Application of genetic algorithm technique for machining parameters optimization in drilling of stainless steel

• Autorzy: Kannan T. Deepan Bharathi , Kumar B. Suresh , Kannan G. Rajesh , Umar M. , Khan Mohammad Chand

Identyfikatory

DOI

10.2478/mme-2019-0036

• Języki publikacji: EN

Abstrakty

EN

This work is aimed at developing relations between the pertinent variables that affect drilling process of stainless steel using artificial neural network. The experiments were conducted on vertical CNC machining centre. The parameters used were spindle speed and feed rate. The effect of machining parameters on entry burr height, exit burr height and surface roughnesswas experimentally evaluated for different spindle speeds and feed rates. A model was established between the drilling parameters and experimentally obtained data using ANN. The predicted values and measured values are fairly close, which indicates that the developed model can be effectively used to predict the burr height and surface roughness in drilling of stainless steel. Genetic algorithm (GA) technique was used in this work to identify the optimized drilling parameters. Confirmation test was conducted with the optimized parameters and it was found that confirmation test results were similar to that of GA-predicted output values.

Słowa kluczowe

EN

drilling; machining parameters; artificial neural network; genetic algorithm

PL

wiercenie; parametry obróbki; sztuczna sieć neuronowa; algorytm genetyczny

• Wydawca: Wydawnictwo Politechniki Łódzkiej
• Czasopismo: Mechanics and Mechanical Engineering
• Rocznik: 2019
• Tom: Vol. 23, nr 1
• Strony: 271--276
• Opis fizyczny: Bibliogr. 14 poz., 1 il. kolor., 1 fot.

Twórcy

autor

Kannan T. Deepan Bharathi

• Department of Mechanical Engineering, SRM Institute of Science and Technology, Kattankulathur, Tamilnadu, India 603203

autor

Kumar B. Suresh

• Department of Mechanical Engineering, K. Ramakrishnan College of Technology, Tiruchirappalli, Tamilnadu, India 621112

autor

Kannan G. Rajesh

• Department of Mechanical Engineering, A.R.S College of Engineering, Sattamangalam, Kanchipuram, Tamilnadu, India 603209

autor

Umar M.

• Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamilnadu, India 620015

autor

Khan Mohammad Chand

• Department of Electronics Engineering, University of Rome Tor Vergata, Roma, Italy 173

Bibliografia

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Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).