تفاصيل الوثيقة
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نوع الوثيقة |
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مقال في مجلة دورية |
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عنوان الوثيقة |
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Model trees and sequential minimal optimization based support vector machine models for estimating minimum surface roughness value Model trees and sequential minimal optimization based support vector machine models for estimating minimum surface roughness value |
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الموضوع |
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Data mining |
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لغة الوثيقة |
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الانجليزية |
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المستخلص |
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Average surface roughness value (Ra) is an important measure of the quality of a machined work piece. Lower the Ra value, the higher is the work piece quality and vice versa. It is therefore desirable to develop mathematical models that can predict the minimal Ra value and the associated machining conditions that can lead to this value. In this paper, real experimental data from an end milling process is used to develop models for predicating minimum Ra value. Two techniques, model tree and sequential minimal optimization based support vector machine, which have not been used before to model surface roughness, were applied to the training data to build prediction models. The developed models were then applied to the test data to determine minimum Ra value. Results indicate that both techniques reduced the minimum Ra value of experimental data by 4.2% and 2.1% respectively. Model trees are found to be better than other approaches in predicting minimum Ra value. |
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ردمد |
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0307-904X |
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اسم الدورية |
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Applied Mathematical Modelling |
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المجلد |
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39 |
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العدد |
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3 |
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سنة النشر |
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1436 هـ
2015 م |
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نوع المقالة |
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مقالة علمية |
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تاريخ الاضافة على الموقع |
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Monday, March 7, 2016 |
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الباحثون
| Sarosh Hashmi | Hashmi, Sarosh | باحث رئيسي | دكتوراه | Sarosh.hashmi@gmail.com |
| Sami Halawani | Halawani, Sami | باحث مشارك | دكتوراه | dr.halawani@gmail.com |
| Omar Barukab | Barukab, Omar | باحث مشارك | دكتوراه | obarukab@gmail.com |
| Amir Ahmad | Ahmad, Amir | باحث مشارك | دكتوراه | amirahmad01@gmail.com |
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الرجوع إلى صفحة الأبحاث
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