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Quick assessment of melt flow index in hybrid bio-composite filaments for bio additive manufacturing

  • Roopsandeep Bammidi EMAIL logo , Hymavathi Madivada EMAIL logo and Sreeramulu Dowluru
Published/Copyright: October 27, 2025
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Abstract

The use of mechanical property-improving fillers has resulted in defect formation in 3D-printed structures. To the best of our knowledge, this study is the first to modify poly lactic acid (PLA) filaments in bulk by integrating 7–13 wt% holy basil and 0.1–0.4 wt% graphene additive. The results showed a 22–31 % increase in the melt flow index (MFI) of the composites. The filaments’ compositional changes were then submitted to rheological testing to assess the influence on filament structure. To solve common printing difficulties, such as mid-print model voids, we developed a strategy for optimizing extrusion settings using Cura slicing software. In addition, we created an efficient technique of MFI testing of 3D-printed filament systems that does not require the use of expensive laboratory equipment, allowing us to use additional filament property assessment methods. This highlights the need to tune the filament’s extrusion speed in order to improve accuracy and print quality for 3D printing additive prototyping.


Corresponding authors: Roopsandeep Bammidi, Department of Mechanical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, Andhra Pradesh, 534101, India; and Department of Mechanical Engineering, Aditya Institute of Technology and Management, Tekkali, Andhra Pradesh, 532201, India, E-mail: ; and Hymavathi Madivada, Department of Mechanical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, Andhra Pradesh, 534101, India, E-mail:

Acknowledgments

The authors would like to thank Sathish Kumar Adapa, Dr. Kelli Durgaprasad, Dr. Dubba Santhosh Kumar of Aditya Institute of Technology and Management, Tekkali, India for their valuable support and contributions to this research.

  1. Research ethics: This work does not contain any studies with human participants or animals performed by any of the authors.

  2. Informed consent: Informed consent was obtained from all individual participants included in the study.

  3. Author contributions: Roopsandeep Bammidi – Conceptualization, Methodology, Writing – Original Draft. Hymavathi Madivada – Writing – Review & Editing, Supervision. Sreeramulu Dowluru – Writing – Review & Editing, Co-Supervision.

  4. Use of Large Language Models, AI and Machine Learning Tools: No large language models, AI, or machine learning tools were used in the preparation of this manuscript.

  5. Conflict of interest: The authors declare no conflict of interest.

  6. Research funding: No funding.

  7. Data availability: The data supporting the findings of this study are available within the article only.

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Received: 2025-04-21
Accepted: 2025-09-17
Published Online: 2025-10-27

© 2025 Walter de Gruyter GmbH, Berlin/Boston

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