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In this study, experimental research has been done to investigate and analyze the effects of fiber hybridization and stacking configurations on the impact performance of carbon/flax bio-hybrid composite laminates. A total of four composite laminates with pure carbon, sandwich, symmetric, and asymmetric stacking configurations were manufactured and investigated in terms of low-velocity impact test with varying energies between 30J and 75J. Both qualitative and quantitative analysis were performed to analyze the damage and failure patterns in the composite layups and were compared with the pure carbon-based layup to identify the effects stacking configuration. The experimental findings showed the symmetric layup having a consistent distribution of flax fiber layers, showed the most enhanced performance as compared to the carbon-based layup Moreover, damage and failure modes differed among layups and increased with varying impact energies. Furthermore, to enable the thorough comparison of the configurations, Composite Performance Index (CPI) was developed, which suggests that bio-hybrid configurations when designed optimally with a suitable number of evenly distributed flax layers, can equal or exceed the performance of a pure carbon fiber configuration.

Jet impingement is considered to be an effective technique to enhance the heat transfer rate, and it finds many applications in the scientific and industrial horizons. The objective of this paper is to summarize heat transfer enhancement through different jet impingement methods and provide a platform for identifying the scope for future work. This study reviews various experimental and numerical studies of jet impingement methods for thermal-hydraulic improvement of heat transfer surfaces. The jet impingement methods considered in the present work include shapes of the target surface, the jet/nozzle–target surface distance, extended jet holes, nanofluids, and the use of phase change materials (PCMs). The present work also includes both single-jet and multiple-jet impingement studies for different industrial applications.

In today’s world, a brain tumor is one of the most serious diseases. If it is detected at an advanced stage, it might lead to a very limited survival rate. Therefore, brain tumor classification is crucial for appropriate therapeutic planning to improve patient life quality. This research investigates a deep-feature-trained brain tumor detection and differentiation model using classical/linear machine learning classifiers (MLCs). In this study, transfer learning is used to obtain deep brain magnetic resonance imaging (MRI) scan features from a constructed convolutional neural network (CNN). First, multiple layers (19, 22, and 25) of isolated CNNs are constructed and trained to evaluate the performance. The developed CNN models are then utilized for training the multiple MLCs by extracting deep features via transfer learning. The available brain MRI datasets are employed to validate the proposed approach. The deep features of pre-trained models are also extracted to evaluate and compare their performance with the proposed approach. The proposed CNN deep-feature-trained support vector machine model yielded higher accuracy than other commonly used pre-trained deep-feature MLC training models. The presented approach detects and distinguishes brain tumors with 98% accuracy. It also has a good classification rate (97.2%) for an unknown dataset not used to train the model. Following extensive testing and analysis, the suggested technique might be helpful in assisting doctors in diagnosing brain tumors.

Permutation flow-shop scheduling is the strategy that ensures the processing of jobs on each subsequent machine in the exact same order while optimizing an objective, which generally is the minimization of makespan. Because of its NP-Complete nature, a substantial portion of the literature has mainly focused on computational efficiency and the development of different AI-based hybrid techniques. Particle Swarm Optimization (PSO) has also been frequently used for this purpose in the recent past. Following the trend and to further explore the optimizing capabilities of PSO, first, a standard PSO was developed during this research, then the same PSO was hybridized with Variable Neighborhood Search (PSO-VNS) and later on with Simulated Annealing (PSO-VNS-SA) to handle Permutation Flow-Shop Scheduling Problems (PFSP). The effect of hybridization was validated through an internal comparison based on the results of 120 different instances devised by Taillard with variable problem sizes. Moreover, further comparison with other reported hybrid metaheuristics has proved that the hybrid PSO (HPSO) developed during this research performed exceedingly well. A smaller value of 0.48 of ARPD (Average Relative Performance Difference) for the algorithm is evidence of its robust nature and significantly improved performance in optimizing the makespan as compared to other algorithms.

An ejector is a simple mechanical device that can be integrated with power generation or the refrigeration cycle to enhance their performance. Owing to the complex flow behavior in the ejector, the performance prediction of the ejector is done by numerical simulations. However, to evaluate the performance of an ejector integrated power cycle or refrigeration cycle, the need for simpler and more reliable thermodynamic models to estimate the performance of the ejector persists. This research, therefore, aims at developing a single mathematical correlation that can predict the ejector performance with reasonable accuracy. The proposed correlation relates the entrainment ratio and the pressure rise across the ejector to the area ratio and the mass flow rate of the primary flow. R141b is selected as the ejector refrigerant, and the results obtained through the proposed correlation are validated through numerical solutions. The comparison between the analytical and numerical with experimental results provided an error of less than 8.4% and 4.29%, respectively.

This study investigates the effects of different fibre stacking configurations on the low-velocity impact (LVI) resistance and compression after impact (CAI) behaviour of hybrid laminates reinforced with flax and basalt fibres. Five types of laminates with different stacking sequences were manufactured using twill weave basalt and flax fibre fabrics, resulting in laminates with 15 layers each. The configurations included symmetric, asymmetric, and sandwich-type laminates with varying the distribution of flax and basalt fibre layers. The laminates were subjected to drop-weight impact tests at energy levels of 30 J, 45 J, and 60 J to evaluate their impact resistance. Post-impact, CAI tests were conducted according to ASTM standards to assess the residual compressive strength. Furthermore, both quantitative and qualitative analysis of results were conducted to investigate the effect of variations in stacking sequences. The experimental results showed that the placement of flax fibre layers significantly influences both the impact performance and residual strength of the hybrid laminates. The results revealed that the symmetric laminate having an alternating arrangement of flax and basalt fibres through its thickness, exhibited superior impact resistance and the highest residual compressive strength across all energy levels. Furthermore, different types of damage mechanisms were also observed depending on the variation in stacking sequences and impact energies, which include the damage the permanent indentation, matrix cracking, fibre pull-out, and delamination. At lower impact energies, all laminates primarily exhibited surface indentations and matrix cracking without perforation.

This study pioneers an integrated approach combining experimental analysis and machine learning (ML) predictions to assess the low-velocity impact (LVI) response of synthetic/natural bio-hybrid fiber-reinforced polymer (HFRP) composite materials. Five different stacking sequences of carbon/flax bio-HFRP were tested for LVI with impact energies from 15 to 90 J, and data such as peak impact force, damage area, and damage extension were recorded. Symmetric configuration with consistent dispersal of natural flax fibers across laminate demonstrated improved impact resistance. Furthermore, six ML algorithms were used: decision tree (DT), random forest, deep neural network with Adam optimizer (DNN-Adam), DNN with stochastic gradient (SGD) optimizer (DNN-SGD), recurrent neural network (RNN) with Adam optimizer (RNN-Adam), and RNN with SGD optimizer (RNN-SGD). Model performance was evaluated using coefficient of determination ( R 2 ), mean square error (MSE), and mean absolute error (MAE). The DT ML model achieved best performance in predicting peak impact force having maximum depth count of 8 and leaf nodes count of 28. For damage area, again, DT model with maximum depth count of 6 and leaf nodes count of 23 exhibited better performance. On the other hand, for damage extension, the RNN-SGD model, having four hidden layers and 70 neurons, outperformed other ML models. Among the investigated parameters, the highest correlation ( R 2  = 0.9987 for training and 0.9922 for test datasets) and lowest errors (MSE = 0.0294 and MAE = 0.1344) were achieved for predicting damage extension. This study is the first to apply advanced ML techniques to predict mechanical responses such as peak impact force, damage area, and damage extension in carbon/flax bio-HFRP composites under LVI conditions, enhancing accuracy and reducing the testing, thereby optimizing resources and significantly minimizing time.

High-Pressure Die Casting (HPDC) is one of the major production processes of the automotive industry, widely used to manufacture geometrically complex nonferrous castings. The mechanical strength and microstructure of HPDC-manufactured products change with variation in several process parameters such as injection pressure, molten temperature, 1st and 2nd stage plunger velocities, cooling temperature, etc. Since these process parameters directly affect casting quality, their optimum combination is needed to maximize productivity of the process and minimize casting defects such as porosity, pinholes, blowholes, etc. Hence, to tackle this problem, an approach is presented in this paper that minimizes the major casting defect, i.e., porosity, in the HPDC process by optimizing parameters through Design Of Experiments (DOE) in combination with Taguchi Analysis. The obtained results showed that cooling time, injection pressure, and 2nd stage plunger velocity had a major influence on the response factor (density of the cast part). It was further concluded that by using a 178-bar injection pressure, 665°C molten temperature, 5 seconds of cooling time, 210°C mold temperature, 0.20 m.s-1 1st stage plunger velocity, and 6.0 m.s-1 2nd stage plunger velocity, the rejection rate of the selected part due to porosity was reduced by 61%.

In this paper, the effect of aluminum oxide, Al[sub.2] O[sub.3] , nanoparticles’ inclusion into Epocast 50-Al/946 epoxy adhesive at different temperatures, subjected to quasi-static tensile loading, is numerically investigated. The single-lap adhesive joint with two different types of material adherends (composite fiber-reinforced polymer (CFRP) and aluminum (Al) 5083 adherends) and adhesive Epocast 50-A1/hardener 946 were modeled in ABAQUS/CAE. A numerical methodology was proposed to analyze the effect on peel stress and shear stress by adding Al[sub.2] O[sub.3] nanoparticles into the neat adhesive at 25 °C, 50 °C, and 75 °C temperatures at four different locations of the adhesive regions: the interface of the adhesive and aluminum adherend (location A), the middle plane of the adhesive region (location B), the middle longer edge (along the length of the adhesive, location C), and the middle shorter edge (along the width of the adhesive, location D). The results showed that adding nanoparticles into the neat adhesive improves joint strength at room and elevated temperatures. High peel and shear stresses were recorded near both edges of the locations (A, B, C, and D). For location A, adding nanofillers into the adhesive resulted in the reduction in peak peel stress by 1.3% for 25 °C; however, it increased by 2.7% and 10.7% for 50 °C and 75 °C temperatures, respectively. Furthermore, the peak shear stress observed a considerable reduction of 19.6% for 25 °C, but it increased by 7.7% and 8.7% for 50 °C and 75 °C temperatures, respectively, for location A. The same trend was also observed for other locations (i.e., B, C, and D). This signified that adding aluminum oxide nanoparticles in the adhesive resulted in increased stiffness at higher temperatures and increased ductility of the joint, as compared to the joint with neat adhesives at room temperature. Moreover, it was observed that locations A and B were more vulnerable to damage initiation, as the peak of stresses lay near the edges, indicating that the crack initiation would take place close to the edges and propagate towards the center, leading to ultimate failure.

Double strap lap adhesive joints between metal (AA 6061-T6) and composite (carbon/epoxy) laminates were fabricated and characterized based on strength. Hand layup methods were used to fabricate double strap match lap joints and double strap mismatch lap joints. These joints were compared for their strength under static and fatigue loadings. Fracture toughness (GIIC) was measured experimentally using tensile testing and validated with numerical simulations using the cohesive zone model (CZM) in ABAQUS/Standard. Fatigue life under tension–tension fluctuating sinusoidal loading was determined experimentally. Failure loads for both joints were in close relation, whereas the fatigue life of the double strap mismatch lap joint was longer than that of the double strap match lap joint. A cohesive dominating failure pattern was identified in tensile testing. During fatigue testing, it was observed that inhomogeneity (air bubble) in adhesive plays a negative role while the long time duration between two consecutive cycle spans has a positive effect on the life of joints.