CrN, AlN, TiN layer were prepared as diffusion barriers between the K417 substrate and Ni+CrAlYSiN nano composite coatings via vacuum arc evaporation. Oxidation kinetics and microstructure evaluation of these nano coating systems at 1000 °C after 100 h were studied. Results show that the AlN layer showed good thermodynamic stability, effectively inhibited the interdiffusion between the coating and the substrate, improved the oxidation resistance of Ni+CrAlYSiN nano composite coatings, and a single-layer Al₂O₃ film was formed on the coating. The CrN layer was decomposed, which did not block the diffusion of elements and had little effect on the oxidation resistance of the Ni+CrAlYSiN nano composite coating. The TiN layer effectively prevented the interdiffusion between the coating and the substrate. However, it deteriorated the oxidation resistance of the composite coating. Similar to the Ni+CrAlYSiN coating without a diffusion barrier, a double-layer oxide film structure with Al₂O₃ as the inner layer and Ni(Al,Cr)₂O₄ as the outer layer formed on the Ni+CrAlYSiN nano composite coatings with the CrN or TiN diffusion barrier.

With the increasing complexity of shale gas extraction conditions, a large number of high-strength collapse-resistant casing is applied to the extraction of unconventional oil and gas resources. There are errors in the traditional API collapse strength formula. A high-precision and low-computational-cost model is needed for predicting the strength of high-collapsible casing. The key influencing factors of casing anti-collapse strength were determined as outer diameter, wall thickness, yield strength, ovality, wall thickness unevenness, and residual stress by analyzing the casing collapse mechanism. In response to the key factors mentioned above, a dataset was formed by measuring the geometric parameters of the full-size casing and collecting data on the results of the anti-collapse strength experiment, which was divided into a training set (70%) and a testing set (30%). Three machine-learning algorithms, a neural network, random forest, and support vector machine, were trained to predict the anti-extrusion strength. The correlation coefficient R², root mean square error RMSE, and average relative MRE were used to evaluate the indexes for model preference evaluation. The results show that machine-learning algorithms have unique advantages in casing anti-collapsing strength prediction. Within which, the neural network prediction model has the best prediction effect, and its characteristics of high precision, low cost and high efficiency are more suitable for the prediction of casing extrusion strength. Its testing set R² is 0.9733, RMSE is 0.0267 and MRE is 0.0782, and the prediction accuracy can reach 92.2% which is much higher than the API calculation result (63.3%). The network prediction model is suitable for casing anti-collapsing strength prediction and meets the actual prediction requirements.

Lead-free (Bi_1/2Na_1/2)(Ti_1−x(Mg_1/3Nb_2/3)_x)O₃ ceramics were synthesized using the solid-phase method, and the effects of varying (Mg_1/3Nb_2/3)⁴⁺ content, substituting for Ti⁴⁺ ions at the B-site of the BNT perovskite lattice, on piezoelectric performance were systematically investigated. The influence of sintering temperature on both piezoelectric and ferroelectric properties was also explored, revealing that sintering temperature significantly affects both the microstructure and the electrical properties of the ceramics. The results indicate that the incorporation of (Mg_1/3Nb_2/3)⁴⁺ significantly enhances the piezoelectric and ferroelectric properties of BNT ceramics. Specifically, a maximum piezoelectric constant of 91 pC/N was achieved at a sintering temperature of 1160 °C and a doping concentration of x = 0.01. By comparing the ferroelectric properties across different doping levels and sintering temperatures, this study provides valuable insights for further design and process optimization of BNT-based piezoelectric materials.

As the “flagship” unit of the petrochemical industry, the operational status of ethylene cracking furnaces directly impacts the stability and efficiency of the entire production chain. During long-term operation under extreme temperatures and complex reaction environments, cracking furnace tubes face core bottlenecks primarily related to thermal and coking effects, such as coke deposition, tube metal overheating, and associated creep damage, which restrict the long-term, safe, and efficient operation of the unit. This paper systematically reviews the key technologies for condition monitoring of cracking furnace tubes, providing an in-depth analysis of various monitoring methods—from traditional infrared thermometry and acoustic emission to emerging optical fiber sensing—covering their working principles, application status, and inherent limitations. Furthermore, it elaborates on the evolution from mechanism-based “white-box” models to data-driven “black-box” models, and further to “gray-box” intelligent diagnostic models that integrate expert knowledge. Industrial application cases of integrated monitoring and diagnostic systems are also introduced. Finally, the paper critically addresses the current severe challenges in data fusion, model generalization, real-time performance, and cost-effectiveness, while outlining future development trends toward digital twins, cross-modal fusion, edge intelligence, and self-evolving systems. The aim is to provide valuable references for technological innovation and engineering applications in this field.