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

TaylorMade Uses Formlabs to Prototype Better Golf Clubs with 3D Printing

Open Source Grinding Machine Cuts Cost of Pellet 3D Printing



Categories

3D Design

3D Printed Art

3D printed automobiles

3D Printed Food


You May Also Like

Tuning 3D Printed Flexible Materials with Microfluidics Droplet System

As the name suggests, microfluidics is centered around the behavior, manipulation, and control of fluids that have been constrained to a very small scale. Obviously, accurate handling is of the...

Featured

3D Printed Respirator Masks Below N95 Standards, Says Virginia Tech Team

We’ve been cautious and careful about promoting 3D-printed COVID safety equipment here at 3DPrint.com. We talked about a general principle of first doing no harm and also discussed safety recommendations...

Featured

6K Partners with Relativity Space, Commissions UniMelt to Transform Sustainability in Metal 3D Printing

On the heels of their recent announcement of commissioning the first two commercial UniMelt systems for sustainable production of additive manufacturing (AM) powders, 6K has now partnered with Relativity Space...

Hybrid Drug Delivery Systems Made by Combining FFF 3D Printing & Conventional Manufacturing

Over the last few years, research has shown that 3D printing has a lot of potential for fabricating drug delivery systems. Now, a group of researchers from the Aristotle University...


Shop

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