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Breakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as it considers only one process parameter, i.e., thermocouple temperature. The system also generates false alarms. Several other process parameters must also be considered to predict breakout accurately. This work has considered multiple process parameters (casting speed, mold level, thermocouple temperature, and taper/mold) and developed a breakout prediction system (BOPS) for continuous casting of steel slabs. The BOPS is modeled using an artificial neural network with a backpropagation algorithm, which further has been validated by using the Keras format and TensorFlow-based machine learning platforms. This work used the Adam optimizer and binary cross-entropy loss function to predict the liquid breakout in the caster and avoid operator intervention. The experimental results show that the developed model has 100% accuracy for generating an alarm during the actual breakout and thus, completely reduces the false alarm. Apart from the simulation-based validation findings, the investigators have also carried out the field application-based validation test results. This validation further unveiled that this breakout prediction method has a detection ratio of 100%, the frequency of false alarms is 0.113%, and a prediction accuracy ratio of 100%, which was found to be more effective than the existing system used in continuous casting of steel slab. Hence, this methodology enhanced the productivity and quality of the steel slabs and reduced substantial capital loss during the continuous casting of steel slabs. As a result, the presented hybrid algorithm of artificial neural network with backpropagation in breakout prediction does seem to be a more viable, efficient, and cost-effective method, which could also be utilized in the more advanced automated steel-manufacturing plants.

Cutting tool characterization plays a crucial role in understanding the behavior of machining operations. The selection of a suitable cutting material, the operating conditions for the work piece, is necessary to yield good cutting-tool life. Several pieces of research have been carried out in cutting-tool characteristics for turning operation. Only a few pieces of research have focused on correlating the vibrations and stress with wear characteristics. This research article deals with stress induced in silicon carbide tool inserts and coated tool inserts while machining SS304 steel. Since this material is much less resistant to corrosion and oxidation it is widely used in engineering applications such as cryogenics, the food industry and liquid contact surfaces. Moreover, these materials have much lower magnetic permeability so they are used as nonmagnetic engineering components which are very hard. This article focuses on the machining of SS304 by carbide tool inserts and then, the cutting forces were observed with a tool dynamometer. Using observed cutting forces, the induced stress in the lathe tool insert was determined by FEA investigation. This research also formulates an idea to predict the tool wear due to vibration. Apparently, the worn-out tool vibrates more than new tools. Using the results, the relation between stress, strain and feed rate, depth of cut and speed was found and mathematically modeled using MINI TAB. It was observed that carbide tool inserts with coating withstand better than uncoated tools while machining SS304. The results were anticipated and correlation between the machining parameters furnished the prediction of tool life and obtaining the best machining outcomes by using coated tool inserts.

The goal of this study was to determine the coefficient of permeability as well as the rate of carbonation of concrete constructed with rice husk ash (RHA) as a partial replacement for cement (i.e., 5%, 10%, and 15%) and two different concentrations of soap solutions (i.e., 1 percent and 2 percent). The microstructural studies of RHA, and carbonated samples have been conducted by using Scanning Electron Microscope (SEM) and X-Ray Diffraction (XRD) analysis. According to this study, the carbonation depth of concrete made with 1% and 2% soap solution concentration and without rice husk ash decreased by 11.89% and 46.55%, respectively. From the results, it may also be observed that the carbonation depth of concrete made with up to 10% replacement of cement by rice husk ash led to maximum carbonation resistance, while more than 10% replacement of cement showed higher carbonation depth. It is also observed that the coefficient of permeability of concrete with 2% soap solution significantly decreased as compared to the 1% soap solution and control mix. It may be observed from the SEM images that 0% soap solution (M1) concrete has a very rough concrete surface which may indicate more voids. However, 2% soap solution concrete has a much smoother surface, which indicates a smaller number of voids. Furthermore, the SEM images showed that the soap solution helps in filling the voids of concrete which ultimately helps in reduction in permeability. Energy Dispersive X-Ray Analysis (EDX) of concrete with 0% (M1) and 2% (M6) soap solution disclosed that the concrete with 2% soap solution (M6) exhibited more silica element formation than the concrete with no soap solution (M1).

The advancement of 3D-printing technology has ushered in a new era in the production of machine components, building materials, prototypes, and so on. In 3D-printing techniques, the infill reduces the amount of material used, thereby reducing the printing time and sustaining the aesthetics of the products. Infill patterns play a significant role in the property of the material. In this research, the mechanical properties of specimens are investigated for gyroid, rhombile, circular, truncated octahedron, and honeycomb infill structures (hexagonal). Additionally, the tensile properties of PLA 3D-printed objects concerning their infill pattern are demonstrated. The specimens were prepared with various infill patterns to determine the tensile properties. The fracture of the specimen was simulated and the maximum yield strengths for different infill structures and infill densities were determined. The results show the hexagonal pattern of infill holds remarkable mechanical properties compared with the other infill structures. Through the variation of infill density, the desired tensile strength of PLA can be obtained based on the applications and the optimal weight of the printed parts.