scholarly journals Online Workpiece Height Estimation for Reciprocated Traveling Wire EDM Based on Support Vector Machine

Procedia CIRP ◽  
2018 ◽  
Vol 68 ◽  
pp. 126-131 ◽  
Author(s):  
Guangwei Huang ◽  
Weiwen Xia ◽  
Ling Qin ◽  
Wansheng Zhao
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Wire Edm ◽  
Height Estimation ◽  
Traveling Wire

Online estimation of workpiece height by least squares support vector machines in wire electrical discharge machining

2014 ◽  
Vol 228 (14) ◽  
pp. 2618-2631 ◽  
Author(s):  
Xue-Cheng Xi ◽  
Shang-Cheng Dou ◽  
Wan-Sheng Zhao
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Electrical Discharge ◽  
Electrical Discharge Machining ◽  
Support Vector ◽  
Wire Electrical Discharge Machining ◽  
Machining Parameters ◽  
Estimation Model ◽  
Height Estimation ◽  
Basic Vector

When machining workpieces with complicated and intricate shapes by wire electrical discharge machining, the workpiece height usually varies along a machining path. To ensure a stable and efficient machining, the machining parameters must be appropriately tuned based on the estimated workpiece height. Though an offline workpiece height estimation model can be established by traditional support vector regression with a satisfactory accuracy, the offline model is unable to cover all possible machining conditions. In this paper, least squares support vector machine is proposed to build up online a workpiece height estimation model. Due to the use of equality constraints in the formulation of least squares support vector machine, all data points are treated as support vectors, thus sparsity is lost as compared with traditional support vector machine. For online applications, a low computational load as well as a limited memory storage are required for an online algorithm. To meet the requirements of online workpiece height estimation, two measures are thus adopted. One is the use of a projection method which measures the relevance of a new data point with existing basic vectors by calculating its residue. The residue is used as criteria for admission of the new data point as a new member of the basic vector set. The other is the restriction on the size of the basic vector set. Removal of an insignificant basic vector is determined by its contribution to the model, which is measured by its coefficient in the model (or support value). Experimental results show that, by online learning, the estimation model can achieve an estimation error less than 2 mm at smooth parts of a workpiece. Based on the estimated workpiece height, proper machining parameters can be set and a stable machining can be achieved.

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

Classification of Fatigued Bills Based on a Support Vector Machine by Using the Amplitude Difference Features of Acoustic Signals

2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Acoustic Signals ◽  
Support Vector ◽  
Amplitude Difference

An Accurate Packer Identification Method Using Support Vector Machine

2014 ◽  
Vol E97.A (1) ◽  
pp. 253-263 ◽  
Author(s):  
Ryoichi ISAWA ◽  
Tao BAN ◽  
Shanqing GUO ◽  
Daisuke INOUE ◽  
Koji NAKAO
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Identification Method
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