Research of the structure and properties of nanostructured coatings based on chrome

Nanostructured chromium-based coatings have been researched. In some cases, it is expedient to use vacuum methods of deposition of chromium coatings by methods of thermal evaporation of pure chromium from tungsten coils or by ion-plasma (magnetron) sputtering method. Due to the low deposition temperature of coatings, there is a possibility of their formation on metallic and non-metallic materials. Based on the above, it is necessary to note the relevance of the study of the technology for the formation of chromium-based coatings by the ion-plasma method. To apply wear-resistant chromium coatings to samples made of R6M5 steel, the method of ion-plasma (magnetron) sputtering with a preliminary treatment of the surface with an ion source was used. The thickness, adhesive strength and corrosion resistance of chromium-based coatings were determined. Electron microscopy methods have been used to study the morphology of the surface and the size of nanoparticles in the structure of chrome coatings. It was revealed that depending on the modes of formation, the coating consists of nanoparticles with sizes from 15 nm to 230 nm

Bispectrum analysis based on dual channel homologous information fusion and its application in fault diagnosis

High order spectrum is a powerful tool for processing the nonlinear and non-Gaussian signals of rotating machinery. As one typical representative of high order spectrum, the bispectrum analysis method has been used widely due to its advantages of low order and effective algorithm. However, traditional bispectrum analysis method based on single channel information often results in inconsistent fault feature extraction results while analyzing the phase coupling of complex vibration signals collected from two different measurement directions at the same measurement point, which will have great negative impact on subsequent rotor dynamic balancing experiment requiring phase information or fault diagnosis. The full vector spectrum analysis method based on dual channel homologous information fusion is an improved method of original two classical homologous information fusion methods (holographic spectrum and full spectrum), which could extract the dual channel fusion features while preserving the original information effectively. To overcome the shortcomings of traditional bispectrum and take advantages of full vector spectrum, a novel bispectrum analysis method based on full vector spectrum analysis is proposed. The proposed method could integrate the dual channel signal information effectively to display the secondary phase coupling comprehensively and accurately, fully reflect the nonlinear feature information contained in the signal, and provide accurate and reliable basis for feature extraction and fault diagnosis in the next step, whose effectiveness and advantage are verified through simulation and experiment

Research of the influence of the technological parameters of the steelmaking process on the design and defect formation of large-sized castings for freight cars

The research conducted at the “Foundry and Mechanical Plant” JSC is devoted to the analysis of the influence of the temperature regime of pouring 20GL steel into molds on the formation of defects. It was experimentally established that the optimal range of pouring temperatures allows avoiding such defects as hot cracks and underfills. Data were obtained on the time of filling molds, the distribution of defects and the preservation of the metal structure at different temperatures. It was proven that compliance with the temperature range of 1580-1590 °C guarantees high quality castings. Based on the results of the study, technological parameters were proposed that ensure minimal formation of defects and uniform formation of the grain structure

Mechanical properties of nano-SiO2 and fly ash composite modified SAP internal curing concrete

In practical applications, Super absorbent polymers (SAP) can have certain adverse effects on concrete. Incorporation of SAP could lead to changes in the pore structure, thereby affecting strength. At the same time, SAP could also present cost challenges. To address these issues, a specific modifier can be incorporated into the SAP internally curing concrete. In this paper, the mechanical properties of nano-SiO2 and fly ash composite modified SAP internal curing concrete were investigated. The mechanical properties of the composite modified SAP were reflected through compressive strength and tensile strength tests as well as porosity mutual verification. The results indicate that nano-SiO2 and fly ash increase the strength of the modified SAP internal curing concrete and reduce the porosity. When the maximum content of nano-SiO2 reaches 3 %, the modification effect is more favourable. Under the combined effect of nano-SiO2 and fly ash, both the strength and porosity of SAP internal curing concrete are improved

Enhancing PLL performance in weak grids: a comparative analysis of backward and bilinear ADC with SOGI-PLL

Most rectifiers using AC grid voltage assume that the voltage is ideal and has no distortion. However, in high-power systems such as water electrolysis, the grid voltage can be distorted. This situation is called a weak grid. In weak grids, the switching of rectifiers causes voltage distortion. Distorted voltage causes phase errors during observation, so it is important to measure voltage without distortion. There are two common methods to reduce errors during observation. One is using a hardware Low-Pass Filter (LPF) to reduce high-frequency switching distortion. The other is using a Second-Order Generalized Integrator (SOGI) Phase-Locked Loop (PLL) to separate the distorted component. Both methods are commonly used, but their performance changes depending on how they are applied. This paper compares the distortion reduction of the hardware LPF and the error caused by the digital method of the SOGI-PLL. Simulation results show that the hardware LPF reduces distortion by about 75 %, and the SOGI-PLL can have up to 6.7 % error depending on the digital method. These results are verified through PSIM simulation

Resistance to penetration of a bulldozer blade when the machine is stationary

The study of the resistance of the bulldozer blade burial at a stationary machine is aimed at studying the force factors acting on the working tool during its penetration into the ground. When the machine is not moving, the blade deepening causes an increase in passive resistance from the ground, which acts on the front and rear edges of the blade. This resistance increases with the depth of burial, which requires an increase in force for further advancement of the working body into the ground. An important feature is that in the initial stages of blade insertion, the resistance at the front face increases significantly, increasing the forces. At the rear edge, the re-sistance also increases as the blade deepens. Cutting angles have a significant effect on resistance changes: at higher cutting angles, the resistance decreases, reducing the load on the working mechanisms. The results of the study emphasize that for efficient knife deepening with the machine stationary, higher cutting angles should be used, which reduces passive resistance, improving productivity and reducing energy costs

Visual large language models for welding assessment

This paper evaluates the effectiveness of visual large language models (LLMs) for weld defect identification, focusing on their potential utility for novice welders. Using the Gemma-3B, Gemma-27B and Qwen2.5-VL-32B models, we benchmark performance against a standardized weld defect dataset and compare against a most modern version of the more traditional YOLO architecture, YOLOv12. Results show the 27B model achieves 66.36 % recall and a lower precision of 46.10 %, while the 3B model demonstrates poor reliability at 35.05 % recall, comparable to the results of the YOLOv12. Meanwhile, Qwen2.5-VL-32B does not produce sufficiently reliable results to gauge them automatically. We conclude that large LLMs can achieve quantitatively superior results on difficult datasets by leveraging innate understanding of welding stemming from their massive pre-training data, allowing improved functionality compared to current state of the art object detectors, and would appear to be beneficial when used in aid of novice welders in training

Multi-scale optimization design of viscoelastic damping sandwich plate

Aiming at the optimization design problem of viscoelastic damping sandwich plate under the background of lightweight, a multi-scale optimization design method is proposed to realize the collaborative optimization design of viscoelastic damping sandwich plate in both macro and micro scales. It is assumed that the viscoelastic damping material is microscopically composed of 3D periodic unit cells. The effective constitutive matrix of the unit cell is derived using the homogenization method. The topology optimization model is established, and sensitivity analysis is conducted. The iteration of the design variables is realized by the Optimization Criterion (OC) method. Several numerical examples are presented to demonstrate the effectiveness of the proposed multi-scale optimization method. In-depth discussions are given for the impact of optimization objectives and volume fractions on the design results. The results show that the optimized configuration on both scales are dependent on the objective mode. The macroscopic optimal layout is affected by the microscopic volume fraction, while the change in macroscopic volume fraction has little effect on the microscopic optimal configuration. The dynamic characteristics of the viscoelastic damping sandwich plate are improved significantly through multi-scale optimization design

Analysis of the influence of heat transfer mechanisms on the curing process of thick-walled UAV parts made of polymer composite materials based on epoxy binder

The paper deals with the main technologies and mechanisms used in the curing process of thick-walled parts of unmanned aerial vehicles (UAV) made of polymer composite materials (PCM) based on epoxy binder. The technological features and characteristics of the curing process are analyzed, and the influence of heat transfer mechanisms on the process kinetics and the final quality of molded parts is investigated. Special attention is paid to the problem of low thermal conductivity of PCM, which leads to temperature inhomogeneity during curing. Various curing methods including convection, infrared (IR) heating and pressing are considered and compared. The advantages of IR heating are revealed, which consist in acceleration of the process and increase of mechanical characteristics of composites. Special emphasis is placed on the statistical validation of the mechanical performance improvements associated with infrared curing. A comparative study of three curing methods – pressing, convection, and infrared (IR) – is conducted with a discussion on energy efficiency, mechanical strength gain, and curing time. The results indicate that IR curing improves tensile strength by 15-20 % over conventional methods, with statistical confidence. The paper offers practical recommendations to ensure uniform heat distribution and optimal curing conditions

A rolling bearing fault diagnosis method under insufficient samples condition based on MSLSTM transfer learning

It usually affects the accuracy and reliability of deep learning based intelligent diagnosis methods under the condition of insufficient samples. Existing methods for handling insufficient samples often have problems such as requiring rich expert experience or consuming a lot of time. To solve the above problems, a rolling bearing fault diagnosis method under insufficient samples condition based on multi-scale long-term and short-term memory network (MSLSTM) transfer learning is proposed, which mainly consists of an improved long-term and short-term memory network named as MSLSTM and transfer learning. By introducing multi-scale convolution operation into the traditional LSTM to improve its drawback that only extracts single type of fault feature information, which leads to poor diagnostic performance in noisy environments. Besides, the pooling layer and global average pooling layer in traditional LSTM are replaced with convolution operation to avoid the problem of information loss. Subsequently, the MSLSTM is combined with transfer learning, and a rolling bearing fault diagnosis method under insufficient samples condition based on MSLSTM transfer learning is proposed, which fine tunes the model parameters using a small amount of target domain data. Feasibility of the proposed method is verified through two kinds of experiments. The proposed method has stronger feature extraction ability and training efficiency compared with other models