Gamification and Machine Learning Inspired Approach for Classroom Engagement and Learning

Technology has enhanced the scope and span of the teaching and learning process but somehow it could not enhance the self-motivation and engagement among the students to the same scale. The lack of self-motivation and intermittent engagement is one of the prime challenges faced by educators today. Perplexing tasks for the faculty are to embroil students during the lecture. This work paves new ways to scale up the enticement using artificial intelligence and machine learning. The intelligent framework proposed here is built on yet another novel methodology used globally for user engagement and is termed gamification. The primary objective of the present research work is to negate the issue of disengagement by designing and implementing a gamified framework on 120 students from higher education that will include student engagement, enticement, and motivation. Generally, mechanisms are designed for specific courses, whereas the gamified system proposed is an open-ended method irrespective of course and the program being studied, and this framework has endeavored on multiple courses. To enhance the utility of the gamified framework, ANFIS model is utilized for smart decision-making concerning rewards distribution that is directly proportional to the number of coins gained by the students. As an outcome, better participation of a group of students under the proposed intelligent gamified system is reported as compared to the control group thus endorsing the success of the model

Three-dimensional transient heat transfer analysis of micro-plasma arc welding process using volumetric heat source models

The micro plasma arc welding process is associated with different physical phenomena simultaneously. This results in complexities to comprehend the actual mechanism involved during the process. Therefore, a robust numerical model that can compute the weld pool shape, temperature distribution, and thermal history needs to be addressed. Unlike, other arc welding processes, the micro plasma arc welding process utilizes thin sheets of thickness between 0.5 and 2 mm. However, joining thin sheets using a high-density arc welding process quickens the welding defects such as burn-through, thermal stresses, and welding-induced distortions. The incorporation of a surface heat source model for computational modeling of the high energy density welding process impedes heat transfer analysis. In that respect, researchers have developed numerous volumetric heat source models to examine the welding process holistically. Although, selecting volumetric heat source models for miniature welding is a significant task. The present work emphasis developing a rigorous yet efficient model to evaluate weld pool shape, temperature distribution, and thermal history of plasma arc welded Ti6Al4V sheets. The computational modeling is performed using a commercially available COMSOL Multiphysics 5.4 package with a finite element approach. Two different prominent thermal models, namely, Parabolic Gaussian and Conical power energy distribution models are used. A comparative analysis is carried out to determine the most suitable heat source model for evaluating temperature distribution, peak temperature, and thermal history. The analysis is done by juxtaposing the simulated half-cross-section weld macrographs with the published experimental results from independent literature. The numerical results showed that the proximity of top bead width magnitude was obtained using the Parabolic Gaussian heat source model for low heat input magnitude of 47.52 and high heat input magnitude of 65.47 J·mm−1, respectively. In terms of percentage error, the maximum top bead width percentage error for the Parabolic heat source model is 13.26%. However, the maximum top bead width percentage error for the Conical heat source model is 18.36%. Likewise, the maximum bottom bead width percentage error for the Parabolic heat source model and the Conical heat source model is 12.3 and 25.8%, respectively. Overall, it was observed that the Parabolic heat source model produces the least deviating outcomes when compared with the Conical distribution. It was assessed that the Parabolic Gaussian heat source model can be a viable heat source model for numerically evaluating micro-plasma arc welded Ti6Al4V alloy of thin sheets

Effect of Al2O3 Nanoparticles on Performance and Emission Characteristics of Diesel Engine Fuelled with Diesel–Neem Biodiesel Blends

Indagation in the sphere of nanoparticle utilisation has provided commendatory upshots in discrete areas of application varying from medicinal use to environmental degradation alleviation. This study incorporates alumina nanoparticles as additives to diesel and biodiesel blends. The prime objective of the present study was the scrutinisation of the denouement of Al2O3 nanoparticle incorporation in diesel–biodiesel blends on a diesel engine’s performance and emission characteristics. Test fuel samples were prepared by blending different proportions of biodiesel and dispersing two concentrations of alumina nanoparticles (25 and 50 ppm) in the diesel. Dispersion was made without the use of a nanoparticle stabiliser to meet real-world feasibility. High-speed shearing was employed to blend the biodiesel and diesel, while nanoparticles were dispersed in the blends by ultrasonication. The blends so devised were tested using a single-cylinder diesel engine at fixed RPM and applied load for three compression ratios. Upshots of brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE) for fuel samples were measured with LabView-based software, whereas CO emissions and unburnt hydrocarbon (UBHC) emissions were computed using an external gas analyser attached to the exhaust vent of the engine. Investigation revealed that the inclusion of Al2O3 nanoparticles culminates in the amelioration of engine performance along with the alleviation of deleterious exhaust from engine. Furthermore, the incorporation of alumina nanoparticles assisted in the amelioration of dwindled performance attributed to biodiesel blending. More favourable results of nanoparticle inclusion were obtained at higher compression ratios compared to lower ones. Reckoning evinced that the Al2O3 nanoparticle is a lucrative introduction for fuels to boost the performance and dwindle the deleterious exhaust of diesel engines

Effect of Al₂O₃ Nanoparticles on Performance and Emission Characteristics of Diesel Engine Fuelled with Diesel–Neem Biodiesel Blends

Indagation in the sphere of nanoparticle utilisation has provided commendatory upshots in discrete areas of application varying from medicinal use to environmental degradation alleviation. This study incorporates alumina nanoparticles as additives to diesel and biodiesel blends. The prime objective of the present study was the scrutinisation of the denouement of Al2O3 nanoparticle incorporation in diesel–biodiesel blends on a diesel engine’s performance and emission characteristics. Test fuel samples were prepared by blending different proportions of biodiesel and dispersing two concentrations of alumina nanoparticles (25 and 50 ppm) in the diesel. Dispersion was made without the use of a nanoparticle stabiliser to meet real-world feasibility. High-speed shearing was employed to blend the biodiesel and diesel, while nanoparticles were dispersed in the blends by ultrasonication. The blends so devised were tested using a single-cylinder diesel engine at fixed RPM and applied load for three compression ratios. Upshots of brake-specific fuel consumption (BSFC) and brake thermal efficiency (BTE) for fuel samples were measured with LabView-based software, whereas CO emissions and unburnt hydrocarbon (UBHC) emissions were computed using an external gas analyser attached to the exhaust vent of the engine. Investigation revealed that the inclusion of Al2O3 nanoparticles culminates in the amelioration of engine performance along with the alleviation of deleterious exhaust from engine. Furthermore, the incorporation of alumina nanoparticles assisted in the amelioration of dwindled performance attributed to biodiesel blending. More favourable results of nanoparticle inclusion were obtained at higher compression ratios compared to lower ones. Reckoning evinced that the Al2O3 nanoparticle is a lucrative introduction for fuels to boost the performance and dwindle the deleterious exhaust of diesel engines

Experimental investigation of microstructural, mechanical and corrosion properties of 316L and 202 austenitic stainless steel joints using cold metal transfer welding

The objective of the current experimental study is to investigate influence of dissimilar welding between 316 L Austenitic and 202 Austenitic Stainless Steel. This is achieved through the utilization of Cold Metal Transfer (CMT) Welding in conjunction with the GTAW mode. Various welding approaches are employed, including the use of ER316L and ER309L fillers, as well as a no-filler (autogenous) welding technique. Microstructural observation shows martensitic formation in the interface region. The tensile test was conducted by using Universal Testing Machine for the weldment and found that ER316L filler (721 MPa) has higher strength than ER309L (672 MPa) and autogenous weld (528 MPa). Charpy impact test was used to determine toughness of the weldment and found that ER309L Filler weld has toughness (87 J) which is higher than ER316L filler weld (5 4 J) and autogenous weld (23 J). Vicker's hardness test shows that autogenous weld hardness (avg. 193.34 HV) which is highest than ER309L filler (avg.188 HV) and ER316L filler weld (avg. 184.67 HV). The intergranular corrosion test by double loop electrochemical potentiokinetic reactivation (DLEPR) test shows sensitization at highest degree for autogenous weld (18.25 %) than ER316L filler weld (9.22 %) and ER309L filler weld (7.28 %)

Advanced Modelling and Simulation of Intermetallic Reinforced Composites for Structural and Functional Applications

In recent years, intermetallic reinforced composites (IRCs) have garnered significant attention due to their exceptional mechanical properties, corrosion resistance, and high-temperature stability, making them ideal candidates for both structural and functional applications. This research paper presents an advanced modelling and simulation approach to understand the microstructural evolution, mechanical behaviour, and functional properties of IRCs. Utilizing a combination of finite element analysis (FEA), molecular dynamics (MD), and phase-field modelling, the study offers a comprehensive insight into the intricate interplay between the matrix, reinforcement, and the resultant composite behaviour. The developed models accurately predict the stress-strain response, thermal conductivity, and fatigue life of the IRCs under various loading and environmental conditions. Furthermore, the simulations provide a detailed understanding of the mechanisms governing crack initiation and propagation in these composites. The outcomes of this research not only pave the way for optimizing the design and processing parameters of IRCs but also underscore the potential of these materials in aerospace, automotive, and energy sectors. The findings presented herein serve as a foundational reference for researchers and engineers aiming to harness the full potential of intermetallic reinforced composites in advanced engineering applications

Investigating the Corrosion Behaviour and Electrochemical Properties of Intermetallic Matrix Composites

In the realm of advanced materials, intermetallic matrix composites (IMC) have garnered significant attention due to their potential for high-temperature applications and superior mechanical properties. This research delves into the corrosion behaviour and electrochemical characteristics of selected IMCs to elucidate their performance in aggressive environments. Employing potentiodynamic polarization tests and electrochemical impedance spectroscopy (EIS) , the study provides a comprehensive analysis of the corrosion kinetics and mechanisms inherent to these materials. The results indicate that the microstructural features, including the distribution of secondary phases and the nature of the matrix, play a pivotal role in determining the corrosion resistance. Furthermore, the presence of certain alloying elements was found to impart passivation capabilities, thereby enhancing the overall corrosion resistance. The EIS data revealed distinct time constants, suggesting multiple electrochemical processes at the interface. This study not only advances our understanding of the corrosion behaviour of IMCs but also underscores the importance of microstructural engineering in tailoring their electrochemical properties. The insights garnered hold profound implications for the design and application of IMCs in industries where corrosion resistance is paramount

Cloud manufacturing, internet of things-assisted manufacturing and 3D printing technology: Reliable tools for sustainable construction

10.3390/su13137327Sustainability (Switzerland)1313732

Effect of Mass on the Dynamic Characteristics of Single- and Double-Layered Graphene-Based Nano Resonators

Graphene has been widely and extensively used in mass sensing applications. The present study focused on exploring the use of single-layer graphene (SLG) and double-layer graphene (DLG) as sensing devices. The dynamic analysis of SLG and DLG with different boundary conditions (BDs) and length was executed using the atomistic finite element method (AFEM). SLG and DLG sheets were modelled and considered as a space–frame structure similar to a 3D beam. Spring elements (Combin14) were used to identify the interlayer interactions between two graphene layers in the DLG sheet due to the van der Waals forces. Simulations were carried out to visualize the behavior of the SLG and DLG subjected to different BDs and when used as mass sensing devices. The variation in frequency was noted by changing the length and applied mass of the SLGs and DLGs. The quantity of the frequency was found to be highest in the armchair SLG (6, 6) for a 50 nm sheet length and lowest in the chiral SLG (16, 4) for a 20 nm sheet length in the bridged condition. When the mass was 0.1 Zg, the frequency for the zigzag SLG (20, 0) was higher in both cases. The results show that the length of the sheet and the various mass values have a significant impact on the dynamic properties. The present research will contribute to the ultra-high frequency nano-resonance applications