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Experimental Study of the Wear Performance of Fused Deposition Modeling Printed Polycarbonate-Acrylonitrile Butadiene Styrene Parts Using Definitive Screening Design and Machine Learning-Genetic Algorithm

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Abstract

The purpose of this study is to investigate the influence of fused deposition modeling (FDM) process parameters on the tribological behavior of PC-ABS built prototypes. Wear resistance of FDM manufactured parts in relation to the variation of FDM process parameters was analyzed and studied using definitive screening design and analysis of variance technique. The worn surfaces of some fabricated samples were also analyzed and investigated in this study by scanning electron microscope. Genetic algorithm was applied to optimize the wear resistance and to identify the exact effect of the input parameters. The results obtained from this study would have an impact on the FDM product performance in various industrial applications. This study demonstrates how the manufacturing parameters can be understood in FDM additive manufacturing technology. By implementing a similar approach, the optimum setting of process parameters, which gives the best tribological properties, can be determined whenever a new material is used for the FDM process.

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Authors and Affiliations

  1. Aerostructures Innovation Research (AIR) Hub, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Omar Ahmed Mohamed

  2. Department of Mechanical and Product Design Engineering, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Syed Hasan Masood

  3. Department of Health Science and Biostatistics, Swinburne University of Technology, Hawthorn, VIC, 3122, Australia

    Jahar Lal Bhowmik

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  1. Omar Ahmed Mohamed
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Mohamed, O.A., Masood, S.H. & Bhowmik, J.L. Experimental Study of the Wear Performance of Fused Deposition Modeling Printed Polycarbonate-Acrylonitrile Butadiene Styrene Parts Using Definitive Screening Design and Machine Learning-Genetic Algorithm. J. of Materi Eng and Perform 31, 2967–2977 (2022). https://doi.org/10.1007/s11665-021-06408-6

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