With the development of the industrial internet of things (IIoT), the existing IIoT platform has gradually formed a large-scale, heterogeneous distributed environment. How to ensure information security and achieve product quality traceability is a big problem that needs to be solved. In view of the low transparency and centralization of current traceability information of steel products, based on the blockchain-based IIoT, using the alliance chain model and the Hyperledger platform, the efficient traceability system of steel products is developed in this paper. Experimental tests prove that production companies, logistics and consumers can participate in the information certification of steel products via the system; consumers can understand the real product manufacturing process, effectively avoiding the incomplete information and low transparency in the traditional information traceability process, and effectively trace the quality of steel products. The system provides an effective scheme for promoting the transformation and upgrading of the steel industry.

In current industrial practice, the finite element modeling of the metal cutting process is essential. In this paper, finite element analysis of conventional and high-speed cutting of 7075-T6 aluminum alloy is carried out. A finite element model of the 7075-T6 aluminum alloy was developed using the Johnson Cook instant on equation to investigate the milling behavior of the alloy under conventional and high-speed conditions. The cutting forces in the X-direction, Y-direction, and Z-direction were predicted analytically for five groups of different Johnson Cook models with different material constants, and the predicted results were compared with the experimentally determined cutting forces to investigate the influence of the Johnson Cook constitutive model parameters on the simulation of the cutting forces of the 7075-T6 aluminum alloy. The results showed that the constitutive model parameters are inconsistent for conventional and higher speed cutting conditions. Under conventional cutting conditions, the JC4 model predicts the material factor cutting forces in good agreement with the experimental results, while under high-speed cutting conditions, the JC5 model predicts the material factor cutting forces in good agreement with the experimental results, but that the finite element model has good applicability in predicting machining performance. Only the experimental data obtained by covering the real strain, strain rate and temperature range to determine the material constant of the Johnson Cook constitutive equation can accurately predict the cutting force in all directions.

We aim to address the issues of difficult acquisition of bearing fault data, few feature data sets, and low efficiency of intelligent diagnosis. In this paper, an orthogonal wavelet transform K-nearest neighbor (OWTKNN) diagnosis method has been proposed. The (OWT) method extracts the peaks of each detail signal as training samples and uses the K-Nearest Neighbor (KNN) method for fault classification. The classification results of the multiple fault test data obtained through rolling bearing tests show that the method can reach a fault recognition rate of 100, and compared with KNN without extracted eigenvalues, it significantly improves the classification effects from various unknown fault data of the bearing inner ring and ball, shortens classification time, and improves the intelligent diagnosis efficiency. In addition, it achieves an overall recognition rate exceeding 95, Comparing OWT, EMD, and VMD feature extraction methods, both the OWTKNN and k-center point clustering algorithm do not exceed 80 (KCA), also bearing testimony of the effectiveness of this method.