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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.

Concrete is a composite material that is commonly used in the construction industry. It will certainly be exposed to fires of varying intensities when used in buildings and industries. The major goal of this article was to look into the influence of mineral additions such as foundry sand and marble dust on the residual characteristics of concrete. To examine the behavior of residual characteristics of concrete after fire exposure, marble dust was substituted for cement and fine sand was substituted for foundry sand in varying amounts ranging from 0% to 20%. It aided in the better disposal of waste material so that it might be used as an addition. The purpose of the experiment was to see how increased temperatures affected residual properties of concrete, including flexural strength, compressive strength, tensile strength, static as well as dynamic elastic modulus, water absorption, mass loss, and ultrasonic pulse velocity. At temperatures of 200 °C, 400 °C, 600 °C, 800 °C, and 1000 °C, the typical fire exposure behavior of concrete was investigated. The effects of two cooling techniques, annealing and quenching, on the residual properties of concrete after exposure to high temperatures were investigated in this study. Replacement of up to 10% of the cement with marble dust and fine sand with foundry sand when concrete is exposed to temperatures up to 400 °C does not influence the behavior of concrete. At temperatures above 400 °C, however, the breakdown of concrete, which includes marble dust and foundry sand, causes a rapid deterioration in the residual properties of concrete, primarily for replacement of more than 10%.

The present study investigates the preference index of natural fibers such as sisal, coir, and rice straw fibers in stone matrix asphalt mixtures (SMA), using waste marble as filler. Waste marble was used as the filler in asphalt mixtures and was crushed by abrasion machine and sieved according to SMA filler requirements. The SEM topography and EDS analysis of sisal, coir, and rice straw fibers were also carried out. The Marshall test was conducted, which is the most acceptable, cost-effective, and widely adopted method to estimate the optimum bitumen and to examine several Marshall Measures, such as flow value, voids filled with bitumen (VFB), stability, voids in mineral aggregate (VMA), and air voids (VA). Furthermore, tests were performed on the specimen with the optimum amount of bitumen, different percentages of fibers, and waste marble as filler to calculate drain down, moister sensitivity, and Marshall Stability. Multi-criteria decision-making (MCDM) techniques were implemented to obtain subjective and objective weights, which were further used to compute the values of the preference index of natural fiber contents. The outcomes revealed favorable results for the usage of marble dust as filler in Stone Matrix Asphalt (SMA). In addition, the preference index upshots are inclined toward the usage of rice straw over coir followed by sisal fiber. It was observed that the value of the preference index in rice straw at 0.3 varied from 0.918, 0.925, and 0.931 in rice straw using equal, objective, and subjective weights, respectively. The maximum drain down value observed is 0.335 based on ASTM-D 6390 and IRC-SP-79 are against 0.3 percent natural fiber. Moreover, as per the prescribed limit of MoRTH, because of the thin film around aggregates, moisture susceptibility characteristics, i.e., better resistance to moisture, were enhanced by more than 80%.

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.

In this article, the comprehensive review on the application, and indeed, a comparative analysis on dry machining of different types of materials (Inconel, steel, aluminum, cast iron, magnesium and advanced materials) used in machining (turning, drilling and milling operations) were carried out in the light of utmost works published in the literature. The work describes the scientific findings of the past twenty years, including sustainable methods (surface texture, solid lubricants, vibration-assisted machining, laser-assisted machining), tool coatings, and geometry of tools. Vibration-assisted machining is another direction that researchers have investigated without the use of cutting coolants, where the complete disposal of coolants is not possible. Various researchers have carried out rigorous experimental work on milling, drilling, and turning operations under dry conditions to machine numerous materials. A significant proportion of experimental data about tool wear, tool wear machining, surface quality, surface integrity, etc, has been analyzed under dry conditions. However, the critical analysis of dry machining for different conventional machining operations for a variety of industrial materials is still lacking for establishing dry machining as a sustainable process for industrial applications. Thus, the critical analysis of various machining parameters and their consequences on tool wear and the surface quality of machined work was carried out in this work. Finally, scientific recommendations based on critical findings were proposed for industrial implementation of dry machining.

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.

The production management system in Industry 4.0 is emphasizes the improvement of productivity within limited constraints by sustainable production planning models. To accomplish this, several approaches are used which include lean manufacturing, kaizen, smart manufacturing, flexible manufacturing systems, cyber–physical systems, artificial intelligence, and the industrial Internet of Things in the present scenario. These approaches are used for operations management in industries, and specifically productivity maximization with cleaner shop floor environmental management, and issues such as worker safety and product quality. The present research aimed to develop a methodology for cleaner production management using lean and smart manufacturing in industry 4.0. The developed methodology would able to enhance productivity within restricted resources in the production system. The developed methodology was validated by production enhancement achieved in two case study investigations within the automobile manufacturing industry and a mining machinery assembly unit. The results reveal that the developed methodology could provide a sustainable production system and problem-solving that are key to controlling production shop floor management in the context of industry 4.0. It is also capable of enhancing the productivity level within limited constraints. The novelty of the present research lies in the fact that this type of methodology, which has been developed for the first time, helps the industry individual to enhance production in Industry 4.0 within confined assets by the elimination of several problems encountered in shop floor management. Therefore, the authors of the present study strongly believe that the developed methodology would be beneficial for industry individuals to enhance shop floor management within constraints in industry 4.0.

In polymer composites, synthetic fibers are primarily used as a chief reinforcing material, with a wide range of applications, and are therefore essential to study. In the present work, we carried out the erosive wear of natural and synthetic fiber-based polymer composites. Glass fiber with jute and Grewia optiva fiber was reinforced in three different polymer resins: epoxy, vinyl ester and polyester. The hand lay-up method was used for the fabrication of composites. L16 orthogonal array of Taguchi method used to identify the most significant parameters (impact velocity, fiber content, and impingement angle) in the analysis of erosive wear. ANOVA analysis revealed that the most influential parameter was in the erosive wear analysis was impact velocity followed by fiber content and impingement angle. It was also observed that polyester-based composites exhibited the highest erosive wear followed by vinyl ester-based composites, and epoxy-based composites showed the lowest erosive wear. From the present study, it may be attributed that the low hardness of the polyester resulting in low resistance against the impact of erodent particles. The SEM analysis furthermore illustrates the mechanism took place during the wear examination of all three types of composites at highest fiber loading. A thorough assessment uncovers brittle fractures in certain regions, implying that a marginal amount of impact forces was also acting on the fabricated samples. The developed fiber-reinforced polymer sandwich composite materials possess excellent biocompatibility, desirable promising properties for prosthetic, orthopaedic, and bone-fracture implant uses.

In this investigation, biodegradable composites were fabricated with polycaprolactone (PCL) matrix reinforced with pine cone powder (15%, 30%, and 45% by weight) and compatibilized with graphite powder (0%, 5%, 10%, and 15% by weight) in polycaprolactone matrix by compression molding technique. The samples were prepared as per ASTM standard and tested for dimensional stability, biodegradability, and fracture energy with scanning electron micrographs. Water-absorption and thickness-swelling were performed to examine the dimensional stability and tests were performed at 23 °C and 50% humidity. Results revealed that the composites with 15 wt % of pine cone powder (PCP) have shown higher dimensional stability as compared to other composites. Bio-composites containing 15–45 wt % of PCP with low graphite content have shown higher disintegration rate than neat PCL. Fracture energy for crack initiation in bio-composites was increased by 68% with 30% PCP. Scanning electron microscopy (SEM) of the composites have shown evenly-distributed PCP particles throughout PCL-matrix at significantly high-degrees or quantities of reinforcing.