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This paper aims to enhance the corrosive wear and electrochemical corrosion of Ti–6Al–4V alloy.

A CoCrAlYTaSi alloy coating was fabricated on Ti–6Al–4V alloy using a laser thermal spraying (LTS). The surface and cross-section morphologies, chemical elements, phases and bonding strength of the obtained coating were analyzed using a scanning electron microscope, energy dispersive spectroscope, X-ray diffraction and scratch test, respectively, The corrosive wear and electrochemical corrosion of CoCrAlYTaSi coating in 3.5 Wt.% NaCl solution were investigated using a wear tester and electrochemical workstation, respectively.

The average coefficient of frictions (COFs) of CoCrAlYTaSi coating under the wear loads of 2, 4 and 6 N are 1.31, 1.02 and 0.88, respectively; and the corresponding wear rates are 0.66 × 10⁻⁴, 1.10 × 10⁻⁴ and 1.30 × 10⁻⁴ mm³·N^–1·m^–1, respectively. The wear mechanism under the wear load of 2 N is abrasive wear, while those under the wear loads of 4 and 6 N are adhesive wear and abrasive wear. The charge transfer resistance of CoCrAlYTaSi coating is 5.368 × 105 Ω·cm², higher than 2.193 × 105 of the substrate.

In this study, a CoCrAlYTaSi coating was firstly fabricated on Ti–6Al–4V alloy using a LTS. Its corrosive wear and electrochemical corrosion in 3.5 Wt.% NaCl solution were investigated, which played a protective role of corrosive wear on Ti–6Al–4V alloy.

The behavioral decision-making of digital-self is one of the important research contents of the network of crowd intelligence. The factors and mechanisms that affect decision-making have attracted the attention of many researchers. Among the factors that influence decision-making, the mind of digital-self plays an important role. Exploring the influence mechanism of digital-selfs’ mind on decision-making is helpful to understand the behaviors of the crowd intelligence network and improve the transaction efficiency in the network of CrowdIntell.

In this paper, the authors use behavioral pattern perception layer, multi-aspect perception layer and memory network enhancement layer to adaptively explore the mind of a digital-self and generate the mental representation of a digital-self from three aspects including external behavior, multi-aspect factors of the mind and memory units. The authors use the mental representations to assist behavioral decision-making.

The evaluation in real-world open data sets shows that the proposed method can model the mind and verify the influence of the mind on the behavioral decisions, and its performance is better than the universal baseline methods for modeling user interest.

In general, the authors use the behaviors of the digital-self to mine and explore its mind, which is used to assist the digital-self to make decisions and promote the transaction in the network of CrowdIntell. This work is one of the early attempts, which uses neural networks to model the mental representation of digital-self.