Amplification of high-intensity pressure waves and cavitation in water using a multi-pulsed laser excitation and black-TiOx optoacoustic lens.

A method for amplification of high-intensity pressure waves generated with a multi-pulsed Nd:YAG laser coupled with a black-TiOx optoacoustic lens in the water is presented and characterized. The investigation was focused on determining how the multi-pulsed laser excitation with delays between 50 µs and 400 µs influences the dynamics of the bubbles formed by a laser-induced breakdown on the upper surface of the lens, the acoustic cavitation in the focal region of the lens, and the high-intensity pressure waves generation. A needle hydrophone and a high-speed camera were used to analyze the spatial distribution and time-dependent development of the above-mentioned phenomena. Our results show how different delays (td ) of the laser pulses influence optoacoustic dynamics. When td is equal to or greater than the bubble oscillation time, acoustic cavitation cloud size increases 10-fold after the fourth laser pulse, while the pressure amplitude increases by more than 75%. A quasi-deterministic creation of cavitation due to consecutive transient pressure waves is also discussed. This is relevant for localized ablative laser therapy.

Power Control during Remote Laser Welding Using a Convolutional Neural Network.

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.