Stress–strain relationship model of glulam bamboo under axial loading

Glulam bamboo has been preliminarily explored for use as a structural building material, and its stress–strain model under axial loading has a fundamental role in the analysis of bamboo components. To study the tension and compression behaviour of glulam bamboo, the bamboo scrimber and laminated bamboo as two kinds of typical glulam bamboo materials were tested under axial loading. Their mechanical behaviour and failure modes were investigated. The results showed that the bamboo scrimber and laminated bamboo have similar failure modes. For tensile failure, bamboo fibres were ruptured with sawtooth failure surfaces shown as brittle failure; for compression failure, the two modes of compression are buckling and compression shear failure. The stress–strain relationship curves of the bamboo scrimber and laminated bamboo are also similar. The tensile stress–strain curves showed a linear relationship, and the compressive stress–strain curves can be divided into three stages: elastic, elastoplastic and post-yield. Based on the test results, the stress–strain model was proposed for glulam bamboo, in which a linear equation was used to describe the tensile stress–strain relationship and the Richard–Abbott model was employed to model the compressive stress–strain relationship. A comparison with the experimental results shows that the predicted results are in good agreement with the experimental curves.

Development of a composite friction modifier with carbon nanotubes for applications at the wheel–rail interface

Wear of wheels and rails is a major problem in railway transportation industry. Solid lubricants constitute a cost-efficient alternative to control wear and friction at the wheel–rail interface, especially when a fine-tuned balance between traction force and energy consumption is sought. In this work, composite friction modifiers (CFMs) composed of a vinyl ester matrix reinforced with molybdenum disulfide and carbon nanotubes were developed. The total solid additive content was less than a half in comparison with a commercial product available on the market, which was used as a reference. A benchmarking study of the CFM was carried out by means of tribological tests in a twin-disc machine at a contact pressure of 1.1 GPa and different slip values. The results indicated that the developed CFM reduce coefficient of traction by 10% compared to unlubricated conditions that is similar to the reference. However, the total mass loss of steel components due to wear under CFM lubrication was lower than in the reference test.

Significance of tungsten disulfide on the mechanical and machining characteristics of phosphor bronze metal matrix composite

The phosphor bronze (PB) is widely preferred in various engineering applications due to its high strength, toughness, fine grain size, low coefficient of friction, and better corrosion resistance. The present work is to investigate the effect of tungsten disulfide (WS2) solid lubricant particle reinforcement in the phosphor bronze metal matrix composite (PBMC) through the mechanical and machining characteristics. The different variant of the composite is fabricated using stir casting technique by varying the volume percentage of WS2 particle from 0% to10%. The prepared PBMC samples are subjected to mechanical and machining (boring and high-speed turning) characterizations. The hardness (Brinell hardness) and flexural strength of the composites are examined as per the ASTM standard. The surface roughness (Ra) of the PBMC sample is analyzed through the boring and high-speed turning operations by varying the spindle speed, feed rate, and depth of cut. The scanning electron microscope (SEM) is employed to confirm the uniform dispersion of the reinforcement particle through the microstructural analysis. The presence of WS2 particles and other ingredients is ensured by X-ray diffraction analysis in the composites. The influence of WS2 reinforcement particles on tool life is analyzed on the PBMC4 (PBMC with 8% WS2) with the predefined machining parameters in the high-speed turning operation. The increase in WS2 particle (0–10%) improves the hardness (11.85%) and flexural strength in PBMC4 as compared to PBMC1 (PBMC with 0% WS2). At a higher spindle speed (1200 r/min), the Ra is reduced in PBMC1 as compared to 900 r/min, whereas the rest of the PBMC sample show higher surface irregularity at 1200 r/min.

Cypermethrin elimination using Fe-TiO2 nanoparticles supported on coconut palm spathe in a solar flat plate photoreactor

In this research, the photocatalytic degradation of cypermethrin using iron-titanium dioxide (Fe-TiO2) nanoparticles supported in a biomaterial was evaluated. The nanoparticles of TiO2 were synthesized by the green chemistry method assisted by ultrasound and doped by chemical impregnation using Fe+3:Ti molar ratios of 0, 0.05, 0.075 and 0.1 to make efficient use of direct sunlight ( λ > 310 nm). All nanoparticles were immobilized on the surface of coconut spathe ( Cocos nucifera). The degradation was carried out at room temperature and natural pH in a flat plate solar reactor, on which the composite material was subjected. The concentration of cypermethrin was determined after 12,000 J m−2 of accumulated radiation from gas chromatography–mass spectrometry and the resulting material was characterized by scanning electron microscopy, transmission electron microscopy, X-ray diffraction, Fourier-transform infrared spectroscopy, ultraviolet–visible diffuse reflectance spectroscopy, and Brunauer-Emmett-Teller (BET) surface area. The best results were achieved with the use of Evonik TiO2 P-25, Fe:Ti = 0 and Fe:Ti = 0.05 in suspension, with percentages of degradation of cypermethrin of 99.84%, 99.62%, and 100%, respectively. However, the materials supported on the biomaterial of coconut allowed to reach degradation percentages higher than 80%, with the advantage that it minimizes operating costs, as they are not necessarily filtering or centrifuging processes to separate the catalyst.

Estimation of fastener pull-through resistance of composite laminates based on generalized regression neural network

The fastener pull-through resistance is a key performance index of composite laminates used for engineering application, and increasing research attention is being paid to developing methods for its calculation or estimation. The currently available research methods mainly focus on the standard test and the finite element analysis for determining the pull-through resistance of composite laminates suffering transverse load by the fasteners. Based on the results of the fastener pull-through resistance experiment performed on X850 composite laminates, a model for estimating the maximum affordable load of composite laminates for the fastener pull-through resistance is proposed, using generalized regression neural network technology. The inputs of this model are simplified to six parameters: the proportion of the ±45° layer of the laminates, the number of the layers, the thickness of the laminates, the bolt head shape, whether the bolt has a washer or not, and the nominal diameter of the bolt; the Gauss function is used as the hidden layer function. The model uses a large portion of the experimental data to train for finding the optimal smoothness factor, which is used to reconstruct the model, and simulation is performed with the remainder of the experimental data. The comparison between the estimated results using the model and the experimental results shows that the generalization ability of the proposed model can meet the estimation requirements. Moreover, the pull-through resistance of composite laminates under transverse load from a fastener can be estimated with high accuracy after some standard fastener pull-through resistance tests of the composite laminates.

Evaluation of the shear bond strength of resin cement to enamel after bleaching with Er: YAG and diode laser

Objective: The shear bond strength (SBS) of composite resin cement and color change to enamel bleached with two different concentrations of hydrogen peroxide (HP) bleaching agents in combination with two different laser applications was evaluated. Materials and Methods: The Er: YAG laser ( λ = 2940 nm) and diode laser ( λ = 940 nm) were used with the bleaching agent consisting of 40% HP opalescence (Opalescence Xtra Boost, Ulgen, South Jordan, UT, USA) and 35% HP whiteness (Whiteness HP Blue, FGM Dental Products, Joinville, SC, Brazil). Seventy-two human extracted teeth were randomly divided into two groups: For the 40% HP treatment, 36 specimens were divided into three subgroups: (1) HP agent with no laser treatment, (2) HP agent treated with Er: YAG laser, and (3) HP agent treated with diode laser. The same protocol was performed for the 35% HP. The color change was analyzed using the Commission Internationale de l’ Eclairage (CIE) L* a* b* system and surface roughness was analyzed by an atomic force microscope. The specimens were bonded with resin cement and a shear bond test was performed at 0.5 mm/min. The failure surfaces were evaluated using scanning electron microscope analysis. Results: Δ E value of the 40% HP + Er: YAG group was significantly higher than the other groups while diode groups showed the lower SBS values than the control groups ( p < 0.05). Conclusions: The Er: YAG laser with 40% HP may be effective for the brighter teeth and the use of Er: YAG laser produced higher adhesion between enamel and resin cement than diode laser.

Intelligent recognition of acoustic emission signals from damage of glass fiber-reinforced plastics

Glass fiber-reinforced plastics (GFRP) is widely used in many industrial fields. When acoustic emission (AE) technology is applied for dynamic monitoring, the interfering signals often affect the damage evaluation results, which significantly influences industrial production safety. In this work, an effective intelligent recognition method for AE signals from the GFRP damage is proposed. Firstly, the wavelet packet analysis method is used to study the characteristic difference in frequency domain between the interfering and AE signals, which can be characterized by feature vector. Then, the model of back-propagation neural network (BPNN) is constructed. The number of nodes in the input layer is determined according to the feature vector, and the feature vectors from different types of signals are input into the BPNN for training. Finally, the wavelet packet feature vectors of the signals collected from the experiment are input into the trained BPNN for intelligent recognition. The accuracy rate of the proposed method reaches to 97.5%, which implies that the proposed method can be used for dynamic and accurate monitoring of GFRP structures.

Evaluation of surface roughness in drilling particle-reinforced composites

Aluminum matrix composite materials being used in different sectors including automobile, aerospace, defense, and medical and are currently displacing unreinforced materials with their superior mechanical properties. The metal removal process of drilling is widely used in many structural applications. This study experimentally investigates the drilling characteristics of silicon carbide (SiCp)-reinforced Al 7075 composites produced by stir casting method. Also, two different drill materials with high-speed steel (HSS) and titanium nitride (TiN)-coated HSS carry out in drilling operation. The effect of operational parameters such as cutting speed and feed rate and materials parameters such as weight fraction of reinforcement and cutting tools on the surface roughness of drilled holes were evaluated in the drilling operations. The results of the drilling test indicate that the feed rate and cutting speed have a very strong effect on the surface roughness of matrix alloy and composite materials. The surface roughness ( Ra) values increased with increasing the feed rate and decreased with increasing the cutting speed. Under 0.10 mm/rev and 20 m/min drilling conditions and using HSS drill, surface roughness values for matrix, 5% SiC-, 10% SiC-, and 15% SiC-reinforced composites, were obtained 2.57, 2.59, 2.61, and 2.64 µm, respectively; besides, using TiN-coated HSS drill, surface roughness values were obtained 1.60, 1.63, 1.64, and 1.66 µm, respectively. An increase in the weight fraction of the abrasive SiC particle resulted in a very crucial deterioration quality of the drilled hole. TiN-coated HSS drills better performance exhibits than uncoated HSS drills for all the drilling operations about surface roughness properties. Short chip formations observed both the matrix alloy and the composite materials for two different drills in the drilling operations.

Tribological characterization of particulate-reinforced aluminum metal matrix nanocomposites: A review

Aluminum (Al)-based composites are on increasing usage in sectors like ground transportation, aerospace, sports, and infrastructure because of the improved properties such as high strength to weight ratio, corrosion, fatigue, and wear resistance. Several applications involving dynamic contact stresses require excellent wear and frictional performance for improved life. Nanocomposites are found to perform exceedingly better than microcomposites and alloys in several lab scale tribological investigations carried out so far in the last decade. In this article, an attempt is made to review those published reports about dry sliding tribological behavior of particulate-reinforced Al nanocomposites. Wear and friction being system properties are found to get influenced by intrinsic factors such as reinforcement, fabrication method, microstructure; extrinsic parameters like load, speed, contact conditions and the system generated in situ tribolayer all being interrelated.

Mechanical properties of coconut trunk particle/polyester composite based on alkali treatment

Coir is a well-known natural fiber extracted from the husk of a coconut tree. In polymer composite materials, the ultimate performance of coir has been shown using surface modification methods. Among them, sodium hydroxide (NaOH) is a comparative and efficient solution used for surface treatment of lignocellulosic fiber. In contrast to coir, coconut timber, a hardwood that dominates the weight of the coconut tree, has not been appropriately considered for use in polymer composites. Therefore, in this article, coconut trunk particle/unsaturated polyester resin composites were experimentally investigated. As a pioneering study, a large range of NaOH concentrations from 2 wt% to 10 wt% (with an interval of 2 wt%) was utilized to treat the surface of the filler. Finally, 4 wt% alkali solution was found as the best content for surface modification based on the mechanical properties of the composite, including those determined by tensile, flexural, and impact test results.