Abstract
The purpose of this study is trying to apply VM [1][2] for measuring machining precision of machine tools. As shown in the left portion of Fig. 1, the AV M server [3] needs process data, which contain sensor data and machining parameters, as inputs for predicting machining accuracies. However, most machine tools do not possess sensors for providing raw process data such as vibration, current, etc. Furthermore, machining operations usually possess severe vibrations and loud noises. It makes the raw data collected from sensors attached to machine tools with low signal-to-noise (S/N) ratios, thereby affecting the prediction accuracy of VM. Even though the AVM has been an effective way to conduct workpiece measurements in high-tech industries [1], there exist challenges when applying the AVM to machining industry. Besides embedding essential sensors on the machine tool effectively, the challenges are 1) Segmentation: to accurately segment essential part of the raw process data from the original NC file, 2) Data Cleaning: to effectively handle raw process/sensor data with low S/N ratios, and 3) Feature Extraction: to properly extract significant features from the segmented raw process data. This work proposes a novel GED-plus-AVM (GAVM) system as depicted in Fig.1 to resolve all the challenges mentioned above. These challenges are judiciously addressed and successfully resolved in this paper.
| Original language | English |
|---|---|
| Title of host publication | 2015 IEEE Conference on Automation Science and Engineering |
| Subtitle of host publication | Automation for a Sustainable Future, CASE 2015 |
| Publisher | IEEE Computer Society |
| Pages | 1446-1447 |
| Number of pages | 2 |
| ISBN (Electronic) | 9781467381833 |
| DOIs | |
| Publication status | Published - 2015 Oct 7 |
| Event | 11th IEEE International Conference on Automation Science and Engineering, CASE 2015 - Gothenburg, Sweden Duration: 2015 Aug 24 → 2015 Aug 28 |
Publication series
| Name | IEEE International Conference on Automation Science and Engineering |
|---|---|
| Volume | 2015-October |
| ISSN (Print) | 2161-8070 |
| ISSN (Electronic) | 2161-8089 |
Other
| Other | 11th IEEE International Conference on Automation Science and Engineering, CASE 2015 |
|---|---|
| Country | Sweden |
| City | Gothenburg |
| Period | 15-08-24 → 15-08-28 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
Cite this
}
Total precision inspection of machine tools with virtual metrology. / Tieng, Hao; Yang, Haw Ching; Cheng, Fan Tien.
2015 IEEE Conference on Automation Science and Engineering: Automation for a Sustainable Future, CASE 2015. IEEE Computer Society, 2015. p. 1446-1447 7294301 (IEEE International Conference on Automation Science and Engineering; Vol. 2015-October).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Total precision inspection of machine tools with virtual metrology
AU - Tieng, Hao
AU - Yang, Haw Ching
AU - Cheng, Fan Tien
PY - 2015/10/7
Y1 - 2015/10/7
N2 - The purpose of this study is trying to apply VM [1][2] for measuring machining precision of machine tools. As shown in the left portion of Fig. 1, the AV M server [3] needs process data, which contain sensor data and machining parameters, as inputs for predicting machining accuracies. However, most machine tools do not possess sensors for providing raw process data such as vibration, current, etc. Furthermore, machining operations usually possess severe vibrations and loud noises. It makes the raw data collected from sensors attached to machine tools with low signal-to-noise (S/N) ratios, thereby affecting the prediction accuracy of VM. Even though the AVM has been an effective way to conduct workpiece measurements in high-tech industries [1], there exist challenges when applying the AVM to machining industry. Besides embedding essential sensors on the machine tool effectively, the challenges are 1) Segmentation: to accurately segment essential part of the raw process data from the original NC file, 2) Data Cleaning: to effectively handle raw process/sensor data with low S/N ratios, and 3) Feature Extraction: to properly extract significant features from the segmented raw process data. This work proposes a novel GED-plus-AVM (GAVM) system as depicted in Fig.1 to resolve all the challenges mentioned above. These challenges are judiciously addressed and successfully resolved in this paper.
AB - The purpose of this study is trying to apply VM [1][2] for measuring machining precision of machine tools. As shown in the left portion of Fig. 1, the AV M server [3] needs process data, which contain sensor data and machining parameters, as inputs for predicting machining accuracies. However, most machine tools do not possess sensors for providing raw process data such as vibration, current, etc. Furthermore, machining operations usually possess severe vibrations and loud noises. It makes the raw data collected from sensors attached to machine tools with low signal-to-noise (S/N) ratios, thereby affecting the prediction accuracy of VM. Even though the AVM has been an effective way to conduct workpiece measurements in high-tech industries [1], there exist challenges when applying the AVM to machining industry. Besides embedding essential sensors on the machine tool effectively, the challenges are 1) Segmentation: to accurately segment essential part of the raw process data from the original NC file, 2) Data Cleaning: to effectively handle raw process/sensor data with low S/N ratios, and 3) Feature Extraction: to properly extract significant features from the segmented raw process data. This work proposes a novel GED-plus-AVM (GAVM) system as depicted in Fig.1 to resolve all the challenges mentioned above. These challenges are judiciously addressed and successfully resolved in this paper.
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U2 - 10.1109/CoASE.2015.7294301
DO - 10.1109/CoASE.2015.7294301
M3 - Conference contribution
AN - SCOPUS:84952763515
T3 - IEEE International Conference on Automation Science and Engineering
SP - 1446
EP - 1447
BT - 2015 IEEE Conference on Automation Science and Engineering
PB - IEEE Computer Society
ER -