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The utilization of multifunctional composite materials presents significant advantages in terms of system efficiency, cost-effectiveness, and miniaturization, making them highly valuable for a wide range of industrial applications. One approach to harness the multifunctionality of carbon fiber reinforced polymer (CFRP) is to integrate it with a secondary material to form a hybrid composite. In our previous research, we explored the use of carbonaceous material derived from coconut shells as a sustainable alternative to inorganic fillers, aiming to enhance the out-of-plane mechanical performance of CFRP. In this study, our focus is to investigate the influence of carbonized coconut shell particles on the non-structural properties of CFRP, specifically electromagnetic interference (EMI) shielding, thermal stability, and water absorption resistance. The carbonized material was prepared by thermal processing at 400 °C. Varying proportions of carbonized material, ranging from 1% to 5% by weight, were thoroughly mixed with epoxy resin to form the matrix used for impregnating woven carbon fabric with a volume fraction of 29%. Through measurements of scattering parameters, we found that the hybrid composites with particle loadings up to 3% exhibited EMI shielding effectiveness suitable for industrial applications. Also, incorporating low concentrations of carbonized particle to CFRP enhances the thermal stability of hybrid CFRP composites. However, the inclusion of carbonized particle to CFRP has a complex effect on the glass transition temperature. Even so, the hybrid composite with 2% particle loading exhibits the highest glass transition temperature and lowest damping factor among the tested variations. Furthermore, when subjected to a 7-day water immersion test, hybrid composites with 3% or less amount of carbonized particle showed the least water absorption. The favorable outcome can be attributed to good interfacial bonding at the matrix/fiber interface. Conversely, at higher particle concentrations, aggregation of particles and formation of interfacial and internal pores was observed, ultimately resulting in deteriorated measured properties. The improved non-structural functionalities observed in these biocomposites suggest the potential for a more sustainable and cost-effective alternative to their inorganic-based counterparts. This advancement in multifunctional composites could pave the way for enhanced applications of biocomposites in various industries.

 

A sophisticated machine learning framework was developed to design thermally robust shape memory vitrimers (TRSMVs) with superior recycling efficiency, an elevatedT_g, and outstanding shape memory properties, surpassing traditional limitations.

 

This study investigated the influence of diverse laser processing parameters on the thermophysical properties of Ti-6Al-4V and AlSi10Mg alloys manufactured via laser powder bed fusion. During fabrication, the laser power (50 W, 75 W, 100 W) and laser scanning speed (0.2 m/s, 0.4 m/s, 0.6 m/s) were adjusted while keeping other processing parameters constant. Besides laser processing parameters, this study also explored the impact of test temperatures on the thermophysical properties of the alloys. It was found that the thermophysical properties of L-PBF Ti-6Al-4V alloy samples were sensitive to laser processing parameters, while L-PBF AlSi10Mg alloy showed less sensitivity. In general, for the L-PBF Ti-6Al-4V alloy, as the laser power increased and laser scan speed decreased, both thermal diffusivity and conductivity increased. Both L-PBF Ti-6Al-4V and L-PBF AlSi10Mg alloys demonstrated similar dependence on test temperatures, with thermal diffusivity and conductivity increasing as the test temperature rose. The CALPHAD software Thermo-Calc (2023b), applied in Scheil Solidification Mode, was utilized to calculate the quantity of solution atoms, thus enhancing our understanding of observed thermal conductivity variations. A detailed analysis revealed how variations in laser processing parameters and test temperatures significantly influence the alloy’s resulting density, specific heat, thermal diffusivity, and thermal conductivity. This research not only highlights the importance of processing parameters but also enriches comprehension of the mechanisms influencing these effects in the domain of laser powder bed fusion.

 

Improving the fireproof performance of polymers is crucial for ensuring human safety and enabling future space colonization. However, the complexity of the mechanisms for flame retardant and the need for customized material design pose significant challenges. To address these issues, we propose a machine learning (ML) framework based on substructure fingerprinting and self-enforcing deep neural networks (SDNN) to predict the fireproof performance of flame-retardant epoxy resins. Our model is based on a comprehensive understanding of the physical mechanisms of materials and can predict fireproof performance and eliminate the needs for properties descriptors, making it more convenient than previous ML models. With a dataset of only 163 samples, our SDNN models show an average prediction error of 3% for the limited oxygen index (LOI). They also provide satisfactory predictions for the peak of heat release rate PHR and total heat release (THR), with coefficient of determination (R2) values of 0.87 and 0.85, respectively, and average prediction errors less than 17%. Our model outperforms the support vector model SVM for all three indices, making it a state-of-the-art study in the field of flame retardancy. We believe that our framework will be a valuable tool for the design and virtual screening of flame retardants and will contribute to the development of safer and more efficient polymer materials.

 

Development in self-healing materials and smart composites has continuously improved for many decades and has given rise to many real-life applications with implications for engineering materials, structures, and human beings who rely on these technological innovations to further human endeavor. This study involves the use of intrinsic selfhealing ability of poly (ethylene-co-methacrylic acid) thermoplastic, known by its commercial name as Surlyn 9520©, and combined two-way shape memory effect with Di cumyl-peroxide (DCP) cross-linked polybutadiene elastomer to achieve crack narrowing and closure with subsequent healing of the polymer blend surface. The simple batch mixing process resulted in an immiscible yet compatible blend, determined by two distinct melting peaks from DSC characterization and FTIR spectroscopy analysis. Different blends ratios of 80/20, 70/30, 60/40, 50/50 were investigated and characterized. However, the 80/20 blend was chosen to demonstrate the significance of the two-way shape memory effect, where a material experiences elongation upon cooling and contraction upon heating to achieve crack closure and effectual healing. Two sets of samples were studied; control Sample known as 2A and 2B samples were one time programmed to about 300% strain. Self-healing, which is a function of the poly(ethylene-co-methacrylic) acid component of the blend, was established for both sets of specimens. The flexural properties from three-point bending test indicate that although both sets of samples achieved good healing efficiencies, the 2B programmed samples displayed better healing efficiencies than the control by 30%.

 

Abstract In this paper, an open-cell metallic foam was filled in by a tough shape memory polymer (SMP), to form a hybrid metal/polymer composite with multifunctionalities and enhanced mechanical properties. This work aims to study the positive composite actions between the metallic skeleton and the SMP filler. Mechanical, thermal, and conductive properties of the resulting hybrid composite were evaluated and compared to the individual components. Uniaxial compression tests and shape memory effect tests were conducted. Results demonstrated an improvement in the compressive strength and toughness. The hybrid composite also exhibited excellent shape recovery and high recovery stress of 1.76 MPa. Infrared thermography has been used to verify the free shape recovery by Joule heating. Sandwich structures with the hybrid composite as the core were studied through low velocity impact test and three-point bending test. The sandwich structures with the composite foam core showed significant performance improvement in both tests. Electrical resistivity study during the three-point bending test validates the possible application of this multifunctional polymer-aluminum open cell foam composite as strain sensor. This type of hybrid composites can be beneficial in many industrial sectors that search for an ideal combination of high strength, high toughness, low weight, damage sensing, and excellent energy absorption capabilities. 

Herein, we present a new sandwich panel composed of a carbon fiber grid-stiffened shape memory vitrimer (SMV) core. The sandwich panels were fabricated via a pin-guided dry-weaving technology, and their impact responses were evaluated via low-velocity impact testing. The main failure mode observed after the first round of impact was the transverse cracking of the SMV matrix in the sandwich core. The healing efficiency according to the crack initiation energy (CIE) was found to be 76.5% after the first healing cycle. Even after the second healing cycle, the healing efficiency was greater than 72%. From the low-velocity impact tests, reinforcing the pure SMV core with a grid-skeleton enhanced the impact resistance significantly, that is, the crack initiation energy and peak load were increased by 64.0% and 169.0%, respectively. The results also show that smaller bay area leads to higher impact resistance. With the repeated crack healing, increased impact tolerance, and shape memory effect, it is expected that the sandwich panels will have a good possibility for usage in aerospace and automotive applications.

 

Additive Manufacturing (AM) is a crucial component of the smart manufacturing industry. In this paper, we propose an automated quality grading system for the fused deposition modeling (FDM) process as one of the major AM processes using a developed real-time deep convolutional neural network (CNN) model. The CNN model is trained offline using the images of the internal and surface defects in the layer-by-layer deposition of materials and tested online by studying the performance of detecting and grading the failure in AM process at different extruder speeds and temperatures. The model demonstrates an accuracy of 94% and specificity of 96%, as well as above 75% in measures of the F-score, the sensitivity, and the precision for classifying the quality of the AM process in five grades in real-time. The high-performance of the model could not be achieved with the values usually used for printing temperature and printing speed, only in addition with much higher values. The proposed online model adds an automated, consistent, and non-contact quality control signal to the AM process. The quality monitoring signal can also be used by the AM machine to stop the AM process and eliminate the sophisticated inspection of the printed parts for internal defects. The proposed quality control model ensures reliable parts with fewer quality hiccups while improving performance in time and material consumption.