Surface characterization and corrosion behavior of 90/10 copper-nickel alloy in seawater from Xiamen bay at 30 °C for 56 days were investigated in this study. The results indicated that the corrosion product layer was mainly a mixture of CuO, Cu₂O, and Cu(OH)₂, with a transition to CuCl, CuCl₂, and Cu₂(OH)₃Cl during the corrosion process. However, as corrosion proceeds, the resistance of the product film was reduced due to its heterogeneous and fairly porous structures, which led to local corrosion of the alloy. The corrosion potentials (E_corr) increase while corrosion current densities (I_corr) decrease with time because of the formation of protective oxide film.

The relationships between the fatigue crack growth rate $\left(da/dN\right)$ and stress intensity factor range $\left(\Delta K\right)$ are not always linear even in the Paris region. The stress ratio effects on fatigue crack growth rate are diverse in different materials. However, most existing fatigue crack growth models cannot handle these nonlinearities appropriately. The machine learning method provides a flexible approach to the modeling of fatigue crack growth because of its excellent nonlinear approximation and multivariable learning ability. In this paper, a fatigue crack growth calculation method is proposed based on three different machine learning algorithms (MLAs): extreme learning machine (ELM), radial basis function network (RBFN) and genetic algorithms optimized back propagation network (GABP). The MLA based method is validated using testing data of different materials. The three MLAs are compared with each other as well as the classical two-parameter model ( $K^{*}$ approach). The results show that the predictions of MLAs are superior to those of $K^{*}$ approach in accuracy and effectiveness, and the ELM based algorithms show overall the best agreement with the experimental data out of the three MLAs, for its global optimization and extrapolation ability.

Rolling bearing is one of the most critical components in rotating machinery, so in order to efficiently select features, reduce feature dimensions and improve the correctness of fault diagnosis, a feature selection and fusion method based on weighted multi-dimensional feature fusion is proposed. Firstly, features are extracted from different domains to constitute the original high-dimensional feature set. Considering the large number of invalid and redundant features contained in such original feature set, a feature selection process that combines with support vector machine (SVM) single feature evaluation, correlation analysis and principal component analysis-weighted load evaluation (PCA-WLE) is put forward in this paper for selecting sensitive features. The selected features are weighted and fused according to their sensitivity so as to further weaken the interference of low important features. Finally, this process is applied to the data provided by the Case Western Reserve University Bearing Data Center and Xi’an Jiaotong University School of Mechanical Engineering, respectively, and the fault is diagnosed by using the particle swarm optimization-support vector machine (PSO-SVM). The results show that this method can accurately identify different fault categories and degrees of bearing, which is superior and practical than single-domain fault diagnosis with higher recognition ability.

Degradation tests of hydrogenated nitrile rubber seals, often used as sealing components in hydraulic systems, were conducted under the free and compression state in air and hydraulic oil at three elevated temperatures for several days to investigate the synergistic effects among three factors. The crosslinking and chain scission reactions both occurred simultaneously at higher temperature during the degradation process, and crosslinking predominated for most cases. Additionally, the synergistic effect between compression stress and hydraulic oil further slowed the degradation rate by limiting oxygen access. However, the higher temperature and hydraulic oil both promoted the formation of oxidation products, whereas the compression stress restrained the formation of amide groups. The fracture morphology results show that the defects gradually formed on the fracture surface, especially for the uncompressed specimens. The increase of the compression set aged in air was more than that in hydraulic oil, implying the more serious degradation. Moreover, rubber seals under the synthetic effect of three environmental factors presented the minimum degradation level. The degradation of the compressed and uncompressed specimens exposed to hydraulic oil is more serious than that of specimens exposed to air.

We propose a peak seeking algorithm to extract the damage characteristic-variation of central wavelength to monitor the crack damage status in aluminum alloy plates using surface bonded fiber Bragg grating (FBG) sensors. The FBG sensors are sensitive to the uniform and non-uniform strain distribution along their longitudinal direction, and the effect appears in the power spectrum of the reflected light from the gauge section. In this paper, we propose a fast-self-adaptive multi-peak seeking algorithm to detect the central wavelength shifting of the FBG reflection spectrum with the crack propagation. The proposed peak searching algorithm results point to a significant improvement compared to other conventional methods. Then the central wavelength shifting is applied to explain the crack propagation behavior of the aluminum plates under quasi-static tensile test conditions. The different damages feature changing intervals which are associated with the crack position and the FBGs location, demonstrating that central wavelength shifting performs as an indicator to detect structural crack damage.

In manufacturing, cutting tools gradually wear out during the cutting process and decrease in cutting precision. A cutting tool has to be replaced if its degradation exceeds a certain threshold, which is determined by the required cutting precision. To effectively schedule production and maintenance actions, it is vital to model the wear process of cutting tools and predict their remaining useful life (RUL). However, it is difficult to determine the RUL of cutting tools with cutting precision as a failure criterion, as cutting precision is not directly measurable. This paper proposed a RUL prediction method for a cutting tool, developed based on a degradation model, with the roughness of the cutting surface as a failure criterion. The surface roughness was linked to the wearing process of a cutting tool through a random threshold, and accounts for the impact of the dynamic working environment and variable materials of working pieces. The wear process is modeled using a random-effects inverse Gaussian (IG) process. The degradation rate is assumed to be unit-specific, considering the dynamic wear mechanism and a heterogeneous population. To adaptively update the model parameters for online RUL prediction, an expectation–maximization (EM) algorithm has been developed. The proposed method is illustrated using an example study. The experiments were performed on specimens of 7109 aluminum alloy by milling in the normalized state. The results reveal that the proposed method effectively evaluates the RUL of cutting tools according to the specified surface roughness, therefore improving cutting quality and efficiency.