Improving Quality in the Process of Hot Rolling of Steel Sheets

Microstructure and Hardness Characteristics of Swing-Arc SAW Hardfacing Layers

Hot-rolled backup rolls are widely used in steel rolling and usually need to be repaired by arc hardfacing after becoming worn. However, a corrugated-groove defect commonly occurs on the roll surface due to the uneven hardness distribution in the hardfacing layers, affecting the proper usage of the roll. Accordingly, a new swing-arc submerged arc welding (SA-SAW) process is proposed to attempt to solve this drawback. The microstructure and hardness are then investigated experimentally for both SAW and SA-SAW hardfacing layers. It is revealed that a self-tempering effect occurs in the welding pass bottom and the welding pass side neighboring the former pass for both processes, refining the grain in the two areas. In all the zones, including the self-tempering zone (STZ), heat-affected zone (HAZ), and not-heat-affected zone in the welding pass, both SAW and SA-SAW passes crystallize in a type of columnar grain, where the grains are the finest in STZ and the coarsest in HAZ. In addition, the arc swing improves the microstructure homogeneity of the hardfacing layers by obviously lowering the tempering degree in HAZ while promoting the even distribution of the arc heat. Accordingly, the hardness of the SA-SAW bead overall increases and distributes more uniformly with a maximum difference of < 80 HV0.5 along the horizontal direction of the bead. This hardness difference in SA-SAW is accordingly decreased by ~38.5% compared to that of the SAW bead, further indicating the practicability of the new process.

The Relationship between Process Capability and Quality of Measurement System

This article deals with the design of appropriate measures that had to be taken for the implemented measurement system. The measurement result was significantly negatively affected by several factors. Forty-five samples of shafts used in the production of surgical drills were measured. Measurements were performed by metrological appraisers with a calibrated digital micrometer. Measurement and subsequent data processing revealed low process capability (CP and PP indices). A large portion of the shafts had an observed size below the lower specific limit (LSL). Therefore, it was necessary to take corrective action. This paper focuses on the corrective measures implemented in the measurement system. The micrometer met the requirements of the standard and was metrologically capable. The shafts were measured by eight metrological appraisers, so attention was focused on the potential impact of the metrological appraiser. The measured data were evaluated by uncertainty analysis, paired t-tests, measurement systems analysis (MSA) and Cohen’s kappa. The number of non-compliant shafts was shown to increase with decreasing measurement capability. The measurement system was evaluated as conditionally capable, even incapable. One possibility was to identify the optimal pair of metrological appraisers. The pair of metrological appraisers E and F appeared to be the most suitable combination for most methods. Due to the relatively high %EV index, the second option was to improve the work with the measuring instrument, that is, improve the training and supervision of metrological appraisers in the measurement process. Repeated measurements by the pair with the highest capability (metrological appraisers E and F) resulted in an increase in the value of the capability indices and a decrease in the number of shafts out of tolerance for the same shafts. As the value of these indices was lower than 1.33 during repeated measurements, corrective measures had to be taken, not only in the measurement system, but also in the production system.

Research of U-Net-Based CNN Architectures for Metal Surface Defect Detection

The quality, wear and safety of metal structures can be controlled effectively, provided that surface defects, which occur on metal structures, are detected at the right time. Over the past 10 years, researchers have proposed a number of neural network architectures that have shown high efficiency in various areas, including image classification, segmentation and recognition. However, choosing the best architecture for this particular task is often problematic. In order to compare various techniques for detecting defects such as “scratch abrasion”, we created and investigated U-Net-like architectures with encoders such as ResNet, SEResNet, SEResNeXt, DenseNet, InceptionV3, Inception-ResNetV2, MobileNet and EfficientNet. The relationship between training validation metrics and final segmentation test metrics was investigated. The correlation between the loss function, the , , , and validation metrics and test metrics was calculated. Recognition accuracy was analyzed as affected by the optimizer during neural network training. In the context of this problem, neural networks trained using the stochastic gradient descent optimizer with Nesterov momentum were found to have the best generalizing properties. To select the best model during its training on the basis of the validation metrics, the main test metrics of recognition quality (Dice similarity coefficient) were analyzed depending on the validation metrics. The ResNet and DenseNet models were found to achieve the best generalizing properties for our task. The highest recognition accuracy was attained using the U-Net model with a ResNet152 backbone. The results obtained on the test dataset were and .

Analysis of Fault Conditions in the Production of Prestressed Concrete Sleepers

Industries demand that their products are high quality with the least number of defects due to the rapid improvement in manufacturing technology. Quality is a critical criterion for evaluation in manufacturing firms. The production of a final product that can meet customer requirements is essential in a sustainable supply chain system to reduce costs, increase productivity and provide high-quality products. The aim of the study is to identify the root cause of defects emerging in the production process of prestressed railway concrete sleepers. Ishikawa diagram and Pareto analysis were used to identify the root cause. The results showed that the cause of the faulty concrete sleeper is the breaking of the bolts, which are supplied by the external provider. Since the supplier refused to accept the complaint, chemical analysis and measuring of hardness and microhardness of bolts were realized. It showed that the hardness of the bolts does not achieve the values, which should be achieved after the declared heat-treatment. As a corrective action, the input control of bolts hardness was proposed as well as establishing the team cooperating with the supplier to improve the heat treatment process, which will be the objective of further study.

Assessment of Technical and Ecological Parameters of a Diesel Engine in the Application of New Samples of Biofuels

The technical and environmental parameters of the diesel internal combustion engine using two new samples of biofuels SAMPLE 1 and SAMPLE 2 were evaluated in this paper. SAMPLE 1 and SAMPLE 2 biofuels were tested on a LOMBARDINI LDW 502 internal combustion engine, which was loaded on a dynamometer according to the applicable national and international standards. This method can also be applied to marine engines and contribute to a higher level of marine ecology. The result of the testing was to determine the impact of tested biofuels on the technical parameters engine power and torque and the environmental parameters emissions of smoke, nitrogen oxides, and economy of the internal combustion engine-specific fuel consumption. From the measured data, another parameter was calculated, such as the injected fuel dose and the overall efficiency of the internal combustion engine. The results show that the new samples of SAMPLE 1 and SAMPLE 2 biofuels tested could be a suitable alternative to standard diesel.

Application of FTA Analysis for Calculation of the Probability of the Failure of the Pressure Leaching Process

European Union legislation requires organizations to assess their processes in the context of risk management. The main task of risk management is to manage all risks that can significantly affect the outcome of processes. The article is focused on risk evaluation in pressure leaching at elevated temperature using the method Fault Tree Analysis (FTA). The effectivity of pyrite and arsenic pyrite decomposition by oxidative pressure leaching is influenced by the duration of the process, by the temperature, concentration of the leaching solution and by a density of the slurry. It was found that, under equitable conditions, the arsenic pyrite decomposes more intensely than pyrite. Under laboratory conditions, leaching is performed in an autoclave. Due to the aggressive environment, increased pressure and temperature, process failure is possible. Its probability was calculated by FTA. It has been found that the probability of the top event in the examined process was disproportionately high (0.057) and represents an invitation to take corrective actions. The Monte Carlo method was used for the simulation of the effect the probability of basic events on the probability of the top event—the failure of the leaching process.