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المؤلفAhmad, Nooraziah
المؤلفJanahiraman, Tiagrajah V.
المؤلفTarlochan, Faris
تاريخ الإتاحة2023-01-26T07:03:29Z
تاريخ النشر2015
اسم المنشورArabian Journal for Science and Engineering
المصدرScopus
معرّف المصادر الموحدhttp://dx.doi.org/10.1007/s13369-014-1420-0
معرّف المصادر الموحدhttp://hdl.handle.net/10576/38907
الملخصPrediction model allows the machinist to determine the values of the cutting performance before machining. According to the literature, various modeling techniques have been investigated and applied to predict the cutting parameters. Response surface methodology (RSM) is a statistical method that only predicts effectively within the observed data provided. Most artificial intelligent systems mostly had an issue with user-defined data and long processing time. Recently, the extreme learning machine (ELM) method has been introduced, combining the single hidden layer feed- forward neural network with analytically determined output weights. The advantage of this method is that it can overcome the limitations due to the previous methods which include too many engineers' judgment and slow iterative learning phase. Therefore, in this study, the ELM was proposed to model the surface roughness based on RSM design of experiment. The results indicate that ELM can yield satisfactory solution for predicting the response within a few seconds and with small amount of error. 2014, King Fahd University of Petroleum and Minerals.
اللغةen
الناشرSpringer Verlag
الموضوعArtificial neural network
Extreme learning machine
Prediction
Surface roughness
العنوانModeling of Surface Roughness in Turning Operation Using Extreme Learning Machine
النوعArticle
الصفحات595-602
رقم العدد2
رقم المجلد40
dc.accessType Abstract Only


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