Abstract
The increase in complex workpieces with changing geometries demands advanced control algorithms in order to achieve stable welding regimes. Usually, many experiments are required to identify and confirm the correct welding parameters. We present a method for controlling laser power in a remote laser welding system with a convolutional neural network (CNN) via a PID controller, based on optical triangulation feedback. AISI 304 metal sheets with a cumulative thickness of 1.5 mm were used. A total accuracy of 94% was achieved for CNN models on the test datasets. The rise time of the controller to achieve full penetration was less than 1.0 s from the start of welding. The Gradient-weighted Class Activation Mapping (Grad-CAM) method was used to further understand the decision making of the model. It was determined that the CNN focuses mainly on the area of the interaction zone and can act accordingly if this interaction zone changes in size. Based on additional testing, we proposed improvements to increase overall controller performance and response time by implementing a feed-forward approach at the beginning of welding.
Highlights
Remote laser welding is a fast, complex process that achieves fast welding speeds and greater penetration depths at higher accuracy over a larger working area
All convolutional neural network (CNN) models became almost stagnant at 200 epochs; the learning phases were concluded at this stage
A novel method for laser-power control during remote laser welding based on a convolutional neural network was proposed and implemented
Summary
Remote laser welding is a fast, complex process that achieves fast welding speeds and greater penetration depths at higher accuracy over a larger working area. It is especially used in the automotive, electronics, and appliance industry [1,2,3,4]. The first challenge in laser welding applications is to accurately deliver the laser beam to the weld location This is done with off-line simulations [11] or in-line with an optical method [12,13,14,15]. The key issue is in comprehending the welding process itself, e.g., melt pool dynamics [22,23], plasma dynamics [24,25], or the effect of material properties [26], with which new welding parameters for various material combinations and joint configurations can be established
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