IJE TRANSACTIONS B: Applications Vol. 30, No. 11 (November 2017) 1807-1813   

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M. Momeni, S. Ridha, S. J. Hosseini, X. Liu, A. Atashnezhad and S. Ghaheri
( Received: June 08, 2017 – Accepted in Revised Form: September 08, 2017 )

Abstract    This study is designed to consider the two important yet often neglected factors, which are factory recommendation and bit features, in optimum bit selection. Image processing techniques have been used to consider the bit features. A mathematical equation, which is derived from a neural network model, is used for drill bit selection to obtain the bit’s maximum penetration rate that corresponds to the optimum parameters for drilling. At the end, the bit with the maximum penetration rate is chosen. The results of this study showed that bit pattern can be inserted in the calculation through a proper bit image processing technique. This is to ensure that each unique bit can be discriminated from other bits. The values of mean square error and coefficient of determination (R2) were respectively found as 0.0037 and 0.9473, for the rate of penetration model. The image processing techniques were used to extract the bit features. The artificial neural network black box was converted to white box in order to extract a mathematical equation and visibility of the model.


Keywords    Bit selection, Artificial neural network, Image processing techniques, Genetic algorithm, Optimum drilling operation


چکیده    این مطالعه به منظور بررسی عوامل دوگانه مهم اما اغلب نادیده گرفته شده، که شامل توصیه کارخانه و ویژگی های مته است، در انتخاب مته مطلوب طراحی شده است. تکنیک های پردازش تصویر برای در نظر گرفتن ویژگی های مته استفاده شده اند. از یک معادله ریاضی که از یک مدل شبکه عصبی استخراج شده است برای انتخاب مته حفاری برای به دست آوردن حداکثر میزان نفوذ مته که به پارامترهای بهینه برای حفاری مربوط می شود استفاده می گردد. در پایان، مته با حداکثر میزان نفوذ انتخاب می شود. نتایج این مطالعه نشان می دهد که الگوی مته را می توان از طریق یک روش پردازش تصویر مته مناسب در محاسبه قرار داد. این برای اطمینان از این است که هر مته منحصر به فرد می تواند متمایز از مته های دیگر باشد. مقادیر خطای میانگین مربع و ضریب همبستگی (R2) به ترتیب 0037/0 و 9473/0 برای مدل میزان نفوذ به دست آمد. از تکنیک های پردازش تصویر برای استخراج ویژگی های مته استفاده شد. جعبه سیاه شبکه عصبی مصنوعی به منظور استخراج معادلات ریاضی و شفافیت مدل، به جعبه سفید تبدیل شد.


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