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Machine condition monitoring in FDM based on electret microphone, SVM, and neural networks

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Abstract

The fused deposition modeling (FDM) process, also known as 3D printing, deals with the manufacture of parts by adding layers of fused filament. Research on manufacturing process monitoring is on the rise, with an emphasis on investigating low-cost transducers as substitutes for the traditional, pricier options. The present study addresses a critical gap in the literature concerning the monitoring of the FDM process using acoustic signals from an electret microphone attached to the extruder. By employing an extensive signal processing and feature extraction analysis, including RMS values, ratio of power (ROP), and count statistics, this research uncovers distinguishable patterns in raw signals that relate to different machine conditions such as normal operation, extruder clogging, and filament shortages. Additionally, machine learning algorithms, specifically neural networks and support vector machine (SVM), are utilized to classify these machine conditions. Notably, signal filtering is found to significantly improve the classification models. The spectral analysis further contributes to characterizing the printing process, especially in identifying frequency values associated with defects. In conclusion, the methodology developed in this study holds promise for real-time monitoring systems, as it showcases high accuracy in classifying machine conditions and offers the potential to ensure quality and detect anomalies early in the printing process. Future research is encouraged to refine the methodology and explore its scalability across different FDM systems and materials.

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Funding

The authors would like to thank the Brazilian funding agency: the National Council for Scientific and Technological Development (CNPq), Grant # 306774/2021-6. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior–Brasil (CAPES), finance code 001.

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Authors and Affiliations

  1. Department of Electrical Engineering, Faculty of Engineering, Sao Paulo State University-UNESP, Bauru, Sao Paulo, Brazil

    Thiago Glissoi Lopes, Paulo Roberto Aguiar, Paulo Monteiro de Carvalho Monson & Reinaldo Götz de Oliveira Junior

  2. Department of Chemical, Materials and Industrial Production Engineering, University of Naples Federico II, Naples, Italy

    Doriana Marilena D’Addona

  3. Department of Electrical and Computer Engineering, São Carlos School of Engineering, University of São Paulo (USP), São Carlos, São Paulo, Brazil

    Pedro de Oliveira Conceição Júnior

  4. Biomedical Systems, São Paulo State Technological College, Bauru, São Paulo, Brazil

    Reinaldo Götz de Oliveira Junior

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  1. Thiago Glissoi Lopes
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  2. Paulo Roberto Aguiar
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  3. Paulo Monteiro de Carvalho Monson
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  4. Doriana Marilena D’Addona
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  5. Pedro de Oliveira Conceição Júnior
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Contributions

T. G. L.: conceptualization, methodology, printing test, software, and writing—original draft preparation; P. R. A.: conceptualization, methodology, software, data curation, writing—original draft preparation, and validation; P. M. C. M.: conceptualization, methodology, software, data curation, and writing—original draft preparation; D. M. D.: methodology and validation; P. O. C. Jr.: methodology and validation; R. G. O. Jr.: methodology and validation.

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Correspondence to Thiago Glissoi Lopes.

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Lopes, T.G., Aguiar, P.R., Monson, P.M.d. et al. Machine condition monitoring in FDM based on electret microphone, SVM, and neural networks. Int J Adv Manuf Technol 129, 1769–1786 (2023). https://doi.org/10.1007/s00170-023-12375-0

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