11 research outputs found
Dry sliding wear behaviour of Ta/NbC filled glass-epoxy composites at elevated temperatures
No full textIn this work an attempt was made to evaluate wear loss, specific wear rate and coefficient of friction of Glass-Epoxy (G-E) composites with and without Tantalum Niobium Carbide (Ta/NbC) filler. A vacuum assisted resin transfer moulding (VARTM) technique was employed to fabricate the composite specimens. The fabricated wear specimens were tested by using pin-on-disk test rig at various temperatures viz., 30, 60, 90 and 120° C at normal applied loads of 10 N and 20 N. Sliding velocity of the disc of 1.5 m/s was maintained and test was continued for each sample up to a sliding distance of 5000 m. The wear loss in both the composites increases with increase in temperature and applied normal load. However, Ta/NbC particulate filler incorporated G-E composite exhibits lower wear rate and higher coefficient of friction as compared to unfilled G-E composite. The features of worn surfaces of the specimens were examined under scanning electron microscopy (SEM) and findings are analysed
Low Velocity Angular Impact Test on E Glass Epoxy Laminates using Multi-Orientation Fixture
No full text
Analysis of factors influencing deflection in sandwich panels subjected to low-velocity impact
No full textEffect of Nanoclay in Epoxy-based Fibre-glass Composite Laminates subjected to Low Velocity Impact
No full textMathematical modelling to predict the tensile strength of additively manufactured AlSi10Mg alloy using artificial neural networks
No full textIntegrating machine learning in additive manufacturing to simulate real manufacturing outcomes can significantly reduce the cost of manufacturing through selective manufacturing. However, limited research exists on developing a prediction model for the mechanical properties of the material. The input variables include key selective laser melting process parameters such as laser power, layer thickness, scan speed, and hatch spacing, with tensile strength as the output. The artificial neural network (ANN) based mathematical model is compared with a second-degree polynomial regression model. The robustness of both models was further assessed with the new data points beyond those used in the development of ANN-based mathematical model and regression model. The results demonstrate that the proposed ANN-based mathematical model offers superior accuracy, with a mean absolute percentage error (MAPE) value of 4.74 % and the R2 (goodness of fit) value of 0.898 in predicting the strength of AlSi10Mg. The ANN-based mathematical method also demonstrates the strong performance on the new data, achieving a regression value of 0.68. This concludes that the model shows sufficient proof to consider a viable option for predicting the tensile strength
Influence of manufacturing parameters on the strength of PLA parts using Layered Manufacturing technique: A statistical approach
No full textInfluence of manufacturing parameters on the strength of PLA parts using Layered Manufacturing technique: A statistical approach
No full textA 3D printing was successfully used to fabricate samples of Polylactic Acid (PLA). Processing parameters such as Lay-up speed, Lay-up thickness, and printing nozzle were varied. All samples were tested for flexural strength using three point load test. A statistical mathematical model was developed to correlate the processing parameters with flexural strength. The result clearly demonstrated that the lay-up thickness and nozzle diameter influenced flexural strength significantly, whereas lay-up speed hardly influenced the flexural strength
Experimental and Numerical Investigation on Damage Resistance Characteristics of Woven E-Glass/Epoxy Composite Laminates Subjected to Drop-Weight Impacts
No full textThe utilization of composite materials in structural components has been on the rise in the aerospace, automotive, and marine industries. Although these materials offer numerous benefits, they can be damaged by various sources, such as low-velocity drop-weight impacts. Debris on a runway or tools falling onto composites can cause this type of impact, which has led to extensive research on crashworthiness and impact damage assessment. This study aimed to assess the response of woven E-glass/epoxy composite laminates under low-velocity drop-weight impacts. Tests were conducted using experimental methods and numerical simulations with a drop-weight impact-testing machine and the explicit finite element software LS-DYNA. The experimental tests were performed according to ASTM standards, with varied magnitudes of initial impact energy ranging from 7.85 J to 23.54 J and a specimen thickness of 4 mm. Force–time, energy–time, and force–displacement histories, obtained through the experiments and numerical analyses along with images of the damaged specimens, were examined. The effective stress contours are also illustrated to gain a deeper comprehension of the stress distribution in the laminates. The findings demonstrated that the impact energy significantly influences the impact response of the specimens, and both the experimental and numerical analyses yielded similar results, validating the modeling approach for the impact problem in composite materials. The study provides insight into the damage mechanism of woven E-glass/epoxy composite laminates under drop-weight impacts and is expected to contribute to a better understanding of their response in low-velocity drop-weight impact events
