Electron beam welding (EBW) is a complex process used in manufacturing high-value components in the aerospace and nuclear industries. The Fourth Industrial Revolution is a fusion of advances in artificial intelligence, sensing techniques, data science, and other technologies to improve productivity and competitiveness in fast-growing markets.
Although the EBW process can be monitored by characterisation of the electron beams before welding or using backscattered electron signals (BSE), the noise and lack of understanding of these signal patterns is a major obstacle to the development of a reliable, rapid and cost-effective process analysis and control methodology.
In this thesis a controlled experiment was designed to be relevant to those industries and improve understanding of the relationship between beam and weld quality. The welding quality control starts before welding, continue throughout the welding process, and is completed with examination after welding. The same workflow was followed in this thesis, focusing on aforementioned QC stages, starting with beam probing experiments, followed by monitoring weld pool stability using high dynamic range camera and BSE signals, and ending with metallographic inspection on sections.
The rapid development of computer vision methods brought an idea of classifying beam probing data before welding, which is first QC stage. Dataset of 3015 BeamAssure measurements was used in combination with deep learning, and various encoding methods such as Recurrence Plots (RP), Gramian Angular Fields (GAF), and Markov Transition Fields (MTF). The segmentation and classification results achieved a remarkable rate of 97.6% of accuracy in the classification task. This part of the work showed that use of time-series images enabled identification of the beam focus location before welding and providing recommended focus adjustment value.
To replicate in-process QC step, titanium alloy (Ti-6Al-4V) plates were welded with a gap opened in a stepwise manner, to simulate gap defects and introduce weld pool instability. Experiments were conducted to monitor the weld pool stability with a HDR camera and BSE detector designed for the need of this experiment. Signal and image analysis revealed occurrence of the weld defects and their locations, which was reflected by last QC stage, metallographic inspection on sections. This final part of the work proved that whatever method is used for gap defects monitoring, those joint misalignments can be easily registered by both methods. More interestingly, BSE monitoring allowed porosity and humping detection, which shapes and location were projected onto the BSE signal amplitude.
Presented three stage QC method can contribute to a better understanding of beam probing and BSE signals patterns, providing a promising approach for quality assurance in EBW and could lead to higher weld integrity by improved process monitoring.