RESEARCH NEWS
GreenBotAI for flexible , energy-saving robots
Smaller batch sizes instead of mass production , more complex production lines , increasing competitive pressure and unstable supply chains …. Against this backdrop , the Franco-German research project , GreenBotAI , is addressing robotics . Its three main objectives are ensuring production continuity ( even during pandemics ), strengthening Europe ’ s independence in production automation and significantly reducing the energy consumption of robotic applications in European factories .
Technically , GreenBotAI focuses on the reaction and latency times in industrial robotics , optimised path planning and executing well-defined tasks while the robot is in motion (‘ on the fly ’). Without stopping , for example , it is possible to capture 2D images for object recognition for the required handling or assembly task . Fraunhofer IWU in Germany leads the consortium involved in this Franco-German research project .
“ The project focuses on an agile approach to handling materials and workpieces . AI algorithms are supposed to give manufacturing more flexibility and accelerate production in various industrial sectors . The goal is intelligent robotics capable of handling complex tasks spontaneously . Required components in hardware , the latest deep learning methods for monitoring , improvements in data processing and error control are expected to herald a generational shift in robotics ,” commented Fraunhofer IWU .
GreenBotAI is working on several levers to reduce energy consumption by up to 25 percent . These include data-reduced AI models , accelerated gripping tasks and reduced computing power .
At the Hannover Messe earlier this year , the project partners demonstrated what they had so far achieved . An individual image taken in 2D with industrial camera technology (“ One-Shot Demonstration ”) of the component and a small amount of computing power were sufficient for the robot to pick up , inspect and position a component as required . Xeidana ® software , developed at Fraunhofer IWU , verified whether the robot had picked up the correct component .
Guests at the stand could try it out themselves . They could place a gear at any point on a table in the workspace of
Image : © iStock / OleksandrKr .
Controlled by 2D images , the robot picks up a component and , using intelligent AI , fits it into a second gear . Munich University of Applied Sciences , 22 March 2024 .
Xeidana ® software confirms that the robot has gripped the correct gear . Image : © Fraunhofer IWU .
a collaborative robot . Based on the image information , the software determines the positions of all objects relative to the robot , calculates the robot ’ s path and determines the gripping position . The robot then picks up the gear and inspects it , relying entirely on the image information . This is where real-time evaluation of force data comes into play , guiding the robot on how to fit the selected gear into a second gear , using a smart application of AI that mimics human touch . A digital twin visualises all real actions of the robot . Visitors also experienced the assembly and integrated quality control application live .
Project partners in GreenBotAI include Fraunhofer IWU ; Munich University of Applied Sciences ( Faculty of Applied Natural Sciences and Mechatronics ); software developer , INBOLT SAS , and École Nationale Supérieure d ’ Arts et Métiers ( ENSAM LISPEN ). The German Federal Ministry for Economic Affairs and Climate Action is the funding provider on the German side . n
www . iwu . fraunhofer . de / en . html
Member call for Advisory Groups
The EU ’ s Research Fund for Coal and Steel ( RFCS ) has launched a call for applications for the selection of members of the Coal and Steel Advisory Groups . The Research Programme also provides support for clean steel breakthrough technologies leading to near-zero-carbon steel-making projects .
The Programme is managed by the European Research Executive Agency ( REA ) 2 with the support of two advisory groups , the Coal Advisory Group ( CAG ) and the Steel Advisory Group ( SAG ), as well as seven Technical Groups ( TGs ).
Members of the Coal and Steel Advisory Groups are appointed by the Director General of the European Commission ’ s Directorate- General for Research and Innovation for a term of 42 months . The deadline for applications is 27 September 2024 .
The application and selection procedures , as well as the selection criteria , are described in the notice of the call for applications on : https :// shorturl . at / qzOcV n
Remote laser cutting technique
Dr . Lloyd Tinkler , AMRC ( Advanced Manufacturing Research Centre ) senior technical fellow , and Dr . Alexei Winter , AMRC technical lead for electrical machines , have collaborated with University of Sheffield researchers Dr . Luke Jones , Professor Geraint Jewell and Professor Hassan Ghadbeigi on a research article entitled , ‘ Influence of Remote Laser Cutting on Magnetic Loss and Mechanical Properties in 0.2mm Silicon Steel ’.
The results provide insights into the remote laser cutting process and its potential for commercial application in the production of laminated cores for electrical machines .
The research paper investigates the effect of remote laser cutting on the magnetic and mechanical properties of commercial thin electrical steel sheets ( 0.2mm Cogent / Tata Steel NO20 3.2 % Si ). The influence of laser power and consequent thermal damage accumulation on the magnetic permeability , tensile and fatigue properties were studied .
Silicon steels are the most common materials used for electric machines . The cores of electrical machines are typically made from many hundreds of thin , insulated thin sheets or laminations of silicon steel to enhance flux density and reduce eddy current losses . For details , see bit . ly / 3AH9oUg n
24 | ismr . net | ISMR September 2024