In-process surface roughness estimation model for compliant abrasive belt machining process
Surface roughness inspection in robotic abrasive belt machining process is an off-line operation which is time-consuming. An in-process multi-sensor integration technique comprising of force, accelerometer and acoustic emission sensor was developed to predict state of the surface roughness during ma...
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| Main Authors: | , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/89656 http://hdl.handle.net/10220/47107 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | Surface roughness inspection in robotic abrasive belt machining process is an off-line operation which is time-consuming. An in-process multi-sensor integration technique comprising of force, accelerometer and acoustic emission sensor was developed to predict state of the surface roughness during machining. Time and frequency-domain features extracted from sensor signals were correlated with the corresponding surface roughness to train the Support vector machines (SVM's) in Matlab toolbox and a classification model was developed. Prediction accuracy of the classification model shows proposed in-process surface roughness recognition system can be integrated with abrasive belt machining process for capping lead-time and is reliable. |
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