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DTSTART:20260308T030000
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DTSTAMP:20260330T152408Z
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DTSTART;TZID=America/New_York:20260326T120000
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DESCRIPTION:IEEE CS Schenectady Chapter is excited to host a hybrid lecture
  on "Learning and Adaptation in Robotic Wire Arc Additive Manufacturing (W
 AAM)".\n\nSpeaker: John Wen\, RPI\n\nAbstract:\n\nWire arc directed energy
  deposition robotic additive manufacturing offers near-net-shape fabricati
 on for aerospace components and the repair and modification of cast and fo
 rged parts. However\, inherent process variability can compromise geometri
 c accuracy and material quality. While in-process sensors have been introd
 uced to mitigate these issues\, they often require pausing the process\, w
 hich limits productivity and adversely affects part quality\, especially f
 or large\, heavy builds.\n\nThis talk will present a real-time\, closed-lo
 op control system that continuously adjusts both robot path speed and wire
  feed rate during the build process. The system integrates in-situ geometr
 y and thermal sensors to enable dynamic monitoring and adaptive control ba
 sed on a data-driven process model. This work is performed on a Wire Arc A
 dditive Manufacturing (WAAM) testbed at Rensselaer Polytechnic Institute (
 RPI). The system includes a welding robot with a wrist-mounted laser scann
 er\, and a monitoring robot equipped with infrared thermal cameras. The sy
 stem software architecture is based on the Robot Raconteur middleware.\n\n
 Speaker(s): John Wen\, \n\nRoom: 149/151\, Bldg: ETEC Building\, Universit
 y of Albany\, Albany\, NY\, New York\, United States\, 12203\, Virtual: ht
 tps://events.vtools.ieee.org/m/541128
LOCATION:Room: 149/151\, Bldg: ETEC Building\, University of Albany\, Alban
 y\, NY\, New York\, United States\, 12203\, Virtual: https://events.vtools
 .ieee.org/m/541128
ORGANIZER:s.salem@ieee.org
SEQUENCE:86
SUMMARY:Hybrid Lecture on Learning and Adaptation in Robotic Wire Arc Addit
 ive Manufacturing (WAAM)
URL;VALUE=URI:https://events.vtools.ieee.org/m/541128
X-ALT-DESC:Description: <br /><p>IEEE CS Schenectady Chapter is excited to 
 host a hybrid lecture on "Learning and Adaptation in Robotic Wire Arc Addi
 tive Manufacturing (WAAM)".</p>\n<p>Speaker: John Wen\, RPI&nbsp\;</p>\n<p
  class="MsoNormal" align="left"><strong>Abstract:</strong></p>\n<p class="
 MsoNormal" align="left">Wire arc directed energy deposition robotic additi
 ve manufacturing offers near-net-shape fabrication for aerospace component
 s and the repair and modification of cast and forged parts. However\, inhe
 rent process variability can compromise geometric accuracy and material qu
 ality. While in-process sensors have been introduced to mitigate these iss
 ues\, they often require pausing the process\, which limits productivity a
 nd adversely affects part quality\, especially for large\, heavy builds.</
 p>\n<p class="MsoNormal" align="left">This talk will present a real-time\,
  closed-loop control system that continuously adjusts both robot path spee
 d and wire feed rate during the build process. The system integrates in-si
 tu geometry and thermal sensors to enable dynamic monitoring and adaptive 
 control based on a data-driven process model. This work is performed on a 
 Wire Arc Additive Manufacturing (WAAM) testbed at Rensselaer Polytechnic I
 nstitute (RPI). The system includes a welding robot with a wrist-mounted l
 aser scanner\, and a monitoring robot equipped with infrared thermal camer
 as. The system software architecture is based on the Robot Raconteur middl
 eware.</p>
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