Browsing by Author "Potgieter J-G"

Now showing 1 - 3 of 3
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    Hybrid deposition additive manufacturing: novel volume distribution, thermo-mechanical characterization, and image analysis
    (The Brazilian Society of Mechanical Sciences and Engineering, 25/08/2022) Harris M; Mohsin H; Potgieter J-G; Arif K; Anwar S; AlFaify A; Farooq MU
    The structural integrity of additive manufacturing structures is a pronounced challenge considering the voids and weak layer-to-layer adhesion. One of the potential ways is hybrid deposition manufacturing (HDM) that includes fused filament fabrication (FFF) with the conventional filling process, also known as “HDM composites". HDM is a potential technique for improving structural stability by replacing the thermoplastic void structure with a voidless epoxy. However, the literature lacks investigation of FFF/epoxy HDM-based composites regarding optimal volume distribution, effects of brittle and ductile FFF materials, and fractographic analysis. This research presents the effects of range of volume distributions (10–90%) between FFF and epoxy system for tensile, flexure, and compressive characterization. Volume distribution in tensile and flexure samples is achieved using printable wall thickness, slot width, and maximum width. For compression, the printable wall thickness, slot diameter, and external diameter are considered. Polylactic acid and acrylonitrile butadiene styrene are used to analyze the brittle and ductile FFF structures. The research reports novel application of image analysis during mechanical characterization using high-quality camera and fractographic analysis using scanning electron microscopy (SEM). The results present surprising high tensile strain (0.038 mm/mm) and compressive strength (64.5 MPa) for lower FDM-percentages (10%, 20%) that are explained using in situ image analysis, SEM, stress–strain simulations, and dynamic mechanical analysis (DMA). In this regard, the proposed work holds novelty to apply DMA for HDM. The optimal volume distributions of 70% and 80% alongside fractographic mechanisms for lower percentages (10%, 20%) can potentially contribute to structural applications and future material-based innovations for HDM.
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    Low-Cost CO Sensor Calibration Using One Dimensional Convolutional Neural Network
    (MDPI AG, 11/01/2023) Ali S; Alam F; Arif K; Potgieter J-G
    The advent of cost-effective sensors and the rise of the Internet of Things (IoT) presents the opportunity to monitor urban pollution at a high spatio-temporal resolution. However, these sensors suffer from poor accuracy that can be improved through calibration. In this paper, we propose to use One Dimensional Convolutional Neural Network (1DCNN) based calibration for low-cost carbon monoxide sensors and benchmark its performance against several Machine Learning (ML) based calibration techniques. We make use of three large data sets collected by research groups around the world from field-deployed low-cost sensors co-located with accurate reference sensors. Our investigation shows that 1DCNN performs consistently across all datasets. Gradient boosting regression, another ML technique that has not been widely explored for gas sensor calibration, also performs reasonably well. For all datasets, the introduction of temperature and relative humidity data improves the calibration accuracy. Cross-sensitivity to other pollutants can be exploited to improve the accuracy further. This suggests that low-cost sensors should be deployed as a suite or an array to measure covariate factors.
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    Partial Biodegradable Blend for Fused Filament Fabrication: In-Process Thermal and Post-Printing Moisture Resistance
    (MDPI AG, 9/04/2022) Harris M; Mohsin H; Naveed R; Potgieter J-G; Ishfaq K; Ray S; Guen M-JL; Archer R; Arif K
    Despite the extensive research, the moisture-based degradation of the 3D-printed polypropylene and polylactic acid blend is not yet reported. This research is a part of study reported on partial biodegradable blends proposed for large-scale additive manufacturing applications. However, the previous work does not provide information about the stability of the proposed blend system against moisture-based degradation. Therefore, this research presents a combination of excessive physical interlocking and minimum chemical grafting in a partial biodegradable blend to achieve stability against in-process thermal and moisture-based degradation. In this regard, a blend of polylactic acid and polypropylene compatibilized with polyethylene graft maleic anhydride is presented for fused filament fabrication. The research implements, for the first time, an ANOVA for combined thermal and moisture-based degradation. The results are explained using thermochemical and microscopic techniques. Scanning electron microscopy is used for analyzing the printed blend. Fourier transform infrared spectroscopy has allowed studying the intermolecular interactions due to the partial blending and degradation mechanism. Differential scanning calorimetry analyzes the blending (physical interlocking or chemical grafting) and thermochemical effects of the degradation mechanism. The thermogravimetric analysis further validates the physical interlocking and chemical grafting. The novel concept of partial blending with excessive interlocking reports high mechanical stability against moisture-based degradation.