Self-Pierce Riveting: Development and Assessment for Joining Polymer-Metal Hybrid Structures in Lightweight Automotive Applications.

In recent years, the transportation industry has faced the challenge of cutting costs, meeting increasingly stringent environmental regulations, and significantly increasing transportation volumes. One approach to meeting these challenges is to develop new, improved transportation vehicles using new materials and innovative joining techniques. Multi-material structures are becoming an alternative to body parts. Self-pierce riveting technology plays a crucial role in this process, and hybrid structures depend exclusively on it. In this article, recent advances in self-pierce riveting technology are analyzed to meet today's challenges and future multi-material applications.

Sustainable Bioplastics for Food Packaging Produced from Renewable Natural Sources.

It is crucial to find an effective, environmentally acceptable solution, such as bioplastics or biodegradable plastics, to the world's rising plastics demand and the resulting ecological destruction. This study has focused on the environmentally friendly production of bioplastic samples derived from corn starch, rice starch, and tapioca starch, with various calcium carbonate filler concentrations as binders. Two different plasticizers, glycerol and sorbitol, were employed singly and in a rich blend. To test the differences in the physical and chemical properties (water content, absorption of moisture, water solubility, dissolution rate in alcohol, biodegradation in soil, tensile strength, elastic modulus, and FT-IR) of the produced samples, nine samples from each of the three types of bioplastics were produced using various ratios and blends of the fillers and plasticizers. The produced bioplastic samples have a multitude of features that make them appropriate for a variety of applications. The test results show that the starch-based bioplastics that have been suggested would be a better alternative material to be used in the packaging sectors.

Multiobjective Optimization of Chemically Assisted Magnetic Abrasive Finishing (MAF) on Inconel 625 Tubes Using Genetic Algorithm: Modeling and Microstructural Analysis.

The demand for the surface integrity of complex structures is drastically increasing in the field of aerospace, marine and automotive industry. Therefore, Inconel alloy, due to its superior attributes, has a wide scope for the improvement in surface integrity. To achieve the precise surface finish and enhance the process performance, process optimization is necessary. In current paper, chemically assisted MAF process parameters were optimized using the genetic algorithm (GA) approach during finishing of Inconel 625 tubes. Regression models were developed for improvement in internal surface finish (PIISF), improvement in external surface finish (PIESF), and material removal (MR) using Design expert software. Then, the surface microstructure of Inconel 625 tubes was analyzed using scanning electron microscopy (SEM). ANOVA analysis predicts that processing time and abrasive size have the highest percentage contribution in improving the surface finish and material removal. Multioptimization results suggested to set the level of processing time (A) at 75 min, surface rotational speed (B) at 60 RPM, weight % of abrasives (C) at 30%, chemical concentration (D) at 500 gm/lt and abrasive size (E) at 40 microns to obtain optimal parameters for PIISF, PIESF and MR responses.

In Situ Micro-Observation of Surface Roughness and Fracture Mechanism in Metal Microforming of Thin Copper Sheets with Newly Developed Compact Testing Apparatus.

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 Ra 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.

Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms.

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.