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China: Applying Neural-Network Machine Learning to Additive Manufacturing Processes https://ift.tt/2JMifG1 In ‘Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives,’ authors Xinbo Qi, Guofeng Chen, Yong Li, Xuan Cheng, and Changpeng Li investigate how machine learning (ML) and neural network algorithms (NN) can be applied to additive manufacturing. While the many benefits of AM processes continue to be uncovered, availing themselves to countless industries today, there are still numerous drawbacks and scenarios for defects which continue to challenge users around the world—from porosity to anisotropic microstructures, to distortion, and more. Prototypes may not always require perfection as simple models, however, parts meant for true functional, industrial use must be strong and produced without threat to their overall integrity. The authors point out the importance of understanding the following:
In machine learning, the NN algorithm is only increasing in popularity for use and is currently under ‘rapid development,’ most often employed in computer vision, voice recognition, language processing, and self-driving vehicles. It is a supervised type of ML, operating with labeled data, and within additive manufacturing is showing good suitability for ‘agile manufacturing’ in industry.
The most common types of NNs are:
In design for additive manufacturing, the engineers create a CAD model which was then applied in analytical software for AM simulation. Many deviations are found, however, when comparing the models to the actual 3D prints—often due to stress during production and resulting distortion. The researchers state that they usually perform compensation for better accuracy. Sensors have been created for the hardware and software, and a variety of different sensors can be used for in situ measurements too.
Machine Learning is often connected with 3D printing, from varying monitoring methods and smarter metal additive manufacturing, to construction. What do you think of this news? Let us know your thoughts! Join the discussion of this and other 3D printing topics at 3DPrintBoard.com. [Source / Images: ‘ Applying Neural-Network-Based Machine Learning to Additive Manufacturing: Current Applications, Challenges, and Future Perspectives’] Please enable JavaScript to view the comments powered by Disqus.Printing via 3DPrint.com | The Voice of 3D Printing / Additive Manufacturing https://3dprint.com July 18, 2019 at 02:00AM
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