Resistancebased sintering
Resistance-based sintering is a highly effective method for consolidating metal powders that combines the use of axial pressure with a flowing , high-intensity current without the need for atmospheric chambers . EWI began developing this technique a couple of years ago in an effort to replace spark plasma sintering . Its team made use of its resistancewelding and load-frame resources to devise trials with titanium powders , applying a projection-welding approach .
The resistance-based sintering process involves inserting the powder of interest into a conductive support frame with a materials-appropriate liner which is then sealed with conductive caps . The frame is placed into a resistance welding fixture . Pressure and current are applied to sinter the material into final shape within seconds .
“ The results have been extremely promising . Resistance sintering trials using several powder combinations have yielded fully consolidated parts with no evidence of internal oxidation and controllable porosity . In addition , the method offers several advantages for creating alloys over other known processes such as die casting , metal injection moulding and hot isostatic pressing ,” commented EWI .
It highlighted various benefits of the process , including :
■ On-demand parts manufacturing .
■ Removes the need for expensive dies .
■ Eliminates atmospheric chambers and shielding gas .
■ No need for skilled supervision .
■ Significant reduction in energy needs .
■ Reduces or eliminates need for postprocess machining .
“ Resistance-based sintering has great potential for use across many manufacturing industries , from medical devices to the automotive and aviation sectors ,” it told ISMR .
For further details , email Olga Eliseeva , EWI project engineer , on oeliseeva @ ewi . org n
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RESEARCH NEWS
Thanks to a sophisticated modularisation approach using efficient components , SURFinpro has a wide variety of potential deployments and is easy to adapt . Image : © Shutterstock / Fraunhofer IWS .)
Optimising production processes through modularisation
Improved speed , precision and flexibility ; it is important to take advantage of every possible opportunity in optimising production . To this end , researchers at the Fraunhofer Institute for Material and Beam Technology IWS have developed SURFinpro , a solution that uses artificial intelligence and optical measurement technology to detect , classify and visualise defects in real-time , and report them to the factory carrying out the production . Fraunhofer specialists presented this system at Laser World of Photonics ( 27-30 June 2023 ).
Ultra-light , ultra-thin and reliable while reaching rapid production speeds …… Dr . Christopher Taudt , group manager for surface metrology at the Zwickau-based Fraunhofer Application Centre for Optical Metrology and Surface Technologies AZOM ( part of Fraunhofer IWS ) and his team in Germany are focused on making this reality . Together the scientists have developed a system that detects surface defects , artifacts as well as texture changes , and that evaluates them with the support of artificial intelligence . This process can rapidly capture 3D-information of surfaces in high resolution . The measurement data is used to generate supplementary information in-line for ongoing production processes .
“ The system does not just detect defects ; it classifies them at the same time and immediately establishes a wider context . Our customers receive information about the type of defect , along with many other parameters such as the defect ’ s density , geometric dimensions and frequency ,” added Taudt . “ This represents a significant added value compared to conventional systems .”
The measurement system has been operating successfully in industry for more than a year , analysing a roll-to-roll process with substrate widths of 70cm . To leverage further potential for optimisation , Christopher
Taudt and the SURFinpro team are training the system within ongoing production using a defect catalogue . As defects are reported , they are fed into a neural network , refining the detection accuracy . The researchers use the measurement information to check if new defects occur or existing defects are modified , which requires dynamic response of the system .
“ On the one hand , we are working to develop better neural networks that require less data ,” explained the scientist . “ In addition , we are also developing new training strategies within ongoing operation .”
The Fraunhofer AZOM specialists are currently adapting their technology to new fields of application , such as continuous fibrecomposite manufacturing processes . Another target group that the team envisages for the algorithms and defect-classification system is the semiconductor industry , for example in the production of flexible semiconductor materials .
Currently , the Fraunhofer AZOM solution uses a maximum of four cameras . In a next step , the researchers envision adding additional camera systems . Another key aspect for the scientists is the system speed . Very short cycle times need to be particularly rapid . The scientists believe that one of the key characteristics of their system is its modularity . Thanks to a sophisticated modularisation approach using efficient components , SURFinpro provides a wide variety of potential deployments and is easy to adapt .
“ Many of the technologies that we use in our system have been developed as standalone components in a way that they can also be implemented effectively in various other contexts and projects ,” concluded the Fraunhofer scientists . n
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