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Please note that all times are shown in the time zone of the conference. The current conference time is: 27th Jan 2022, 10:02:46pm CET
Session Chair: Manuel Henn, Institut für Strahlwerkzeuge IFSW, Germany
Location:Room 4 ICM
1:30pm - 1:45pm
Open-loop control of complex pulse shapes for laser beam welding
Marc Seibold, Klaus Schricker, Jean Pierre Bergmann
Technische Universität Ilmenau, Germany
Pulsed laser beam welding is of high importance in micro-welding applications and used for materials susceptible to hot cracking, e.g. 6xxx aluminum alloys. Pulse shapes are adjusted to prevent hot cracks by reducing solidification rates which is accompanied by decreased welding speeds. Numerical simulations are now used for optimizing the tradeoff between crack-free welds and highest possible welding speeds. This procedure requires small deviations between the nominal value of the laser beam power calculated by numerical simulations and the actual value in the experiment. A methodology is developed and validated for fiber laser and Nd:YAG laser beam sources (IPG YLM-450/4500-QCW, LASAG SLS 200CL60HP) using different pulse shapes. The differences between nominal and actual values were identified by high-speed power measurements and reduced from 13% down to 2% for complex pulse shapes over time. This paper will show how to set up power compensation to emit an accurate complex pulse shape.
1:45pm - 2:00pm
Monitoring of laser welding and cladding processes with edge artificial intelligence combining thermal and visual cameras
Beñat Arejita1,2, Juan Fernando Isaza1, Aitzol Zuloaga2
1EXOM Engineering, Spain; 2UPV/EHU, Spain
Laser welding and cladding are well known for their complexity and high dynamics, therefore being challenging for in situ and real-time quality control and monitoring. To tackle this challenge, this work presents a dedicated hardware implementation performing real time image processing of a multi camera configuration with a visual and a NIR camera coaxially set up with the laser beam and an off-axis stereoscopic camera. The coaxial images are analysed by edge artificial intelligence technics allowing real-time closed loop temperature control and an adaptive scanner head positioning to perform a precise melt pool monitoring and process traceability. In parallel, the volumetric positioning of the scanner head and laser job interpretation are done using the stereoscopic information, linking it with the job definition of the part being processed. The presented system can be used during Nickel-strip welding of big battery packs or during identification of milled recesses in cladding applications.
2:00pm - 2:15pm
Real-time adaptation of the dross attachment level in the laser cutting process based on process emission images
Matteo Pacher1, Mara Tanelli2,4, Silvia C. Strada2, Davide Gandolfi1, Sergio M. Savaresi2, Barbara Previtali3
1Adige S.P.A., BLMGROUP, Via per Barco 11, 38056, Levico Terme (TN), Italy; 2Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, via G. Ponzio 34/5, 20133 Milano, Italy; 3Dipartimento di Meccanica, Politecnico di Milano, Via La Masa 1, 20156 Milano, Italy; 4Istituto di Elettronica e Ingegneria dell’Informazione e delle Telecomunicazioni - IEIIT CNR Corso Duca degli Abruzzi 24, 10129 Torino, Italy
In the field of melt and blow metal laser cutting, dross attachment is the most important quality indicator. Accordingly, process parameters are generally optimized to ensure high productivity while minimizing the level of dross attachment. The resulting set of parameters often penalizes the productivity to increase reliability. As a result, there exists a productivity margin that could be exploited by controlling the quality level in closed-loop, thus optimizing the process parameters in real-time. To closed-loop control the process, two steps must be performed: a real-time, reliable estimate of cutting quality must be available and, a closed-loop controller should adapt the process parameters according to the desired quality level. This work presents the design and experimental validation of a real-time estimation and control algorithm based on process emission images that adapts the cutting speed to fulfill a desired dross attachment level.
Project Name: LT4.0. Funding from LP6/99 Autonomous Province of Trento