Comparison of Surface Roughness Prediction with Regression and Tree Based Regressions During Boring Operation
Modern manufacturing methods permit the study and prediction of surface roughness since the acquisition of signals and its processing is made instantaneously. With the availability of better computing facilities and newer algorithms in the machine learning domain, online surface roughness prediction...
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| Format: | EJournal Article |
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Institute of Advanced Engineering and Science,
2017-09-01.
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| Online Access: | Get fulltext |
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| LEADER | 02365 am a22003013u 4500 | ||
|---|---|---|---|
| 001 | ijeecs8950_7524 | ||
| 042 | |a dc | ||
| 100 | 1 | 0 | |a Surendar, S. |e author |
| 100 | 1 | 0 | |e contributor |
| 700 | 1 | 0 | |a Elangovan, M. |e author |
| 245 | 0 | 0 | |a Comparison of Surface Roughness Prediction with Regression and Tree Based Regressions During Boring Operation |
| 260 | |b Institute of Advanced Engineering and Science, |c 2017-09-01. | ||
| 500 | |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/8950 | ||
| 520 | |a Modern manufacturing methods permit the study and prediction of surface roughness since the acquisition of signals and its processing is made instantaneously. With the availability of better computing facilities and newer algorithms in the machine learning domain, online surface roughness prediction will lead to the manufacture of intelligent machines that alert the operator when the process crosses the specified range of roughness. Prediction of surface roughness by multiple linear regression, regression tree and M5P tree methods using multivariable predictors and a single response dependent variable Ra (surface roughness) is attempted. Vibration signal from the boring operation has been acquired for the study that predicts the surface roughness on the inner face of the workpiece. A machine learning approach was used to extract the statistical features and analyzed by four different cases to achieve higher predictability, higher accuracy, low computing effort and reduction of the root mean square error. One case among them was carried out upon feature reduction using Principle Component Analysis (PCA) to examine the effect of feature reduction. | ||
| 540 | |a Copyright (c) 2017 Institute of Advanced Engineering and Science | ||
| 540 | |a http://creativecommons.org/licenses/by-nc-nd/4.0 | ||
| 546 | |a eng | ||
| 690 | |||
| 690 | |||
| 655 | 7 | |a info:eu-repo/semantics/article |2 local | |
| 655 | 7 | |a info:eu-repo/semantics/publishedVersion |2 local | |
| 655 | 7 | |2 local | |
| 786 | 0 | |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 7, No 3: September 2017; 887-892 | |
| 786 | 0 | |n 2502-4760 | |
| 786 | 0 | |n 2502-4752 | |
| 786 | 0 | |n 10.11591/ijeecs.v7.i3 | |
| 787 | 0 | |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/8950/7524 | |
| 856 | 4 | 1 | |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/8950/7524 |z Get fulltext |