The full penetration hole as a stochastic process: controlling penetration depth in keyhole laser-welding processes

Abstract

Although laser-welding processes are frequently used in industrial production the quality control of these processes is not satisfactory yet. Until recently, the "full penetration hole" was presumed as an image feature which appears when the keyhole opens at the bottom of the work piece. Therefore it was used as an indicator for full penetration only. We used a novel camera based on "cellular neural networks" which enables measurements at frame rates up to 14 kHz. The results show that the occurrence of the full penetration hole can be described as a stochastic process. The probability to observe it increases near the full penetration state. In overlap joints, a very similar image feature appears when the penetration depth reaches the gap between the sheets. This stochastic process is exploited by a closedloop system which controls penetration depth near the bottom of the work piece ("full penetration") or near the gap in overlap joints ("partial penetration"). It guides the welding process at the minimum laser power necessary for the required penetration depth. As a result, defects like spatters are reduced considerably and the penetration depth becomes independent of process drifts such as feeding rate or pollution on protection glasses.

Connection Mechanism of Molten Pool during Laser Transmission Welding of T-Joint with Minor Gap Presence

“…Thus, in this process, the keyhole was fully open and belonged to full penetration welding. The air is not entrapped into the pool due to the fully open keyhole [ 13 ]. At t = 0.045 s, the gap is not present, unfull penetration welding was stable and the effects of gap on the pore formation could be neglected.…”

Real-Time GPU-Based Digital Image Correlation Sensor for Marker-Free Strain-Controlled Fatigue Testing

“…Therefore, there is no significant additional latency due to the GPU. Shorter latencies for image processing systems in the range of 0.1 ms require different hardware architectures like FPGA or specialized sensor-processor hardware like cellular neural networks [26,36].…”

“…A comparable application is particle image velocity (PIV) measurement where a GPU-based system with 2048 × 1024 pixel images was evaluated in real-time at a frame rate of up to 220 Hz [24]. Other authors used specialized cameras with SIMD processing elements integrated into the circuitry of the image sensor reaching frame rates of 1 to 14 kHz at resolutions of 128 × 128 to 176 × 144 pixel images for closed-loop applications in robotics and laser beam welding [25,26]. So, strain-controlled material testing can be considered as one of the first closed-loop applications for GPU-based image processing systems.…”

Numerical Simulation of Transport Phenomena for Laser Full Penetration Welding

“…The net evaporated mass loss of evaporation can be calculated as, (8) in which ρ s is the saturation density at liquid surface temperature T l , and β is the modification factor concerning back-scattered flux. The recoil pressure at the keyhole wall can be calculated as (9) Where p s (T l ) is the saturation density at liquid surface temperature T ι .…”

“…Fabbro et al (2005) 6) analyzed the dynamics of the keyhole and its complete geometry (front wall inclination, top and bottom apertures) by using on axis visualizations through the top and the bottom of the keyhole with a high speed video camera. Blug et al (2011) [7][8][9] investigated coaxial images of laser full penetration welding, extracted full penetration from coaxial images and developed a close loop control system to improve laser full penetration welding quality. Similarly, Zhang et al (2013) 10) studied the full penetration hole dynamics using an auxiliary illuminant.…”