Russian Researchers Develop Neural Network for Metal 3D Printing

Share this Article

3D printing is not a simple process, particularly metal 3D printing. It involves a great deal of complex mathematical modeling, with calculations that can take weeks for even the most basic parts. But scientists from Peter the Great St. Petersburg Polytechnic University have developed a neural network for metal 3D printing that is trained with a large number of parameters, which allows for the faster production of parts as well as the ability to use discovered dependencies to manufacture new parts.

Neural networks are computing systems used to process large data inputs. Researchers at the university used this method to obtain 3D printing process parameters and ensure the stability of the process.

“This was very important for us, since the metal transfer, which takes place in the course of printing parts from wire, is a very complex process characterized by competing physical effects; it has, however, a critical impact on the quality of the printed part,” said Oleg Panchenko, Head of the St. Petersburg Polytechnic University’s Laboratory of Light Materials and Structures SPbPU.

The network was developed in the Mathlab modeling environment, and all data was entered manually. A tool exists for the automatic acquisition of printing process parameters, but so far this data set is being processed online. Next, the researchers will develop an online system based on a neural network that will be learning continuously. The parameters will be added to the system automatically, while their tuning will take place in the course of printing. The researchers believe that the system will improve the quality of parts as well as increase the speed of developing process parameters for further manufacturing.

The neural network is already being used to assess the quality parameters of manufactured parts – for example, if the welding process is stable, if the metal is being melted and transferred correctly, etc. The scientists have also used the network to develop stable printing modes for manufacturing mastheads. They have applied for a patent for the new technology.

“We are the first to use neural networks in electric arc deposition,” Panchenko said.

He added that neural networks will soon find applications in additive manufacturing as well. The researchers believe that the use of similar approaches in the future will allow for the creation of fully automated self-learning systems able to continuously improve the quality of manufactured parts without human supervision.

The neural network developed by the Russian researchers is another step towards the overall automation of additive manufacturing, which has the potential to not only speed up the process and improve the quality of parts but to reduce the risk of human error, which is high when complex mathematics are involved. Metal additive manufacturing still suffers from a great deal of wasted time, money and material due to failed builds, but with advancements such as this one, those failures can potentially be greatly reduced in the future.

Discuss this and other 3D printing topics at 3DPrintBoard.com or share your thoughts below. 

[Source: Sputnik News/Images: SPBPU Media Center]

 

Share this Article


Recent News

Medical Startup axial3D Raises U$S 3 Million To Expand To New Markets

Carnegie Mellon: Optimizing Soft Materials 3D Printing With Machine Learning



Categories

3D Design

3D Printed Art

3D Printed Food

3D Printed Guns


You May Also Like

4D Printing in China: Shape Memory Polymers and Continuous Carbon Fiber

Researchers have been looking further into the benefits of shape memory polymers (SMPs) with the addition of raw materials in the form of continuous carbon fiber (CCF). Authors Xinxin Shen,...

3D Printed Wireless Biosystems for Monitoring Cerebral Aneurysms in Real Time

Continuing to further the progress between 3D printing and electronics within the medical field, authors Robert Herbert, Saswat Mishra, Hyo-Ryoung Lim, Hyoungsuk Yoo, and Woon-Hong Yeo explore a new method...

Feasibility Models to Determine Efficacy of 3D Printing Over Traditional Methods

In ‘Model for Evaluating Additive Manufacturing Feasibility in End-Use Production,’ authors Matt Ahtiluoto, Asko Uolevi Ellman, and Eric Coatenea encourage the idea of exploring 3D printing for designs first, comparing...

Refining Macro and Microscopic Topology Optimization for AM Processes

Researchers from Italy and Germany continue along the path so many are following in refining and perfecting 3D printing processes. In the recently published ‘Structural multiscale topology optimization with stress...


Shop

View our broad assortment of in house and third party products.


Print Services

Subscribe To Our Newsletter

Subscribe To Our Newsletter

Join our mailing list to receive the latest news and updates from our 3DPrint.com.

You have Successfully Subscribed!