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1. Study of Agave Fiber-Reinforced Biocomposite Films

   https://doi.org/10.3390/ma12010099  

   Annandarajah, Cindu ; Li, Peng ; Michel, Mitchel ; Chen, Yuanfen ; Jamshidi, Reihaneh ; Kiziltas, Alper ; Hoch, Richard ; Grewell, David ; Montazami, Reza ( January 2019 , Materials)

   Thermoplastic resins (linear low-density polyethylene (LLDPE), high-density polyethylene (HDPE), and polypropylene (PP)) reinforced by different content ratios of raw agave fibers were prepared and characterized in terms of their mechanical, thermal, and chemical properties as well as their morphology. The morphological properties of agave fibers and films were characterized by scanning electron microscopy and the variations in chemical interactions between the filler and matrix materials were studied using Fourier-transform infrared spectroscopy. No significant chemical interaction between the filler and matrix was observed. Melting point and crystallinity of the composites were evaluated for the effect of agave fiber on thermal propertiesmore »of the composites, and modulus and yield strength parameters were inspected for mechanical analysis. While addition of natural fillers did not affect the overall thermal properties of the composite materials, elastic modulus and yielding stress exhibited direct correlation to the filler content and increased as the fiber content was increased. The highest elastic moduli were achieved with 20 wt % agave fiber for all the three composites. The values were increased by 319.3%, 69.2%, and 57.2%, for LLDPE, HDPE, and PP, respectively. The optimum yield stresses were achieved with 20 wt % fiber for LLDPE increasing by 84.2% and with 30 wt % for both HDPE and PP, increasing by 52% and 12.3% respectively.« less
2. Influence of multi-walled carbon nanotubes on the hydration products of ordinary Portland cement paste

   https://doi.org/https://doi.org/10.1016/j.cemconres.2020.106197  

   Jiaxin Chen, Ange-Therese Akono ( November 2020 , Cement and concrete research)

   We elucidate the mechanisms by which multi-walled carbon nanotubes (MWCNTs) influence the microstructure, fracture behavior, and hydration of cement paste. We disperse MWCNTs using a multi-step approach that involves high-energy pre-dispersion using ultrasonic energy followed by low-energy dispersion using un-hydrated cement particles. In turn, the low-energy dispersion step involves high-shear mixing and mechanical stirring. High-resolution environmental scanning electron microscopy of cement+0.2 wt% MWCNT, cement+0.5 wt% MWNCT, and of cement+1 wt% MWCNT show that MWCNTs bridge air voids, thereby refining the pore size and strengthening the C-S-H matrix. The fracture toughness increased by 9.38% with the addition of 0.2 wt% multi-walledmore »carbon nanotubes, and by 14.06% with the addition of 0.5 wt% multi-walled carbon nanotubes and ligament bridging was the dominant toughening mechanism. Moreover, for all reinforcement levels, MWCNTs induced a conversion of low-density C-S-H into high-density C-S-H along with a drastic drop in the capillary porosity: adding 0.1–0.5 wt% MWCNT resulted in a 200% increase in the volume fraction of high-density C-S-H. Thus, our experiments show that MWCNT enhances the mechanical properties and transport properties by: (i) promoting high-density C-S-H formation, (ii) promoting calcium hydroxide formation, (iii) filling microscopic air voids, (iv) reducing the capillary porosity, (v) increasing the fraction of small gel pores (1.2–2 nm in size), and (vi) by bridging microcracks.« less
3. Metal-matrix composites embedded with piezoelectric PVDF sensors using ultrasonic additive manufacturing

   https://doi.org/10.1117/12.2581845  

   Ramanathan, Arun Kumar ; Gingerich, M. Bryant ; Headings, Leon M. ; Dapino, Marcelo J. ( March 2021 , NDE 4.0 and Smart Structures for Industry, Smart Cities, Communication, and Energy)

   Meyendorf, Norbert G. ; Farhangdoust, Saman (Ed.)

   Metal-matrix composites with active components have been investigated as a way to functionalize metals. As opposed to surface-mounted approaches, smart materials embedded in metals can be effectively shielded against the environment while providing in-situ sensing, health monitoring, actuation, or energy harvesting functions. Typical manufacturing approaches can be problematic, however, in that they may physically damage the smart material or degrade its electromechanical properties. For instance, non-resin-based embedment procedures such as powder metallurgy involve isostatic compression and diffusion bonding, leading to high process temperatures and breakdown of the electromechanical properties of the active component to be embedded. This paper presents themore »development and characterization of an aluminum-matrix composite embedded with piezoelectric polyvinylidene fluoride (PVDF) sensors using ultrasonic additive manufacturing (UAM). UAM incorporates the principles of solid-state, ultrasonic metal welding and subtractive processes to fabricate metal-matrices with seamlessly embedded smart materials and without thermal loading. As implemented in this study, the UAM process uses as-received, commercial Al 6061 tape foilstock and TE Connectivity PVDF film. In order to increase the mechanical coupling between the sensor and the metal-matrix without the aid of adhesives, the PVDF sensor is embedded with an empirically optimized pre-compression defined by the tape foils welded above the sensor. The specimen is characterized by tensile (d31 mode), bending (d31 mode), and compression tests (d33 mode) to evaluate its functional performance. Within the investigated load range, the specimen exhibits open-circuit sensitivities of 4.6 mV/N under uniaxial tension and 9.7 mV/N under compressive impulse tests with better than 95% linearity and frequency bandwidth of several kilohertz. The technology presented in this study could be applied for load and tactile sensing, impact detection and localization, thermal measurements, energy harvesting, and non-destructive testing applications.« less
4. Artificial neural networks and phenomenological degradation models for fatigue damage tracking and life prediction in laser induced graphene interlayered fiberglass composites

   https://doi.org/https://doi.org/10.1088/1361-665X/ac093d  

   Nasser, Jalal ; Groo, Lori Anne ; Sodano, Henry ( June 2021 , Smart materials and structures)

   The mechanical properties of fiber reinforced polymer matrix composites are known to gradually deteriorate as fatigue damage accumulates under cyclic loading conditions. While the steady degradation in elastic stiffness throughout fatigue life is a well-established and studied concept, it remains difficult to continuously monitor such structural changes during the service life of many dynamic engineering systems where composite materials are subjected to random and unexpected loading conditions. Recently, laser induced graphene (LIG) has been demonstrated to be a reliable, in-situ strain sensing and damage detection component in fiberglass composites under both quasi-static and dynamic loading conditions. This work investigates themore »potential of exploiting the piezoresistive properties of LIG interlayered fiberglass composites in order to formulate cumulative damage parameters and predict both damage progression and fatigue life using artificial neural networks (ANNs) and conventional phenomenological models. The LIG interlayered fiberglass composites are subjected to tension–tension fatigue loading, while changes in their elastic stiffness and electrical resistance are monitored through passive measurements. Damage parameters that are defined according to changes in electrical resistance are found to be capable of accurately describing damage progression in LIG interlayered fiberglass composites throughout fatigue life, as they display similar trends to those based on changes in elastic stiffness. These damage parameters are then exploited for predicting the fatigue life and future damage state of fiberglass composites using both trained ANNs and phenomenological degradation and accumulation models in both specimen-to-specimen and cycle-to-cycle schemes. When used in a specimen-to-specimen scheme, the predictions of a two-layer Bayesian regularized ANN with 40 neurons in each layer are found to be at least 60% more accurate than those of phenomenological degradation models, displaying R2 values greater than 0.98 and root mean square error (RMSE) values smaller than 10−3. A two-layer Bayesian regularized ANN with 25 neurons in each layer is also found to yield accurate predictions when used in a cycle-to-cycle scheme, displaying R2 values greater than 0.99 and RMSE values smaller than 2 × 10−4 once more than 30% of the initial measurements are used as inputs. The final results confirm that piezoresistive LIG interlayers are a promising tool for achieving accurate and continuous fatigue life predictions in multifunctional composite structures, specifically when coupled with machine learning algorithms such as ANNs.« less
5. Artificial Neural Networks and Phenomenological Degradation Models for Fatigue Damage Tracking and Life Prediction in Laser Induced Graphene Interlayered Fiberglass Composites

   Nasser, J. ( June 2021 , Smart materials and structures)

   The mechanical properties of fiber reinforced polymer matrix composites are known to gradually deteriorate as fatigue damage accumulates under cyclic loading conditions. While the steady degradation in elastic stiffness throughout fatigue life is a well-established and studied concept, it remains difficult to continuously monitor such structural changes during the service life of many dynamic engineering systems where composite materials are subjected to random and unexpected loading conditions. Recently, laser induced graphene (LIG) has been demonstrated to be a reliable, in-situ strain sensing and damage detection component in fiberglass composites under both quasi-static and dynamic loading conditions. This work investigates themore »potential of exploiting the piezoresistive properties of LIG interlayered fiberglass composites in order to formulate cumulative damage parameters and predict both damage progression and fatigue life using artificial neural networks (ANNs) and conventional phenomenological models. The LIG interlayered fiberglass composites are subjected to tension–tension fatigue loading, while changes in their elastic stiffness and electrical resistance are monitored through passive measurements. Damage parameters that are defined according to changes in electrical resistance are found to be capable of accurately describing damage progression in LIG interlayered fiberglass composites throughout fatigue life, as they display similar trends to those based on changes in elastic stiffness. These damage parameters are then exploited for predicting the fatigue life and future damage state of fiberglass composites using both trained ANNs and phenomenological degradation and accumulation models in both specimen-to-specimen and cycle-to-cycle schemes. When used in a specimen-to-specimen scheme, the predictions of a two-layer Bayesian regularized ANN with 40 neurons in each layer are found to be at least 60% more accurate than those of phenomenological degradation models, displaying R2 values greater than 0.98 and root mean square error (RMSE) values smaller than 10−3. A two-layer Bayesian regularized ANN with 25 neurons in each layer is also found to yield accurate predictions when used in a cycle-to-cycle scheme, displaying R2 values greater than 0.99 and RMSE values smaller than 2 × 10−4 once more than 30% of the initial measurements are used as inputs. The final results confirm that piezoresistive LIG interlayers are a promising tool for achieving accurate and continuous fatigue life predictions in multifunctional composite structures, specifically when coupled with machine learning algorithms such as ANNs.« less