ABSTRACT
The process of laser welding of sheets of HSLA (high-strength low-alloy steel), DP600 (dual-phase steel) and TRIP steels was investigated. A weld was successfully made in a double-sided hot-dip galvanized sheet with a thickness of 0.78-0.81 mm using a laser power of 2 kW per pass without any pretreatment of the weld zone. Microstructure studies revealed the presence of martensitic and ferritic phases in the weld zone, which could be associated with a high rate of its cooling. This made it possible to obtain good strength of the weld, while maintaining sufficient ductility. A relationship between the microstructural features and mechanical properties of welds made in the investigated steels has been established. The highest hardness was found in the alloying region of steels due to the formation of martensite. The hardness test results showed a very narrow soft zone in the heat affected zone (HAZ) adjacent to the weld interface, which does not affect the tensile strength of the weld. The ultimate tensile strength of welds for HSLA steel was 340-450 MPa, for DP600 steel: 580-670 MPa, for TRIP steel: ~700 MPa, respectively, exceeding the strength of base steels.
ABSTRACT
Nowadays, vehicles have advanced driver-assistance systems which help to improve vehicle safety and save the lives of drivers, passengers and pedestrians. Identification of the road-surface type and condition in real time using a video image sensor, can increase the effectiveness of such systems significantly, especially when adapting it for braking and stability-related solutions. This paper contributes to the development of the new efficient engineering solution aimed at improving vehicle dynamics control via the anti-lock braking system (ABS) by estimating friction coefficient using video data. The experimental research on three different road surface types in dry and wet conditions has been carried out and braking performance was established with a car mathematical model (MM). Testing of a deep neural networks (DNN)-based road-surface and conditions classification algorithm revealed that this is the most promising approach for this task. The research has shown that the proposed solution increases the performance of ABS with a rule-based control strategy.
ABSTRACT
The research of fractographic images of metals is an important method that allows obtaining valuable information about the physical and mechanical properties of a metallic specimen, determining the causes of its fracture, and developing models for optimizing its properties. One of the main lines of research in this case is studying the characteristics of the dimples of viscous detachment, which are formed on the metal surface in the process of its fracture. This paper proposes a method for detecting dimples of viscous detachment on a fractographic image, which is based on using a convolutional neural network. Compared to classical image processing algorithms, the use of the neural network significantly reduces the number of parameters to be adjusted manually. In addition, when being trained, the neural network can reveal a lot more characteristic features that affect the quality of recognition in a positive way. This makes the method more versatile and accurate. We investigated 17 models of convolutional neural networks with different structures and selected the optimal variant in terms of accuracy and speed. The proposed neural network classifies image pixels into two categories: "dimple" and "edge". A transition from a probabilistic result at the output of the neural network to an unambiguously clear classification is proposed. The results obtained using the neural network were compared to the results obtained using a previously developed algorithm based on a set of filters. It has been found that the results are very similar (more than 90% similarity), but the neural network reveals the necessary features more accurately than the previous method.
