Abstract
This paper presents a multi-objective optimization approach to select key machining features for improving the predictive accuracy of virtual metrology. Along increasing of complicated machining features, supervised optimization methods can be applied to select the significant features; however, these methods are inapplicable when the number of selected features are far greater than the number of training samples for modeling. Based on a novel unsupervised two-stage clustering procedure, this paper proposes a clustering non-dominated sorting genetic algorithm (CNSGA) to minimize objectives of selecting key features, e.g., the feature number and the clustering ratios. According to the selected features, a virtual metrology system was adopted to predict the machining quality of a machining process in a CNC lathe. The results show that precision and robustness of using the features selected by the proposed CNSGA for predicting machining accuracies of wheel rims are better than that of using the other selection approach.
| Original language | English |
|---|---|
| Article number | 6899432 |
| Pages (from-to) | 899-904 |
| Number of pages | 6 |
| Journal | IEEE International Conference on Automation Science and Engineering |
| Volume | 2014-January |
| DOIs | |
| Publication status | Published - 2014 Jan 1 |
| Event | 2014 IEEE International Conference on Automation Science and Engineering, CASE 2014 - Taipei, Taiwan Duration: 2014 Aug 18 → 2014 Aug 22 |
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All Science Journal Classification (ASJC) codes
- Control and Systems Engineering
- Electrical and Electronic Engineering
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A multi-objective optimization approach for selecting key features of machining processes. / Tieng, Hao; Yang, Haw Ching; Hung, Min Hsiung; Cheng, Fan-Tien.
In: IEEE International Conference on Automation Science and Engineering, Vol. 2014-January, 6899432, 01.01.2014, p. 899-904.Research output: Contribution to journal › Conference article
TY - JOUR
T1 - A multi-objective optimization approach for selecting key features of machining processes
AU - Tieng, Hao
AU - Yang, Haw Ching
AU - Hung, Min Hsiung
AU - Cheng, Fan-Tien
PY - 2014/1/1
Y1 - 2014/1/1
N2 - This paper presents a multi-objective optimization approach to select key machining features for improving the predictive accuracy of virtual metrology. Along increasing of complicated machining features, supervised optimization methods can be applied to select the significant features; however, these methods are inapplicable when the number of selected features are far greater than the number of training samples for modeling. Based on a novel unsupervised two-stage clustering procedure, this paper proposes a clustering non-dominated sorting genetic algorithm (CNSGA) to minimize objectives of selecting key features, e.g., the feature number and the clustering ratios. According to the selected features, a virtual metrology system was adopted to predict the machining quality of a machining process in a CNC lathe. The results show that precision and robustness of using the features selected by the proposed CNSGA for predicting machining accuracies of wheel rims are better than that of using the other selection approach.
AB - This paper presents a multi-objective optimization approach to select key machining features for improving the predictive accuracy of virtual metrology. Along increasing of complicated machining features, supervised optimization methods can be applied to select the significant features; however, these methods are inapplicable when the number of selected features are far greater than the number of training samples for modeling. Based on a novel unsupervised two-stage clustering procedure, this paper proposes a clustering non-dominated sorting genetic algorithm (CNSGA) to minimize objectives of selecting key features, e.g., the feature number and the clustering ratios. According to the selected features, a virtual metrology system was adopted to predict the machining quality of a machining process in a CNC lathe. The results show that precision and robustness of using the features selected by the proposed CNSGA for predicting machining accuracies of wheel rims are better than that of using the other selection approach.
UR - http://www.scopus.com/inward/record.url?scp=84939632195&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84939632195&partnerID=8YFLogxK
U2 - 10.1109/CoASE.2014.6899432
DO - 10.1109/CoASE.2014.6899432
M3 - Conference article
VL - 2014-January
SP - 899
EP - 904
JO - IEEE International Conference on Automation Science and Engineering
JF - IEEE International Conference on Automation Science and Engineering
SN - 2161-8070
M1 - 6899432
ER -