Improvement of part strength prediction modelling by artificial neural networks for filament and pellet based additively manufactured parts

ABSTRACT

Nowadays, extrusion-based additive manufacturing techniques have been utilised from domestic to industrial end-product fabrications. However, parts fabricated through these techniques are facing poorer part strength issues due to its high nature of anisotropic fabrication strategies. Moreover, it may be improved if it can be predicted in the initial stage of fabrication through part strength modelling technique. In this direction, many researchers have attempted to model part strength estimation models that could be utilised for prediction purposes and hence controlled and improved the part strength. However, due to the anisotropic nature of 3D printed parts, models developed in the existing literature are showing more prediction errors, which can affect the final part strength. To overcome this issue, a new part strength modelling technique based on ANN technique has been defined in this study, which reduces the prediction error significantly. For the validation of the developed model, a comparative study has been performed by considering previously developed models and corresponding data available in the existing literature. Results obtained from the proposed ANN model show that prediction errors reduced significantly and it can be one of the possible alternatives to predict part strength of anisotropic behaviour of 3D printed parts.

KEYWORDS:

• Fused filament fabrication
• material extrusion
• 3D printing
• additive manufacturing
• neural network
• response surface methodology

Acknowledgments

This work was supported by the Science and Engineering Research Board (SERB) – DST under its Start-up Research Grant (SRG) scheme [Grant number: SRG/2019/000943].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are openly available in Mendeley Data at http://doi.org/10.17632/98zwbhdtk9.1, reference number [V1].

Additional information

Funding

This work was supported by the Science and Engineering Research Board [SRG/2019/000943].

Notes on contributors

Ashutosh Kumar Gupta

Ashutosh Kumar Gupta received the M.Tech. degree in manufacturing technology from National Institute of Technology Jalandhar, India, in 2020, and worked as junior research fellow on SERB-DST sponsored project in Maulana Azad National Institute of Technology Bhopal, India, in 2021. His current research is focused on material extrusion additive manufacturing and High temperature polymers.

Mohammad Taufik

Mohammad Taufik is an Assistant Professor in the Department of Mechanical Engineering of Maulana Azad National Institute of Technology Bhopal, Madhya Pradesh, India. He received his PhD in the field of additive manufacturing from the PDPM Indian Institute of Information Technology, Design and Manufacturing Jabalpur. His research work on additive manufacturing/3D printing had been published in reputed journals and conferences. He is also serving as a principal investigator (PI) of project titled “Development of a Pellet and Filament Form Integrated Multi-Material Co-Extruder System for Improved Additive Manufacturing Process” sponsored by SERB-DST, New Delhi, India under its Start-up Research Grant (SRG) scheme. Earlier, he has served as senior research fellow at PDPM IIITDM Jabalpur on SERB-DST sponsored research project. His interests include CAD/CAM, Additive Manufacturing/3D Printing, Rapid Prototyping & Tooling, Micro Fabrications in Manufacturing, Micro and Nano Finishing and General Mechanical Engineering Design.