A lubricant is a substance that lessens friction and wear between mechanical parts. A good quality lubricating oil is required for protecting the mechanical equipment and reducing energy consumption. Its performance index (friction coefficient) could be measured by the four-ball wear test. For the morphological analysis of testing data from the four-ball wear test, determination of wear angle is very important for measuring the wear spot diameter, validity of the four-ball wear test and recognition of abnormal wear scar. On the basis of character analysis and image processing technology, a novel wear angle determination algorithm for wear scar is presented in this article. The direction of the wear scar (wear angle) is determined based on angle transformation and grey consistency (the smallest change of grey level). Simulation results show that the algorithm has high precision, good robustness and less time consuming. The average absolute error of our method is 0.8° and the absolute error of 95% of samples is <4°. The average runtime per frame is 3.896 s in the simulation platform.

The shape characteristics of wear spot in the four-ball wear test are often qualitatively identified by human testers based on experience, which are subjective, unquantifiable and biased. This paper proposes an automated method for extracting and expressing the shape features, which is effective for the deformed classification of the wear images. First, the wear spot is segmented from the background, and the geometric parameters of the wear spot are extracted. Based on this, the optimal standard wear spot which match the segmented (or actual) wear spot best is constructed. The established feature variables (i.e. abnormality rate and eccentricity rate) are used to quantitatively assess the deformation degree of segmented wear spot, by comparing the segmented wear spot with the constructed standard wear spot. Finally, Kmeans clustering algorithm is used to verify the validity of deformation features in the classification application. Simulation test results show that feature variables are valuable for identifying the deformation of wear images, and three effect evaluation indicators (CH/SI/DB) shows that the classification effect of the wear images is better.

The arrival delay of connecting flights in a hub-and-spoke network (HSN) can increase operational costs and degrade the level of service, particularly for transfer passengers. Developing a sustainable and resilient schedule by incorporating suitable buffer times can significantly improve the likelihood of successful connections. This paper presents a mathematical model that considers probabilistic flight arrivals at the hub airport to minimize total costs, including those associated with aircraft, gate utilization, and passenger delays. A dual-step search algorithm is developed to determine the optimal buffer times. The model is applied in a case study to optimize the buffer times of connecting flights at a hub airport in Xi’an, China. Additionally, the impacts of model parameters on decision variables and total costs are analyzed. Results indicate that implementing the proposed model can significantly enhance HSN resilience while reducing operational and user costs.

In order to avoid subjective estimation of the direction of groove by using a tester in the four‐ball friction test, an automatic wear angle detection method based on gray difference of inclination angle is proposed in this paper. First, the gray difference between the pixel and its W × 2W neighborhood pixels is acquired, in which each neighborhood pixel corresponds to an inclination angle. Second, a row’s grayscale difference of inclination angle is calculated, which is the sum of grayscale difference of all pixels in the same row with the same inclination angle. Third, the inclination angle corresponding to the minimum row’s grayscale difference is defined as the approach angles. Finally, the first t approaching angles with the highest frequency determine the wear angles. Compared with true angles determined manually, the simulation results of 200 samples show that the average absolute error is 2.1238° and the average running time is 1.3 s per frame. The influence of the algorithm parameters on detection precision is also analyzed.