Fabrication, Characterization, and Evaluation of Luffa cylindrica Fiber Reinforced Epoxy Composites

Additive manufacturing (AM) has developed and gained popularity across the globe into a multi-billion-dollar industry that involves many materials and techniques. AM has created itself as a technology for the manufacturing of metallic parts with enhanced mechanical characteristics that are scientifically sound and commercially feasible. However, there are various challenges, from business point of view, like high machine and material costs. Considering the complexities involved, sustainable manufacturing, optimization tools, and simulation models are necessary in order to save time and costly trial and errors. Topology optimization and simulation of AM processes are commercially available and are receiving attention from scientists and industry. Thus, this chapter is designed to provide readers with a brief introduction to AM technologies with typical applications. The main objective of this chapter is to provide the current trends and innovations in the field of design for additive manufacturing (DFAM), topology optimization, and simulation technologies.

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.