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Machine Learning Approach to Predict Feature Dimensions for Fused Deposition Modelling

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Abstract

Additive manufacturing (AM) offers an efficient way of building complicated geometry in a shorter and cost-effective manner. Though process parameters could be optimised to decrease dimensional deviation of fused deposition modelling (FDM) manufactured components, getting acceptable dimensional accuracy from FDM remains a challenge compared to other AM processes. In this study, a geometric benchmark part was designed based on the NIST (National Institute of Standards and Technology) which comprises different geometrical features, and it was fabricated by varying process parameters on an FDM machine. Dimensions of these features on a fabricated benchmark component were determined using the coordinate measuring machine, and the machine learning (ML) approach was employed to predict the dimensions and deviation. ML models built (linear regression, LASSO, Ridge, random forest, XGBoost) based on the measured dimensions were able to predict the geometrical dimensions (length, width, height, diameter, thin walls/ slots, angle) and dimensional deviation without fabricating the components.

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Acknowledgements

I, Lalitha R, express my sincere gratitude to CFR, Anna University, Chennai, for financial support through Anna Centenary Research Fellowship (ACRF). I, Prithvirajan R, express my sincere gratitude to the Council of Scientific and Industrial Research (CSIR), HRDG, India, for financial support through the CSIR-SRF direct fellowship (File no:09/468/0538/2019 EMR –I).

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Authors and Affiliations

  1. Department of Manufacturing Engineering, College of Engineering Guindy, Anna University, Chennai, India

    Lalitha Radhakrishnan, Prithvirajan Rajendran, Rupalin Biswal, Atul Gir Goswami & Arumaikkannu Ganesan

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  1. Lalitha Radhakrishnan
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  2. Prithvirajan Rajendran
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  3. Rupalin Biswal
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  5. Arumaikkannu Ganesan
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Correspondence to Lalitha Radhakrishnan.

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Radhakrishnan, L., Rajendran, P., Biswal, R. et al. Machine Learning Approach to Predict Feature Dimensions for Fused Deposition Modelling. Trans Indian Inst Met 76, 315–321 (2023). https://doi.org/10.1007/s12666-022-02671-w

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  • DOI: https://doi.org/10.1007/s12666-022-02671-w

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