Based on the central composite rotatable design (CCD), clad beads of Inconel 625 were deposited on the surface of AISI 4130 plates using hot wire pulsed TIG (tungsten inert gas arc welding) technique. The response surface methodology (RSM) was used to establish models between process parameters and geometrical characteristics of the clad beads. Then, multiple-track two-layer weld overlay was deposited using the optimized process parameters. The microstructure of the weld overlay is primarily composed of columnar dendrites, and there are also a few planar crystals and cellular dendrites near the fusion zone. Meanwhile, equiaxed grains and steering dendrites are mainly distributed in the upper portion of the weld overlay. Potentiodynamic polarization tests were used to evaluate the corrosion resistance of the weld overlay and the substrate. The results show that adding clad layers can enhance the corrosion resistance, which degrades with the increase in Fe dilution. Moreover, the corrosion resistance of the second layer surface is close to that of wrought Inconel 625.

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Collecting multi-channel sensory signals is a feasible way to enhance performance in the diagnosis of mechanical equipment. In this article, a deep learning method combined with feature fusion on multi-channel sensory signals is proposed. First, a deep neural network (DNN) made up of auto-encoders is adopted to adaptively learn representative features from sensory signal and approximate non-linear relation between symptoms and fault modes. Then, Locality Preserving Projection (LPP) is utilized in the fusion of features extracted from multi-channel sensory signals. Finally, a novel diagnostic model based on multiple DNNs (MDNNs) and softmax is constructed with the input of fused deep features. The proposed method is verified in intelligent failure recognition for automobile final drive to evaluate its performance. A set of contrastive analyses of several intelligent models based on the Back-Propagation Neural Network (BPNN), Support Vector Machine (SVM) and the proposed deep architecture with single sensory signal and multi-channel sensory signals is implemented. The proposed deep architecture of feature extraction and feature fusion on multi-channel sensory signals can effectively recognize the fault patterns of final drive with the best diagnostic accuracy of 95.84%. The results confirm that the proposed method is more robust and effective than other comparative methods in the contrastive experiments.