The powder metallurgy process of the Al–graphene system is conducted by molecular dynamics (MD) simulations to investigate the role of graphene. During the sintering process, graphene is considered to reduce the pore size and metal grain size based on the volume change and atomic configuration of the Al parts in the composite. Compared with the pure Al system, the space occupied by the same number of Al atoms in the sintered composite is 15–20 nm³ smaller, and the sintered composite has about 5000 fewer arranged atoms. Because these models are carefully designed to avoid a serious deformation of graphene in the tension of sandwich-like composite models, the strengthening mechanism close to the experimental theory where graphene just serves to transfer a load can be studied dynamically. The boundary comprising of two phases is confirmed to hinder the motion of dislocations, while the crack grows along the interface beside graphene, forming a fracture surface of orderly arranged Al atoms. The results indicate that single-layer graphene (SLG) gives rise to an increase of 1.2 or 0.4 GPa in tensile strength when stretched in in-plane or normal direction, while bilayer graphene (BLG) brings a clear rise of 1.2–1.3 GPa in both directions. In both in-plane and normal stretching directions, the mechanical properties of the composite can be improved clearly by graphene giving rise to a strong boundary, new crack path, and more dense structure.

This paper studies the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment and guides the kinematic dynamic simulation modeling of industrial robots in the Industry 4.0 environment in the context of the research. To address the problem that each parameter error has different degrees of influence on the end position error, a method is proposed to calculate the influence weight of each parameter error on the end position error based on the MD‐H error model. The error model is established based on the MD‐H method and the principle of differential transformation, and then the function of uniform variation of six joint angles with time t is constructed to ensure that each linkage geometric parameter is involved in the motion causing error accumulation. Through the analysis of the robot marking process, the inverse solution is optimized for multiple solutions, and a unique engineering solution is obtained. Linear interpolation, parabolic interpolation, polynomial interpolation, and spline curve interpolation are performed on the results after multisolution optimization in the joint angle, and the pros and cons of various interpolation results are analyzed. The trajectory planning and simulation of industrial robots in the Industry 4.0 environment are carried out by using a special toolbox. The advantages and disadvantages of the two planning methods are compared, and the joint space trajectory planning method is selected to study the planning of its third and fifth polynomials. The kinetic characteristics of the robot were simulated and tested by experimental methods, and the reliability of the simulation results of the kinetic characteristics was verified. The kinematic solutions of industrial robots and the results of multisolution optimization are simulated. The methods, theories, and strategies studied in this paper are slightly modified to provide theoretical and practical support for another dynamic simulation modeling of industrial robot kinematics with various geometries.

Rolling bearings are among the most vulnerable components in various types of rotary machinery, and accurate fault detection and localization are essential. When a rolling bearing fails, the signal is non-stationary, and the energy distribution of the vibration signal varies depending on the fault location. In traditional k-nearest neighbor (KNN) fault diagnosis algorithms, Euclidean distance is primarily used to measure the distance between sample points, which is not effective at capturing similarity across different spatial distributions. Moreover, these algorithms assume equal feature importance, which does not reflect the actual characteristics of fault vibration signals. This study proposes a KNN-based rolling bearing fault diagnosis method that incorporates distribution discrepancy and differential feature importance. First, vibration signals are decomposed using three-level wavelet packet decomposition, and the energy of each node at the third level is used as the fault feature. Then, the mean impact value (MIV) algorithm is used to determine the relative importance of each feature, and the Earth mover's distance (EMD) is applied to measure differences between spatial distributions. By integrating Euclidean distance with MIV and EMD and applying the KNN majority voting rule, fault diagnosis is performed. The experimental results indicate that this method achieves a diagnostic accuracy of 99.43%, representing a 5.97% improvement compared to traditional KNN methods. The proposed method demonstrates accurate and effective fault diagnosis performance on the rolling bearing datasets used in this study.