Study on the wire feed speed prediction of double-wire-pulsed MIG welding based on support vector machine regression

2015 ◽  
Vol 79 (9-12) ◽  
pp. 2107-2116 ◽  
Author(s):  
Ping Yao ◽  
JiaXiang Xue ◽  
Kang Zhou
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Feed Speed ◽  
Mig Welding ◽  
Support Vector Machine Regression ◽  
Wire Feed Speed ◽  
Speed Prediction ◽  
The Wire ◽  
Wire Feed

Application of MIG Welding Process of Mild Steel Wire in Additive Manufacturing

2021 ◽  
Vol 877 ◽  
pp. 73-79
Author(s):  
Pattarawadee Poolperm ◽  
Wasawat Nakkiew ◽  
Nirut Naksuk
Keyword(s):  
Additive Manufacturing ◽  
Steel Wire ◽  
Welding Process ◽  
Feed Speed ◽  
Mig Welding ◽  
Microhardness Testing ◽  
Wire Feed Speed ◽  
Forming Characteristics ◽  
Arc Current ◽  
Wire Feed

The purpose of this study is to investigate the forming characteristics of single-pass Metal Inert Gas (MIG) welding wire for multi-layer additive manufacturing parts. Influences of arc current, arc voltage, arc distances, welding speed, wire feed speed, temperatures and heat input on layer formation were analyzed. The deposition of material by MIG process is controlled by a robot (ABB) controller for constructing walls of rectangular box shape. The samples were measured with a microhardness testing and tensile testing onto the welded bead created by the additive manufacturing technique. It was found that the mechanical properties of microhardness values are between 151.70 to 155.80 HV and the tensile strength values are between 472.71 to 491.12 MPa according to transverse and longitudinal sections of the specimens.

An application of backtracking search optimization–based least squares support vector machine for prediction of short-term wind speed

2019 ◽  
Vol 44 (3) ◽  
pp. 266-281 ◽  
Author(s):  
Zhongda Tian ◽  
Yi Ren ◽  
Gang Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Prediction Model ◽  
Least Squares ◽  
Support Vector ◽  
Short Term ◽  
Backtracking Search ◽  
Wind Speed Prediction ◽  
Search Optimization ◽  
Speed Prediction

Wind speed prediction is an important technology in the wind power field; however, because of their chaotic nature, predicting wind speed accurately is difficult. Aims at this challenge, a backtracking search optimization–based least squares support vector machine model is proposed for short-term wind speed prediction. In this article, the least squares support vector machine is chosen as the short-term wind speed prediction model and backtracking search optimization algorithm is used to optimize the important parameters which influence the least squares support vector machine regression model. Furthermore, the optimal parameters of the model are obtained, and the short-term wind speed prediction model of least squares support vector machine is established through parameter optimization. For time-varying systems similar to short-term wind speed time series, a model updating method based on prediction error accuracy combined with sliding window strategy is proposed. When the prediction model does not match the actual short-term wind model, least squares support vector machine trains and re-establishes. This model updating method avoids the mismatch problem between prediction model and actual wind speed data. The actual collected short-term wind speed time series is used as the research object. Multi-step prediction simulation of short-term wind speed is carried out. The simulation results show that backtracking search optimization algorithm–based least squares support vector machine model has higher prediction accuracy and reliability for the short-term wind speed. At the same time, the prediction performance indicators are also improved. The prediction result is that root mean square error is 0.1248, mean absolute error is 0.1374, mean absolute percentile error is 0.1589% and R2 is 0.9648. When the short-term wind speed varies from 0 to 4 m/s, the average value of absolute prediction error is 0.1113 m/s, and average value of absolute relative prediction error is 8.7111%. The proposed prediction model in this article has high engineering application value.

Erratum to: Prognostics of Induction Motor Shaft Based on Feature Importance and Least Square Support Vector Machine Regression

2021 ◽  
Vol 18 (2) ◽  
pp. 8751
Author(s):  
Didik Djoko Susilo ◽  
A. Widodo ◽  
T. Prahasto ◽  
M. Nizam
Keyword(s):  
Support Vector Machine ◽  
Induction Motor ◽  
Mechanical Engineering ◽  
Least Square ◽  
Support Vector ◽  
Motor Shaft ◽  
Support Vector Machine Regression ◽  
Related Article ◽  
Feature Importance

This is an erratum to International Journal of Automotive and Mechanical Engineering 2021; 18(1): 8464–8477. Please refer to the related article: https://doi.org/10.15282/ijame.18.1.2021.06.0641

Correlation between wire feed speed and external mechanical constraint for enhanced process stability in underwater wet flux-cored arc welding

2018 ◽  
Vol 233 (10) ◽  
pp. 2061-2073 ◽  
Author(s):  
Jianfeng Wang ◽  
Qingjie Sun ◽  
Jiangkun Ma ◽  
Peng Jin ◽  
Tianzhu Sun ◽  
...  
Keyword(s):  
Welding Process ◽  
Electrical Signal ◽  
Feed Speed ◽  
Process Stability ◽  
Underwater Wet Welding ◽  
Extinction Process ◽  
Flux Cored Arc Welding ◽  
Wire Feed Speed ◽  
Wire Feed ◽  
Mechanical Constraint

It is a great challenge to improve the process stability in conventional underwater wet welding due to the formation of unstable bubble. In this study, mechanical constraint method was employed to interfere the bubble generated by underwater wet welding, and the new method was named as mechanical constraint assisted underwater wet welding. The aim of the study was to quantify the combined effect of wire feed speed and condition of mechanical constraint on the process stability in mechanical constraint assisted underwater wet welding. Experimental results demonstrated that the introduction of mechanical constraint not only suppressed the bubble without floating but also stabilized the arc burning process. The degree of influence of mechanical constraint, which changed with wire feed speed, played an important role during the mechanical constraint assisted underwater wet welding process. For all wire feed speeds, the fluctuations of welding electrical signal were decreased through introduction of mechanical constraint. The difference in the proportion of arc extinction process between underwater wet welding and mechanical constraint assisted underwater wet welding became less with increasing wire feed speed. At wire feed speed lower than 7.5 m/min, the improvement of process stability was very significant by mechanical constraint. However, the further improvement produced limited effect when the wire feed speed was greater than 7.5 m/min. The observation results showed that a better weld appearance was afforded at a large wire feed speed, corresponding to a lower variation coefficient.

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