparison, temperature is
obtained experimentally. Comparison between numerical results and experimental data shows that
neural network can be used more efficiently to determine temperature of hole in a drilling process.
کلیدواژه‌ها
Temperature of drilling hole؛ Artificial Neural Network؛ Levenberg-Marquardt
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