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The purpose of this work is to comprehensively reveal the spatial distribution and evolution features of a running-in attractor.

The friction coefficient signals extracted from wear experiments are reconstructed. A projected matrix is obtained based on the reconstructed matrix. Then the approach of three-dimensional (3D) histogram of phase points is proposed, which is used to intuitively characterize the complex properties of the running-in attractor.

The space occupied by the running-in attractor gradually contracts, then stabilizes and finally expands; the maximum of phase points number in a certain bin initially decreases, then keeps stable and finally increases rapidly; yet the percentage of bins number storing phase points shows an inverse variation tendency. Consequently, 3D histogram evolves from a nonuniform state to a uniform state then returns back to the nonuniform state, which indicates the evolution rule of “formation, stabilization and disappearance” of the running-in attractor.

Characterization on the features of the running-in attractor can provide valuable information about friction systems and their dynamic behaviors.

The purpose of this study is to solve the issues of time-consuming and complicated computation of traditional measures, as well as the underutilization of two-dimensional (2D) phase-trajectory projection matrix, so a new set of features were proposed based on the projection of attractors trajectory to characterize the friction-induced attractors and to reveal the tribological behavior during the running-in process.

The frictional running-in experiments were conducted by sliding a ball against a static disk, and the friction coefficient was collected to reconstruct the friction-induced attractors. The projection of the attractors in 2D subspace was then mapped and the distribution of phase points was adapted to conduct the feature extraction.

The evolution of the proposed moment measures could be described as “initial rapid decrease/increase- midterm gradual decrease/increase- finally stable,” which could effectively reveal the convergence degree of the friction-induced attractors. Moreover, the measures could also describe the relative position of the attractors in phase–space domain, which reveal the amplitude evolution of signals to some extent.

The proposed measures could reveal the evolution of tribological behaviors during the running-in process and meet the more precise real-time running-in status identification.

The purpose of this paper is to study the correlation between the dynamic features of the running-in attractor and the wear particle group, so as to characterize the running-in attractor by means of the wear particle group.

Wear particles are collected in phased wear experiments, and their dynamic features are investigated by the equivalent mean chord length L. Then, the correlation between the equivalent mean chord length L and the correlation dimension D of the running-in attractor is studied.

In the wear process, the equivalent means chord length L first decreases, then remains steady, and finally increases, this process agrees with the increase, stabilization and decrease of the correlation dimension D. Therefore, the wear particle group has a dynamic nature, which characterizes the formation, stabilization, and disappearance of a running-in attractor. Consequently, the dynamic characteristics and evolution of a running-in attractor can be revealed by the wear particle group.

The intrinsic relationship between the wear particle group and the running-in attractor is proved, and this is advantageous for further revealing the dynamic features of the running-in attractor and identifying the wear states.

The purpose of this paper is to study the dynamic features of friction coefficient during running-in state based on recurrence analysis, so as to recognize the running-in state of crankshaft journal bearings.

The friction coefficient was measured in the friction experiments and the dynamic features are analyzed by recurrence plots (RPs), unthreshold recurrence plots (URPs) and recurrence quantification analysis.

During the running-in process, RPs have gone through disrupted patterns, drift patterns and homogeneous patterns successively. URP shows that the phase trajectory spirals in the disrupted pattern gradually converge in the drift pattern and remain stable in the homogeneous pattern. Three independent measures, recurrence rate, entropy and laminarity, are chosen to characterize friction coefficient from the perspective of point, diagonal line and vertical line structures of the RPs.

The results provide a feasible way to monitor the running-in process and recognize the running-in state.

This study aims to reconstruct the frictional vibration signal from noise and characterize the running-in process by frictional vibration.

There is a strong correlation between tangential frictional vibration and normal frictional vibration. On this basis, a new frictional vibration reconstruction method combining cross-correlation analysis with ensemble empirical mode decomposition (EEMD) was proposed. Moreover, the concept of information entropy of friction vibration is introduced to characterize the running-in process.

Compared with the wavelet packet method, the tangential friction vibration and the normal friction vibration reconstructed by the method presented in this paper have a stronger correlation. More importantly, during the running-in process, the information entropy of friction vibration gradually decreases until the equilibrium point is reached, which is the same as the changing trend of friction coefficient, indicating that the information entropy of friction vibration can be used to characterize the running-in process.

The study reveals that the application EEMD method is an appropriate approach to reconstruct frictional vibration and the information entropy of friction vibration represents the running-in process. Based on these results, a condition monitoring system can be established to automatically evaluate the running-in state of mechanical parts.

The EEMD method was applied to reconstruct the frictional vibration. Furthermore, the information entropy of friction vibration was used to analysis the running-in process.

Running-in is a transient process prior to steady state and of great importance for mechanical performance. To reveal the fractal behavior in the running-in process, the steel-on-steel friction and wear tests were performed.

The friction coefficient, friction temperature, friction noise and vibration were recorded, and the surface profile of lower sample was measured on line. The signals and profiles were characterized by correlation dimension and box-counting dimension, respectively.

The signals have the consistent fractal evolvement law, that is, the correlation dimension increases and tends to a stable value. The box-counting dimension of one surface becomes close to that of the other surface. The running-in process can be interpreted as a process in which the fractal dimension of friction signals increases, and the counter surfaces spontaneously adapt to and modify each other to form a spatial ordered structure.

The results reveal the running-in behavior from a new perspective.

As a key basic component used in machining, high-speed steel (HSS) tools often prone to wear and failure during machining. Therefore, the purpose of this study is to adopt a suitable approach to improve the stability of the cutting force, the service life and the wear resistance.

Laser shock processing (LSP) was used to process the tool rake face and the tribological test was performed with ball-on-disk wear tester.

Experimental results show that cutting force of the LSP-treated tool is lower than untreated tool under the same cutting conditions. Wear rate of the tool nose treated by LSP decreases obviously and the tool life increases by 40 per cent.

HSS is often used in the manufacture of complex cutting tools. The main value of this article is to improve the tool surface wear resistance, thereby extending the service life of cutter. This paper is valuable not only in theory but also with reference value in engineering practice.

AISI 52100-AISI 1045 specimens were used as the ring-on-disc tribopairs in the experiments to investigate the stability of friction process.

The coefficient of friction (COF) signals were measured throughout the friction process and the recurrence plots (RPs) and recurrence quantification analysis (RQA) are adapted to analyze the stability of the tribosystem.

The results show that the COF time-series acquired from different tests possess the same dynamic evolution laws. The evolution of RPs follows the rules of “disrupted-homogeneous-disrupted,” which corresponds to the “running-in, steady-state and increasing stages” of friction process. Additionally, the evolution laws of RQA measures LAM, Vmax and TT accord with the “bathtub curve.” Therefore, both RPs and RQA measures can inform quantitative interpretations of tribological behaviors and friction process identification.

The both RPs and RQA are capable of characterizing the tribological behaviors and can depict the various stages of friction process.

This article addresses issues associated with applications of ideas from “chaos theory” to educational administration and leadership as found in the literature. Implications are considered in relation to claims concerning the behaviour of non‐linear dynamic systems, and to the nature of the interpretations and recommendations that are made. To aid the analysis a simple non‐linear model is constructed and its behaviour simulated. Questions emerging from the analysis are used to focus on issues deemed significant, both for evaluating arguments presented on behalf of chaos, and for furthering insights aimed at enhancing the understanding and practice of leadership in organisations.

The purpose of this paper is to propose a modification of the original PSO algorithm in order to avoid the evaluation of the objective function outside the feasible set, improving the parallel performances of the algorithm in the view of its application on parallel architectures.

Classical PSO iteration is repeated for each particle until a feasible point is found: the global search is performed by a set of independent sub-iteration, at the particle level, and the evaluation of the objective function is performed only once the full swarm is feasible. After that, the main attractors are updated and a new sub-iteration is initiated.

While the main qualities of PSO are preserved, a great advantage in terms of identification of the feasible region and detection of the best feasible solution is obtained. Furthermore, the parallel structure of the algorithm is preserved, and the load balance improved. The results of the application to real-life optimization problems, where constraint satisfaction sometime represents a problem itself, gives the measure of this advantage: an improvement of about 10 percent of the optimal solution is obtained by using the modified version of the algorithm, with a more precise identification of the optimal design variables.

Differently from the standard approach, utilizing a penalty function in order to discharge unfeasible points, here only feasible points are produced, improving the exploration of the feasible region and preserving the parallel structure of the algorithm.