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.

A better understanding of material deformation behaviours with changes in size is crucial to the design and operation of metal microforming processes. In order to facilitate the investigation of size effects, material deformation behaviours needed to be determined directly from material characterizations. This study was aimed at the design and manufacture of a compact universal testing machine (UTM) compatible with a 3D laser-confocal microscope to observe the deformation behaviour of materials in real-time. In this study, uniaxial micro tensile testing was conducted on three different thin (0.05 mm, 0.1 mm, and 0.3 mm) copper specimens with characteristic dimensions at micro scales. Micro tensile experimental runs were carried out on copper specimens with varying grain sizes on the newly developed apparatus under a 3D laser-confocal microscope. Microscale experiments under 3D laser-confocal microscope provided not only a method to observe the microstructure of materials, but also a novel way to observe the early stages of fracture mechanisms. From real-time examination using the newly developed compact testing apparatus, we discovered that fracture behaviour was mostly brought about by the concave surface formed by free surface roughening. Findings with high stability were discovered while moving with the sample grasped along the drive screw in the graphical plot of a crosshead’s displacement against time. Our results also showed very low mechanical noise (detected during the displacement of the crosshead), which indicated that there were no additional effects on the machine, such as vibrations or shifts in speed that could influence performance. The engineering stress-strain plots of the pure copper-tests with various thicknesses or samples depicted a level of stress necessary to initiate plastic flowing inside the material. From these results, we observed that strength and ductility declined with decreasing thickness. The influence of thickness on fracture-strain, observed during tensile testing, made it clear that the elongation-at-break of the pure-copper foils intensely decreased with decreases in thickness. The relative average surface-roughness R_a was evaluated, which showed us that the surface-roughness escalated with the increasing trend of plasticity deformation (plastic strain) ε. For better understanding of the effects of plastic strain on surface roughness prior to material fractures, micro tensile tests were performed on the newly developed machine under a 3D laser-confocal-microscope. We observed that homogeneous surface roughness was caused by plastic strain, which further formed the concave surface that led to the fracture points. Finally, we concluded that surface roughness was one of the crucial factors influencing the fracture behaviour of metallic sheet-strips in metal microforming. We found that this type of testing apparatus could be designed and manufactured within a manageable budget.

Pure TiO₂ nanoparticles (TiO₂NPs) were produced via the sol–gel method and then coated with silver nanoparticles (AgNPs) to reduce their optical band gap. The concurrent synthesis and immobilization of AgNPs over TiO₂NPs was achieved through the interaction of an open-air argon plasma jet with a solution of silver nitrate/stabilizer/TiO₂NPs. The one-pot plasma synthesis and coating of AgNPs over TiO₂NPs is a more straightforward and environmentally friendly method than others. The plasma-produced Ag/TiO₂ nanocomposites were characterized and tested for their photocatalytic potential by degrading different concentrations of methyl blue (MB) in water. The dye concentration, oxidant dose, catalyst dose, and reaction time were also optimized for MB degradation. XRD results revealed the formation of pure AgNPs, pure TiO₂NPs, and Ag/TiO₂ nanocomposites with an average grain size of 12.36 nm, 18.09 nm, and 15.66 nm, respectively. The immobilization of AgNPs over TiO₂NPs was also checked by producing SEM and TEM images. The band gap of AgNPs, TiO₂NPs, and Ag/TiO₂ nanoparticles was measured about 2.58 eV, 3.36 eV, and 2.86 eV, respectively. The ultraviolet (UV) results of the nanocomposites were supportive of the degradation of synthetic dyes in the visible light spectrum. The AgNPs in the composite not only lowered the band gap but also obstructed the electron–hole recombinations. The Ag/TiO₂ composite catalyst showed 90.9% degradation efficiency with a 5 ppm dye concentration after 120 min of light exposure.