Wear and Corrosion Properties of Mo Surface-modified Layer in TiNi Alloy Prepared by Plasma Surface Alloying

In order to improve the wear resistance and restrain nickel release of TiNi alloys,the Mo modified layers on TiNi substrates were obtained using the double glow plasma surface alloying technique.Scanning electron microscopy（SEM）,glow discharge optical emission spectroscopy（GDOES） and X-ray diffraction（XRD） were employed to investigate the morphology,composition and structure.Microhardness test and scratch test were performed to analyze the microhardness and coating/substrate adhesion.Tribological and electrochemical behaviors of the Mo modified layers on TiNi were tested by the reciprocating wear instrument and electrochemical measurement system.The Ni concentrations in Hanks’ solution where surface electrochemical tests took place were measured by mass spectrometry.The surface-modified layer contained a Mo deposition layer and a Mo diffusion layer.The X-ray diffraction analysis revealed that the modified layers were composed of Mo,MoTi,Mo Ni,and Ti2Ni.The microhardnesses of the Mo modified layers treated at 900 ℃ and 950 ℃ were 832.8 HV and 762.4 HV,respectively,which was about 3 times the microhardness of the TiNi substrate.Scratch tests indicated that the modified layers possessed good adhesion with the substrate.Compared with as-received TiNi alloy,the modified alloys exhibited significant improvement of wear resistance against Si3N4 with low normal loads during the sliding tests.Mass spectrometry displayed that the Mo alloy layers had successfully inhibited the Ni release into the body.

Design of recognition algorithm for multiclass digital display instrument based on convolution neural network

Digital display instrument identification is a crucial approach for automating the collection of digital display data.In this study,we propose a digital display area detection CTPNpro algorithm to address the problem of recognizing multiclass digital display instruments.We developed a multiclass digital display instrument recognition algorithm by combining the character recognition network constructed using a convolutional neural network and bidirectional variable-length long short-term memory(LSTM).First,the digital display region detection CTPNpro network framework was designed based on the CTPN network architecture by introducing feature fusion and residual structure.Next,the digital display instrument identification network was constructed based on a convolutional neural network using twoway LSTM and Connectionist temporal classification(CTC)of indefinite length.Finally,an automatic calibration system for digital display instruments was built,and a multiclass digital display instrument dataset was constructed by sampling in the system.We compared the performance of the CTPNpro algorithm with other methods using this dataset to validate the effectiveness and robustness of the proposed algorithm.