Approaches to Industry 4.0 implementation for electron beam quality assurance using BeamAssure

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

Electron Beam Weld Shape Prediction Based on Electron Beam Probing Technology

Electron beam welding (EBW) is a joining process that has been widely applied in many modern industrial sectors. However, in order to achieve a satisfactory welding quality for a given material and configuration, a trial-and-error approach is usually adopted before moving to the final production. This procedure is often wasteful, time consuming and expensive when the raw material is at high cost, and greatly relies on the operators’ personal experience. To enable a ‘smarter’ welding process and reduce the inconsistent human factor, this PhD study is to develop a novel method based on statistic modelling, numerical modelling and artificial neural networks to predict the weld profile, which is the main criterion for assessing the welding quality. The models are set up with electron beam characteristics collected through a 4-slits technology to determine the actual focal spot size and power density, therefore the uncertainty caused by beam variation can be reduced. Multi-influences caused by electron beam, machine parameters and process environment are considered, and the predictions cover a wide range of linear beam power ranging from 86 J/mm to 324 J/mm. Finally, a novel simulation tool for predicting electron beam weld shape has been developed with assistance of a 4-slits beam probing technology to reduce the amount of manual work traditionally needed to achieve high-efficiency and high-quality welding joints. Validated by experimental results, the model is able to predict the weld profile with high accuracy and reliability for both partially and fully penetrated welding situations. By combining the numerical model and artificial intelligence, a weld-profile prediction system is to be integrated in current EB welding machines to allow a less-experienced operator to achieve high welding quality