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Dimensional Error Minimization through Parameter Optimization for 3D Printed Nylon Aramid Composites Using SWARA-CoCoSo and Machine Learning Algorithms

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Abstract

FDM parts are susceptible to low-dimensional accuracy due to the involvement of numerous process parameters. It is highly essential to identify the significant influencing parameter and optimal parameter setting for reducing dimensional inaccuracies. The main objective of this paper is to investigate the influence of six FDM parameters such as layer thickness, extrusion temperature, bed temperature, print speed, raster angle and part orientation over the dimensional deviations in a cuboid sample with hole at centre through L18 mixed fractional factorial design. The prepared specimens are evaluated for geometric deviations and the errors are analyzed using SWARA-CoCoSo and machine learning algorithms. The optimal parameter combination for reduced dimensional error is predicted as A2B3C3D2E3F3 (0.14 mm layer thickness, 235 °C print temperature, 90 °C bed temperature, 40 mm/s print speed, 45° raster angle and upright positioned printing). Layer thickness is found to be highly significant than any other process parameter with maximum contribution of 64.49% and subsequently followed by print speed with 13.55%.The final appraisal score of SWARA-CoCoSo technique has been trained and tested using machine learning algorithms. Adaboost algorithm outperformed all other regression algorithms with maximum R2 value of 0.999 and Decision tree algorithm ranked top in case of classification algorithms with maximum accuracy of 94.4%.The predictions of SWARA-CoCoSo and machine learning algorithms are in good agreement in identifying the optimal parameter combination and highly influencing parameter. Surface morphology of samples carried out through FESEM ensures the presence of voids, pores and irregular layers deposited at higher layer thickness values.

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Abbreviations

FDM:

Fused deposition modeling

SWARA:

Step-wise weight assessment ratio analysis

CoCoSo:

Combined compromise solution

ABS:

Acronitrile butadiene styrene

PLA:

Poly lactic acid

PET-G:

Polyethylene terephthalate glycol-enhanced

HIPS:

High impact polystyrene

PC:

Polycarbonate

ANOVA:

Analysis of variance

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Authors and Affiliations

  1. Department of Aeronautical Engineering, Nehru Institute of Technology, Coimbatore, India

    N. Mohammed Raffic

  2. Department of Mechanical Engineering, Chendhuran College of Engineering and Technology, Pudukkottai, India

    K. Ganesh Babu

  3. Department of Mechanical Engineering, Jazan University, Jazan, Kingdom of Saudi Arabia

    Rajasekaran Saminathan & Haitham Hadidi

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  4. Haitham Hadidi
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Mohammed Raffic, N., Ganesh Babu, K., Saminathan, R. et al. Dimensional Error Minimization through Parameter Optimization for 3D Printed Nylon Aramid Composites Using SWARA-CoCoSo and Machine Learning Algorithms. J. of Materi Eng and Perform 32, 11326–11346 (2023). https://doi.org/10.1007/s11665-023-08608-8

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