Advanced ceramics, such as alumina and zirconia, are widely used in modern industries, due to their superior properties, such as high hardness and strength. Fracture toughness is a significant property that describes the capability of materials to resist crack instability and expansion to failure, and also helps when calculating the allowable working stress, and crack size, of the component. This paper comprehensively lists the current toughness-testing methods. With comparative investigations of various methods, edge chipping is found to be the simplest way of measuring the ceramic fracture-toughness, in terms of the dominant median crack, chip geometrical similarity, and force-distance relations, giving consideration to its potential application in industry. Moreover, to avoid the data variance from crack-length measurement in edge chipping, it is further proposed that the energy analyses can be used to improve the way in which the toughness expression is formulated.
Concrete wastewater from mixing stations leads to environment contamination due to its high alkalinity. The wastewater can be reused if its solid content is accurately and timely detected. However, investigations into the traditional methods for wastewater reuse have demonstrated that they are time consuming and not efficient. Therefore, the exact acquirement of solid content in concrete wastewater becomes a necessity. Recent studies have shown that deep learning has been successfully applied to detect the concentration of chemical solutions and the particle content of suspending liquid. Moreover, deep learning can also be used to recognize the accurate water level, which facilitates the detection of the solid–liquid separation surface after wastewater sedimentation. Therefore, in this article the feasibility and challenges of applying deep learning to detect the solid content of concrete wastewater were comprehensively evaluated and discussed. Finally, an experimental setup was proposed for future research, and it indicated that transfer learning, data augmentation, hybrid approaches, and multi-sensor integration techniques can be selected to facilitate future experimental performances.
Abstract. Non-destructive stress measurement is necessary to provide safety
maintenance in some extreme machining environments. This paper reports a
case study that reveals the potential application of automatic metal stress
monitoring with the aid of the magnetic Barkhausen noise (MBN) signal and deep
learning algorithms (convolutional neural network, CNN, and long short-term memory, LSTM). Specifically, we applied the
experimental magnetic signals from steel samples to validate the
feasibility and efficiency of two deep learning models for stress
prediction. The results indicate that the CNN model possesses a faster training
speed and a better test accuracy (91.4 %), which confirms the feasibility of automatic stress monitoring applications.
Advanced structural materials have been widely used in modern industries, such as mining, building, aerospace, chip manufacturing and surface engineering [...]
In this paper, we propose an Electrocardiogram (ECG) classification model based on FFC (Fast Fourier Convolution) and ResNet. The model utilizes FFC and ResNet for feature extraction and classification. We further improve the network performance and convergence speed through batch normalization and residual concatenation. The experimental results demonstrate that the model exhibits excellent classification performance under different data distributions in the PTB-XL database and trains faster than traditional ResNet models. Additionally, we introduce a new module, FFC-R, and validate its excellent performance in ECG classification tasks. This innovation is expected to provide powerful support for the diagnosis and treatment of heart diseases.
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