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In recent years, with the increase in computing power, artificial intelligence can gradually be regarded as intelligent agents and interact with humans, this interactive network has become increasingly complex. Therefore, it is necessary to model and analyze this complex interactive network. This paper aims to model and demonstrate the evolution of crowd intelligence using visual complex networks.

This paper uses the complex network to model and observe the collaborative evolution behavior and self-organizing system of crowd intelligence.

The authors use the complex network to construct the cooperative behavior and self-organizing system in crowd intelligence. Determine the evolution mode of the node by constructing the interactive relationship between nodes and observe the global evolution state through the force layout.

The simulation results show that the state evolution map can effectively simulate the distribution, interaction and evolution of crowd intelligence through force layout and the intelligent agents’ link mode the authors proposed.

Based on the complex network, this paper constructs the interactive behavior and organization system in crowd intelligence and visualizes the evolution process.

Artificial intelligence is gradually penetrating into human society. In the network era, the interaction between human and artificial intelligence, even between artificial intelligence, becomes more and more complex. Therefore, it is necessary to describe and intervene the evolution of crowd intelligence network dynamically. This paper aims to detect the abnormal agents at the early stage of intelligent evolution.

In this paper, differential evolution (DE) and K-means clustering are used to detect the crowd intelligence with abnormal evolutionary trend.

This study abstracts the evolution process of crowd intelligence into the solution process of DE and use K-means clustering to identify individuals who are not conducive to evolution in the early stage of intelligent evolution.

Experiments show that the method we proposed are able to find out individual intelligence without evolutionary trend as early as possible, even in the complex crowd intelligent interactive environment of practical application. As a result, it can avoid the waste of time and computing resources.

In this paper, DE and K-means clustering are combined to analyze the evolution of crowd intelligent interaction.

The purpose of this paper is to supplement and upgrade existing research on LPBF of NiTi alloys. Laser powder bed fusion (LPBF) is a promising method for fabricating nickel–titanium (Ni–Ti) alloys. It is well known that the energy density is mainly adjusted through the scanning speed and laser power. Nevertheless, there is lack in research on the effects of separately adjusting the scanning speed and laser power on the properties of the final Ni–Ti components. On the other hand, although Ni-rich Ni–Ti alloys [such as Ni54(at.%)Ti] have great potential in structural applications because of their high hardness and good shape stability, at present, there are few studies focusing on this grade of Ni–Ti alloy.

In this work, the energy density was adjusted by changing the laser power and scanning speed separately, and the corresponding process parameters were used to fabricate Ni54(at.%)Ti alloys. The formability (including the relative density, impurity content, etc.) and tensile properties of the LPBF Ni54(at.%)Ti alloys fabricated with different combinations of process parameters were analyzed.

The effects of increasing the laser power and reducing the scanning speed on the properties of the LPBF Ni54(at.%)Ti alloys and the property differences between components manufactured with different combinations of laser power and scanning speed under the same energy density were analyzed. The optimal process parameters were selected to fabricate the components that achieved the highest ultimate tensile strength of 537 MPa, a high relative density of 98.23%, a relatively low impurity content (0.073 Wt.% of carbon and 0.06 Wt.% of oxygen) and an ideal pseudoelasticity (95% recovery rate loaded at 300 MPa).

The effects of increasing the laser power and reducing the scanning speed on the properties of LPBF Ni54(at.%)Ti alloys were studied in this paper. This work is an upgrade and supplement to the existing research on fabricating Ni-rich Ni–Ti alloys by the LPBF method.