Industrial CT is useful for defect detection, dimensional inspection and geometric analysis. While it does not meet the needs of industrial mass production, because of its time-consuming imaging procedure. This article proposes a novel stationary real-time CT system with multiple X-ray sources and detectors, which is able to refresh the CT reconstructed slices to the detector frame frequency. This kind of structure avoids the movement of the X-ray sources and detectors. Projections from different angles can be acquired with the objects' translation, which makes it easier to be integrated into pipeline. All the detectors are arranged along the conveyor, and observe the objects in different angle of view. With the translation of objects, their X-ray projections are obtained for CT reconstruction. To decrease the mechanical size and reduce the number of X-ray sources and detectors, the FBP reconstruction algorithm was combined with deep-learning image enhancement. Medical CT images were applied to train the deep-learning network for its quantity advantage in comparison with industrial ones. It is the first time to adopt this source-detector layout strategy. Data augmentation and regularization were used to elevate the generalization of the network. Time consumption of the CT imaging process was also calculated to prove its high efficiency. It is an innovative design for the 4th industrial revolution, providing an intelligent quality inspection solution for digital production. Keywords non-destructive testing • defect inspection • deep-learning • automated production line • real-time CT • parallel computing

In order to count stacked-sheet in real time, a non-contact method based on broadband X-ray absorption spectra (XAS) and long short-term memory (LSTM) network was proposed. Five hundred sheets of standard A4 printing papers (70 g/m2) were taken as experimental samples. The broadband XAS detection equipment was used to scan the papers leading to 500 broadband XAS data, and the data were preprocessed by principal component analysis (PCA). LSTM was built to count stacked papers, and compared with polynomial fitting model(PFM) and artificial neural network (ANN) to verify the difference in prediction accuracy. Mean square error (MSE), Mean absolute error (MAE), Max-error (MAXE) and Coefficient of determination (R2) were selected as evaluation indexes of above models. The experimental results showed that the proposed approach can count stacked-sheet accurately with the MAE was 1.0895 and the prediction time was less than 0.006 second. All the index results of LSTM were better than those of PFM and ANN. Therefore, this study using broadband XAS and LSTM realized real-time stacked-sheet counting, and provided a new idea for thickness measurement field.

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Stacked sheets counting is an important segment in the printing and packaging industry. It can meet the strict quality control and avoid great economic loss. Traditional counting methods based on photoelectric sensors or image processing face the challenges of low efficiency, breakage, and low contrast. In this paper, a non-contact and real-time counting method was developed by combining broadband X-ray absorption spectra (XAS) with long short-term memory network (LSTM). First, 500 sheets of standard A4 (70 g/m 2 ) printing paper stacked one by one were scanned by the broadband XAS detection equipment. Second, the collected 500 broadband XAS data were pre-processed by principal component analysis (PCA) to reduce the data dimension. Finally, LSTM was constructed to extract the temporal features of XAS data and establish a relationship with the number of paper sheets; meanwhile, polynomial fitting model(PFM) and artificial neural network (ANN) were proposed to compare with LSTM. The results showed that the combination of broadband XAS and LSTM had a maximum error of 1.8504 sheets and a single measurement time of 0.006 sec. To the best of our knowledge, this work was the first study to analyze and utilize the broadband XAS and LSTM for counting task. It provided a new non-contact and real-time counting method for stacked sheets.