Prediction of mechanical properties of carbon fiber based on cross-scale FEM and machine learning

2019 ◽  
Vol 212 ◽  
pp. 199-206 ◽  
Author(s):  
Zhenchao Qi ◽  
Nanxi Zhang ◽  
Yong Liu ◽  
Wenliang Chen
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Carbon Fiber ◽  
Prediction Of Mechanical Properties

A machine learning case study with limited data for prediction of carbon fiber mechanical properties

2019 ◽  
Vol 105 ◽  
pp. 123-132 ◽  
Author(s):  
Gelayol Golkarnarenji ◽  
Minoo Naebe ◽  
Khashayar Badii ◽  
Abbas S. Milani ◽  
Reza N. Jazar ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Carbon Fiber ◽  
Limited Data

scholarly journals Directed Energy Deposition-Arc (DED-Arc) and Numerical Welding Simulation as a Hybrid Data Source for Future Machine Learning Applications

2021 ◽  
Vol 11 (15) ◽  
pp. 7075
Author(s):  
Jan Reimann ◽  
Stefan Hammer ◽  
Philipp Henckell ◽  
Maximilian Rohe ◽  
Yarop Ali ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Energy Deposition ◽  
Cooling Time ◽  
Regression Equations ◽  
Welding Simulation ◽  
Geometrical Features ◽  
Machine Learning Applications ◽  
Prediction Of Mechanical Properties ◽  
Directed Energy

This research presents a hybrid approach to generate sample data for future machine learning applications for the prediction of mechanical properties in directed energy deposition-arc (DED-Arc) using the GMAW process. DED-Arc is an additive manufacturing process which offers a cost-effective way to generate 3D metal parts, due to its high deposition rate of up to 8 kg/h. The mechanical properties additively manufactured wall structures made of the filler material G4Si1 (ER70 S-6) are shown in dependency of the t8/5 cooling time. The numerical simulation is used to link the process parameters and geometrical features to a specific t8/5 cooling time. With an input of average welding power, welding speed and geometrical features such as wall thickness, layer height and heat source size a specific temperature field can be calculated for each iteration in the simulated welding process. This novel approach allows to generate large, artificial data sets as training data for machine learning methods by combining experimental results to generate a regression equation based on the experimentally measured t8/5 cooling time. Therefore, using the regression equations in combination with numerically calculated t8/5 cooling times an accurate prediction of the mechanical properties was possible in this research with an error of only 2.6%. Thus, a small set of experimentally generated data set allows to achieve regression equations which enable a precise prediction of mechanical properties. Moreover, the validated numerical welding simulation model was suitable to achieve an accurate calculation of the t8/5 cooling time, with an error of only 0.3%.

scholarly journals Machine Learning Enabled Prediction of Mechanical Properties of Tungsten Disulfide Monolayer

ACS Omega ◽  
2019 ◽  
Vol 4 (6) ◽  
pp. 10121-10128 ◽  
Author(s):  
Xinyu Wang ◽  
Dan Han ◽  
Yang Hong ◽  
Haiyi Sun ◽  
Jingzhi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mechanical Properties ◽  
Tungsten Disulfide ◽  
Prediction Of Mechanical Properties
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