The original brain storm optimization (BSO) fails to consider some potential information interactions in its individual update pattern, causing the premature convergence for complex problems. To address this problem, we propose a BSO algorithm with multi-information interactions (MIIBSO). First, a multi-information interaction (MII) strategy is developed, thoroughly considering various information interactions among individuals. Specially, this strategy contains three new MII patterns. The first two patterns aim to reinforce information interaction capability between individuals. The third pattern provides interactions between the corresponding dimensions of different individuals. The collaboration of the above three patterns is established by an individual stagnation feedback mechanism, contributing to preserve the diversity of the population and enhance the global search capability for MIIBSO. Second, a random grouping (RG) strategy is introduced to replace both the K-means algorithm and cluster center disruption of the original BSO algorithm, further enhancing the information interaction capability and reducing the computational cost of MIIBSO. Finally, a dynamic difference step-size (DDS), which can offer individual feedback information and improve search range, is designed to achieve an effective balance between global and local search capability for MIIBSO. By combining the MII strategy, RG, and DDS, MIIBSO achieves the effective improvement in the global search ability, convergence speed, and computational cost. MIIBSO is compared with 11 BSO algorithms and five other algorithms on the CEC2013 test suit. The results confirm that MIIBSO obtains the best global search capability and convergence speed amongst the 17 algorithms.

In this paper, we present a novel interactive cutting simulation model for soft tissue based on the meshless framework. Unlike most existing methods that consider the cutting process of soft tissue in an over simplified manner, the presented model is able to simulate the complete cutting process that includes three stages: deformation before cutting open, cutting open, and deformation after cutting open. To characterize the complicated physical and mechanical properties of soft tissue, both nonlinearity and viscoelasticity were incorporated into the equations governing the motion of soft tissue. A line contact model was used for simulating the cutting process after analyzing the two major types of surgical instruments, i.e., scalpel and electrostick. The cutting speed and angle were taken into account in order to improve haptic rendering. Biomechanical tests and simulation experiments verified the validity of the introduced model. Specifically, the displacement versus cutting force curves can be divided into three segments corresponding to the three stages of the cutting process. The results were also applied in a liver cutting simulating system and satisfactory visual effect and haptic feedback were achieved.

In the present work, the majority of implemented virtual surgery simulation systems have been based on either a mesh or meshless strategy with regard to soft tissue modelling. To take full advantage of the mesh and meshless models, a novel coupled soft tissue cutting model is proposed. Specifically, the reconstructed virtual soft tissue consists of two essential components. One is associated with surface mesh that is convenient for surface rendering and the other with internal meshless point elements that is used to calculate the force feedback during cutting. To combine two components in a seamless way, virtual points are introduced. During the simulation of cutting, the Bezier curve is used to characterize smooth and vivid incision on the surface mesh. At the same time, the deformation of internal soft tissue caused by cutting operation can be treated as displacements of the internal point elements. Furthermore, we discussed and proved the stability and convergence of the proposed approach theoretically. The real biomechanical tests verified the validity of the introduced model. And the simulation experiments show that the proposed approach offers high computational efficiency and good visual effect, enabling cutting of soft tissue with high stability.

Brass (e.g., H80 brass) is widely used in different industrial sectors. To ensure its quality and reliability, the stress damage state of brass has to be monitored and analyzed in practice. This article introduces a new micro-magnetic method for monitoring and analyzing the tensile stress damage state of brass. Specifically, the stress-magnetic flux density model of H80 brass in the micro-magnetic environment was developed based on the stress-magnetic coupling effect. The magnetic flux density value was then used as an indicator of the different stages of tensile damage, and a new model of the spontaneous magnetic signal and structural deformation of H80 brass in the tensile process was established. The tensile test results show that the strain of H80 brass in the elastic deformation stage is 0%-5%, there is a small amount of dislocation, and the magnetic flux density does not change significantly; the strain increases to 5%-48% in the work-hardening stage, the dislocation density increases, a dislocation wall is formed at the internal lattice, and the magnetic flux density decreases with increasing tensile stress; the strain continues to increase to 48%-52% in the necking fracture stage and the internal lattice. When the strain at the necking fracture stage continues to increase to 48%-52%, the dislocation density of the internal lattice gradually stabilizes, accompanied by the appearance of sub-slip bands, and the number and density of slip bands increase; the magnetic flux density drops abruptly when fracturing occurs. These results show that the developed micro-magnetic method is accurate and reliable for monitoring and analyzing the tensile stress damage state of brass.

This study proposes a weld defect inspection method based on a combination of machine vision and weak magnetic technology to inspect the quality of weld formation comprehensively. In accordance with the principle of laser triangulation, surface information about the weldment is obtained, the weld area is extracted using mutation characteristics of the weld edge and an algorithm for identifying defects with abnormal average height in the weld surface is proposed. Subsequently, a welding seam inspection process is developed and implemented, which is composed of a camera, a structured light sensor, a magnetic sensor and a motion control system. Inspection results from an austenitic stainless steel weldment show that the method combining machine vision and magnetism can identify defect locations accurately. Comprehensive analysis of the test results can effectively classify surface and internal defects, estimate the equivalent sizes of defects and evaluate the quality of weld formation in multiple dimensions.

In view of the problems of poor safety, slow detection speed, and low accuracy of existing nondestructive testing (NDT) technologies, such as X-ray methods and ultrasonic detection in detecting electron beam weld defects in aluminum alloys, this study proposes a weak magnetic NDT method based on the geomagnetic field. Firstly, the finite element analysis method was used to establish a simulation model of aluminum alloy electron beam welding defects, and the distribution characteristics of the magnetic field around weld defects, such as cracks and pores, were obtained. Then, the magnetic anomaly signal at the crack weld was identified by combining the wavelet transform and the least squares method. Finally, experimental tests show that the proposed method can safely, quickly, and accurately detect the defects of aluminum alloy electron beam welds.