Thermal modeling for control of friction stir welding process in automated manufacturing

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Proceedings titleProceedings of the ASME Design Engineering Technical Conference
ConferenceASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC/CIE 2011, 28 August 2011 through 31 August 2011, Washington, DC
Pages763768; # of pages: 6
SubjectAccurate design; Aluminum parts; Automated Manufacturing; Cambridge; Control methods; Experimental data; Friction behavior; Friction stir; Heat distribution; Infra-red cameras; Input variables; Joining process; Modeling and control; Modeling approach; Modeling results; Modeling technique; Process parameters; Rotating spindle; Spatial and temporal variation; Thermal behaviors; Thermal characteristics; Thermal modeling; Transient heat transfer; Wear resistant; Weld quality; Weld seam; Weld temperature; Welding institutes; Work study; Aerospace industry; Aluminum; Design; Electric welding; Friction stir welding; Gas welding; Joining; Laser beam welding; Magnesium; Magnesium printing plates; Manufacture; Neural networks; Real time control; Thermoanalysis; Thermocouples; Three dimensional; Titanium; Welds; Process control
AbstractFriction stir welding is a patented joining process invented in 1991 at The Welding Institute in Cambridge, UK, and further developed to the stage suitable for production. In this process, a wear resistant rotating tool is used to join sheet and plate with different materials such as aluminum, copper, lead, magnesium, zinc, and titanium. This work studies the thermal characteristics of this process and provides a modeling technique based on Neural Network that can be used for real-time control. A thermal feed-back control method is presented to control the process. Using some thermal modeling for the heat distribution during friction stir welding process, this paper displays the complexity of obtaining an accurate design for the thermal feed back control. A three-dimensional transient heat transfer model is developed here for a sequential joining process (Friction Stir Welding- FSW) applied on aluminum parts. A neural network is created based on a set of experiments to predict the spatial and temporal variations in the temperature over the weld seam for different set of input variables. The model includes the dynamic and friction behavior of the rotating spindle and the thermal behaviors of the weld components involved. The significance of this modeling approach is that it captures the movement of the spindle, simulating a sequential joining process along a continuous weld seam. The modeling results are compared with experimental data obtained by thermocouples and infrared camera, and accurately predict the trend of variations in weld temperature. A fuzzy-logic based controller is proposed to regulate the FSW process parameters to maintain the weld temperature within the margin required to ensure the weld quality. This modeling and control system can have applications in manufacturing aluminum parts in automotive and aerospace industry. Copyright © 2011 by ASME.
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AffiliationNational Research Council Canada (NRC-CNRC); Aerospace (AERO-AERO)
Peer reviewedYes
NPARC number21271241
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Record identifier6db7bd12-8068-49f6-9f6f-e40f064d54f1
Record created2014-03-24
Record modified2016-05-09
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