Spatters are frequently occurred defects in laser welding, which affect the weld aesthetics, deteriorate the mechanical performances and even lead to the weld joint rejection for products with strict requirement of surface finish. In this work, a novel beam shaping technology was used to mitigate spatter defects in laser welding of aluminium alloys by superimposing additional ring-shaped beam. An experimental and numerical combined research program was implemented to reveal the characterises of welding performances and mechanism of spatters mitigation in superimposed core and ring-shaped laser beam welding. The experimental results showed that a proper addition of a certain power of ring-shaped beam could significantly improve the surface finish of weld joints and mitigate the spatters. A comprehensive three-dimensional, transient, multi-physics thermal-fluid coupled model of laser welding was also established to study the changes in physical process owing to the addition of ring-shaped beam. The simulated results indicated that the additional ring-shaped beam could result in an enlarged keyhole entrance and a stabilized keyhole, which are the main factors that are attributed to the sound weld surface finish and mitigated spatters. 

Mitigating spatters in keyhole-mode laser welding by superimposing additional ring-shaped beam

Al/Cu laser-welded overlap joints, in which weld-penetration depth significantly influences both joint strength and electrical conductivity, are widely applied in automotive battery cells. In this study, a unisensor convolutional neural network (CNN) model that predicts penetration depth using coaxial weld-pool images as input and multisensor CNN models that utilize additional photodiode signals are proposed. The penetration depth was estimated using an optical coherence tomography sensor. The coefficient of determination values for the unisensor and multisensor CNN models were between 0.982 and 0.985, and their mean absolute errors were between 0.0278 and 0.0302 mm. The short-term Fourier transform multisensor model presented the best performance in terms of prediction of penetration depth when applied to the photodiode signal. The proposed prediction models were validated using a gradually varying laser power experiment, which demonstrated the efficacy of this approach and its potential use in automotive applications. Keywords: Laser welding, Al/Cu overlap joint, Penetration-depth estimation, Image sensor, Photodiode, CNN, Deep learning. 

Weld-penetration-depth estimation using deep learning models and multisensor signals in Al/Cu laser overlap welding

Weld quality inspection is a key aspect of modern manufacturing. Automatic identification, location, and dimensioning of welds and defects are necessary for complex industrial environments. Weld and defects dimensional data are important indicators for high precision assessment of weld quality, while precise positioning of welds and defects can provide important location data for subsequent processing. In this study, a novel weld and defects detection, location, and size calculation method is proposed based on deep fully convolutional neural networks. Firstly, 43 high-precision high-power laser weld plate scanned images are automatically cropped to 800 pixels × 800 pixels by the proposed sliding window sampling method for training, testing, and validation, while an image data enhancement method is proposed to quadruple the amount of data to improve the model performance. Next, the OU-net image semantic segmentation model is established to accurately identify different detection targets, which can achieve MIoU and MPA values of 78.15% and 85.18% on the validation set after sufficient training, respectively. Then, the proposed cropped segmentation image fusion recovery method is used to obtain the complete weld segmentation image for a complete display of the different inspection targets on the weld plate. Finally, a detection targets localization and size calculation method is applied to accurately obtain the position data and size data of different targets. The validation results show that the proposed detection strategy can locate and measure every weld seam, collapse, overlap, spatter, and hump on the weld plate. 

Weld beads and defects automatic identification, localization, and size calculation based on a lightweight fully convolutional neural network

The growth in wearable medical sensor-based technologies has made it possible to capture high volume physiological data of patients, both within and outside the hospital The acquired physiological data are analyzed, usually in real-time, using a patient monitoring application for early disease detection or to detect any other changing conditions of a patient In some cases, it is desirable to have a distributed, scalable patient monitoring system to which the physiological data of different patients can be submitted for online analysis Such a system should be able to support the concurrent analysis of multiple data streams of different patients, allowing a clinician to remotely monitor more than one patient from a single location This type of system also conserves resources, since in this case, there is no need to provision computational resources for every single patient being monitored In this paper, we explore the usability of Apache Storm, an open-source real-time processing engine, in the development of such a scalable patient monitoring system The contribution of this work, therefore, is to demonstrate that it is possible to achieve a more resource-efficient alternative to the isolated patient monitoring systems by using a distributed real-time computation platform, Apache Storm, to develop a scalable health monitoring system that can support the concurrent monitoring of multiple patients To show how the proposed system can be developed, we describe a prototype implementation of a multi-tenant health monitoring application that monitors the arrhythmia status of multiple patients, based on a simple ECG analysis, using Apache Storm

A Scalable Patient Monitoring System Using Apache Storm

As a mechanical internal locking structure, the forming process of a self-piercing riveted (SPR) joint is currently mainly investigated through riveting interruption experiments and finite element simulation. To solve the problems of complex simulation modelling processes, high experimental costs, and low efficiency, an SPR process prediction method based on deep learning was proposed to predict the deformation process and damage of SPR joints in carbon fibre-reinforced composites and aluminium alloys. The original images of the model dataset were obtained based on the simulation results, and image segmentation technology was used to classify the cross-section and damage morphology of the joint. A deep learning model based on a convolutional neural network and conditional generation antagonism model architecture was established, and the section shape and damage morphology of the joint were predicted by inputting the percentage of punch displacement. The k-fold cross validation method was used for model training, and the untrained data were used as the test set to verify the predictive ability of the model by comparing the forming section parameters of the SPR joint. The results show that the deep learning model used can accurately predict the deformation state and damage evolution of riveted materials at different joining stages. The average prediction accuracies of the height of the riveted head, residual thickness, and rivet spread are 95.80 %, 95.68 %, and 92.40 %, respectively. 

Forming process prediction of a self-piercing riveted joint in carbon fibre reinforced composites and aluminium alloy based on deep learning

Real-time identification of molten pool and keyhole using a deep learning-based semantic segmentation approach in penetration status monitoring

We present a method for transmission and processing of real-time trans-continental medical data streams. We apply fundamentals of existing network technologies to create a secure tunnel from a remote hospital through an open-network to the Artemis Cloud. We capture and store incoming 1Hz data stream in our real-time event stream processor to allow for online real-time monitoring of the patient status. The contributions of this paper extend the Critical Care as a Service paradigm by incorporating remote monitoring centers. The results establish feasibility of the system to support real-time monitoring. However, existing protocols were required significant optimization to account for variability in throughput and availability of the network.

Cloud framework for real-time synchronous physiological streams to support rural and remote Critical Care

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Real-time identification of molten pool and keyhole using a deep learning-based semantic segmentation approach in penetration status monitoring

Cloud framework for real-time synchronous physiological streams to support rural and remote Critical Care