Performance Prediction of Diamond Sawblades Using Artificial Neural Network and Regression AnalysisPerformance Prediction of Diamond Sawblades Using Artificial Neural Network and Regression Analysis

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 Author Aydin, Gokhan ♦ Karakurt, Izzet ♦ Hamzacebi, Coskun Source SpringerLink Content type Text Publisher Springer Berlin Heidelberg File Format PDF Copyright Year ©2015 Language English
 Subject Domain (in DDC) Technology ♦ Engineering & allied operations Subject Keyword Diamond sawblades ♦ Granite ♦ Specific energy ♦ Artificial neural networks ♦ Regression analysis ♦ Engineering ♦ Science Abstract This paper is concerned with the application of artificial neural networks (ANNs) and regression analysis for the performance prediction of diamond sawblades in rock sawing. A particular hard rock (granitic) is sawn by diamond sawblades, and specific energy (SE) is considered as a performance criterion. Operating variables namely peripheral speed (V $_{p}$), traverse speed (V $_{c}$) and cutting depth (d) are varied at four levels for obtaining different results for the SE. Using the experimental results, the SE is modeled using ANN and regression analysis based on the operating variables. The developed models are then tested and compared using a test data set which is not utilized during construction of models. The regression model is also validated using various statistical approaches. The results reveal that both modeling approaches are capable of giving adequate prediction for the SE with an acceptable accuracy level. Additionally, the compared results show that the corresponding ANN model is more reliable than the regression model for the prediction of the SE. ISSN 13198025 Age Range 18 to 22 years ♦ above 22 year Educational Use Research Education Level UG and PG Learning Resource Type Article Publisher Date 2015-01-31 Publisher Place Berlin, Heidelberg e-ISSN 21914281 Journal Arabian Journal for Science and Engineering Volume Number 40 Issue Number 7 Page Count 10 Starting Page 2003 Ending Page 2012