Smart Industrial Internet of Things Framework for Composites Manufacturing.

Composite materials are increasingly important in making high-performance products. However, contemporary composites manufacturing processes still encounter significant challenges that range from inherent material stochasticity to manufacturing process variabilities. This paper proposes a novel smart Industrial Internet of Things framework, which is also referred to as an Artificial Intelligence of Things (AIoT) framework for composites manufacturing. This framework improves production performance through real-time process monitoring and AI-based forecasting. It comprises three main components: (i) an array of temperature, heat flux, dielectric, and flow sensors for data acquisition from production machines and products being made, (ii) an IoT-based platform for instantaneous sensor data integration and visualisation, and (iii) an AI-based model for production process forecasting. Via these components, the framework performs real-time production process monitoring, visualisation, and prediction of future process states. This paper also presents a proof-of-concept implementation of the framework and a real-world composites manufacturing case study that showcases its benefits.

Extracting and characterizing novel cellulose fibers from Chamaerops humilis rachis for textiles' sustainable and cleaner production as reinforcement for potential applications.

New cellulose (CL) fibers are derived from Chamaerops humilis (Ch) rachis. They play an essential role in various industries to produce environmentally friendly products as an alternative to enhancing and strengthening lightweight composites, such as dashboards automotive. Distinctive properties of Ch fibers (ChFs) were determined by extracting fibers from dwarf palm plant branches using anaerobic analysis. This search comprehensively studies morphological, physical, mechanical, and thermal characteristics and water absorption testing. The fiber diameter was 241.23 ± 34.77 µm, while the obtained linear density and density were 13.71 ± 0.57 Tex and 0.801 ± 0.05 g/cm3, respectively. The moisture content was 8.5 %, and the moisture regain was 9.29 %. Scanning electron microscopy images showed the fibers and smooth and rough surfaces. The thermogravimetric analysis demonstrated the maximum degradation of 352 °C, thermal stability of 243 °C, and the kinetic activation energy reached (79.78 kJ/mol). X-ray diffraction proves the availability of CL, with a crystallinity index = 68.38 % and crystal size = 2.92 nm. Fourier transform infrared succeeded in detecting functional groups and chemical compounds of fibers. The fibers exhibited a tensile stress of 110.85 ± 77.08 MPa, an elongation at a break rate of 2.29 ± 1.27 %, and Young's modulus of 6.05 ± 3.9 GPa. The maximum likelihood method (2P-Weibull distribution) was employed to examine the distribution of mechanical properties of fibers. According to the results above, new ChFs are an excellent reinforcement for elaborating fiber-reinforced biocomposites.

Numerical Study of Thin-Walled Polymer Composite Part Quality When Manufactured Using Vacuum Infusion with Various External Pressure Controls.

The article presents the results of modeling various modes of vacuum infusion molding of thin-walled polymer-composite structures of arbitrary geometry. The small thickness of the manufactured structures and the fixation of their back surface on the rigid surface of the mold made it possible to significantly simplify the process model, which takes into account the propagation of a thermosetting resin with changing rheology in a compressible porous preform of complex 3D geometry, as well as changes in boundary conditions at the injection and vacuum ports during the post-infusion molding stage. In the four modes of vacuum-infusion molding studied at the post-infusion stage, the start time, duration and magnitude of additional pressure on the open surface of the preform and in its vacuum port, as well as the state of the injection gates, were controlled (open-closed). The target parameters of the processes were the magnitude and uniformity of the distribution of the fiber volume fraction, wall thickness, filling of the preform with resin and the duration of the process. A comparative analysis of the results obtained made it possible to identify the most promising process modes and determine ways to eliminate undesirable situations that worsen the quality of manufactured composite structures. The abilities of the developed simulation tool, demonstrated by its application to the molding process of a thin-walled aircraft structure, allow one to reasonably select a process control strategy to obtain the best achievable quality objectives.

Review of Approaches to Minimise the Cost of Simulation-Based Optimisation for Liquid Composite Moulding Processes.

The utilisation of numerical process simulation has greatly facilitated the challenging task of liquid composite moulding (LCM) process optimisation, providing ease of solution evaluation at a significantly reduced cost compared to complete reliance on physical prototyping. However, due to the process complexity, such process simulation is still considerably expensive at present. In this paper, cost-saving approaches to minimising the computational cost of simulation-based optimisation for LCM processes are compiled and discussed. Their specific applicability, efficacy, and suitability for various optimisation/moulding scenarios are extensively explored in detail. The comprehensive analysation and assimilation of their operation alongside applicability for the problem domain of interest are accomplished in this paper to further complement and contribute to future simulation-based optimisation capabilities for composite moulding processes. The importance of balancing the cost-accuracy trade-off is also repeatedly emphasised, allowing for substantial cost reductions while ensuring a desirable level of optimization reliability.

Application of KNN and ANN Metamodeling for RTM Filling Process Prediction.

Process simulation is frequently adopted to facilitate the optimization of the resin transfer molding process. However, it is computationally costly to simulate the multi-physical, multi-scale process, making it infeasible for applications involving huge datasets. In this study, the application of K-nearest neighbors and artificial neural network metamodels is proposed to build predictive surrogate models capable of relating the mold-filling process input-output correlations to assist mold designing. The input features considered are the resin injection location and resin viscosity. The corresponding output features investigated are the number of vents required and the resultant maximum injection pressure. Upon training, both investigated metamodels demonstrated desirable prediction accuracies, with a low prediction error range of 5.0% to 15.7% for KNN metamodels and 6.7% to 17.5% for ANN metamodels. The good prediction results convincingly indicate that metamodeling is a promising option for composite molding applications, with encouraging prospects for data-intensive applications such as process digital twinning.

Magnetically Cured Macroradical Epoxy as Antimicrobial Coating.

The radical-bearing epoxy monomer could be the ideal embodiment of multifunctionality in epoxy-based materials. This study demonstrates the potential of macroradical epoxies as surface coating materials. A diepoxide monomer derivatized with a stable nitroxide radical is polymerized with a diamine hardener under the influence of a magnetic field. The magnetically oriented and stable radicals in the polymer backbone render the coatings antimicrobial. The unconventional use of magnets during polymerization proved crucial in correlating the structure-property relationships with antimicrobial performance inferred from oscillatory rheological technique, polarized macro-attenuated total reflectance - infrared (macro-ATR-IR) spectroscopy and X-ray photoelectron spectroscopy (XPS). The magnetic thermal curing influenced the surface morphology, resulting in a synergy of the coating's radical nature with microbiostatic performance assessed using the Kirby-Bauer test and liquid chromatography - mass spectroscopy (LC-MS). Further, the magnetic curing of blends with a traditional epoxy monomer demonstrates that radical alignment is more critical than radical density in imparting biocidal behavior. This study shows how the systematic use of magnets during polymerization could pave for probing more significant insights into the mechanism of antimicrobial action in radical-bearing polymers.

Evaluation of the Failure Mechanism in Polyamide Nanofibre Veil Toughened Hybrid Carbon/Glass Fibre Composites.

The interface of hybrid carbon/E-glass fibres composite is interlayered with Xantu.layr® polyamide 6,6 nanofibre veil to localise cracking to promote a gradual failure. The pseudo-ductile response of these novel stacking sequences examined under quasi-static three-point bending show a change to the failure mechanism. The change in failure mechanism due to the interfacial toughening is examined via SEM micrographs. The incorporation of veil toughening led to a change in the dominant failure mechanism, resulting in fibre yielding by localised kinking and reduced instances of buckling failure. In alternated carbon and glass fibre samples with glass fibre undertaking compression, a pseudo-ductile response with veil interlayering was observed. The localisation of the fibre failure, due to the inclusion of the veil, resulted in kink band formations which were found to be predictable in previous micro buckling models. The localisation of failure by the veil interlayer resulted in a pseudo-ductile response increasing the strain before failure by 24% compared with control samples.