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Machine Learning-Based Mechanical Behavior Optimization of 3D Print Constructs Manufactured Via the FFF Process

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Abstract

Fused filament fabrication (FFF) is one of the fastest-growing additive manufacturing processes due to its low operational cost and the capability to rapidly construct prototypes with complex geometrical structures. Albeit the fact that FFF technology has been widely studied, the printing conditions that offer the optimum mechanical behavior exploiting numerical tools have not been systematically studied yet. The main goal of the study is to introduce machine learning-based models that predict the tensile strength of 3D printed parts and the development of an optimization tool, which infers the appropriate printing conditions according to the requirements of the user. The current paper deals with a computational and experimental study over the effect of specific process-related parameters and their impact on the mechanical behavior of an object manufactured via the FFF procedure. The data for the development of the predictive models and the numerical optimization module were acquired by manufacturing specimens with varying printing conditions. The results demonstrate that by adapting the suggested values on the printing parameters from the machine learning models and the optimization module, a remarkable enhancement on the mechanical properties of the printed parts can be achieved. Finally, the present work is the first to utilize machine learning-based regression models coupled with optimization techniques in order to improve the mechanical behavior of products manufactured via the FFF process.

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Acknowledgments

«This research has been co-financed by the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH – CREATE – INNOVATE (project code: T1EDK- 04928)».

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Authors and Affiliations

  1. Centre for Research and Technology Hellas – Information Technologies Institute (CERTH/ITI), Thessaloniki, Greece

    Paschalis Charalampous, Nikolaos Kladovasilakis, Ioannis Kostavelis & Dimitrios Tzovaras

  2. Digital Manufacturing and Materials Characterization Laboratory, School of Science and Technology, International Hellenic University, Thessaloniki, Greece

    Nikolaos Kladovasilakis, Konstantinos Tsongas & Dimitrios Tzetzis

Authors
  1. Paschalis Charalampous
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  2. Nikolaos Kladovasilakis
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  3. Ioannis Kostavelis
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  4. Konstantinos Tsongas
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  5. Dimitrios Tzetzis
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  6. Dimitrios Tzovaras
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Correspondence to Paschalis Charalampous.

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Charalampous, P., Kladovasilakis, N., Kostavelis, I. et al. Machine Learning-Based Mechanical Behavior Optimization of 3D Print Constructs Manufactured Via the FFF Process. J. of Materi Eng and Perform 31, 4697–4706 (2022). https://doi.org/10.1007/s11665-021-06535-0

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  • DOI: https://doi.org/10.1007/s11665-021-06535-0

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