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Neural Networks for Prediction of 3D Printing Parameters for Reducing Particulate Matter Emissions and Enhancing Sustainability

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  • Ewa Dostatni

    (Faculty of Mechanical Engineering, Poznan University of Technology, 5 M. Skłodowska-Curie Square, 60-965 Poznan, Poland)

  • Filip Osiński

    (Faculty of Mechanical Engineering, Poznan University of Technology, 5 M. Skłodowska-Curie Square, 60-965 Poznan, Poland)

  • Dariusz Mikołajewski

    (Faculty of Computer Science, Kazimierz Wielki University, 30 M. Chodkiewicza Street, 85-064 Bydgoszcz, Poland)

  • Alžbeta Sapietová

    (Faculty of Mechanical Engineering, University of Žilina, 1 M. Univerzitna, 010-26 Žilina, Slovakia)

  • Izabela Rojek

    (Faculty of Computer Science, Kazimierz Wielki University, 30 M. Chodkiewicza Street, 85-064 Bydgoszcz, Poland)

Abstract

This study focuses on the application of neural networks to optimize 3D printing parameters in order to reduce particulate matter (PM) emissions and enhance sustainability. This research identifies key parameters, such as head temperature, bed temperature, print speed, nozzle diameter, and cooling, that significantly impact particle matter emissions. Quantitative analysis reveals that higher head temperatures (225 °C), faster print speeds (50 mm/s), and larger nozzle diameters (0.8 mm) result in elevated PM emissions, while lower settings (head temperature at 190 °C, print speed at 30 mm/s, nozzle diameter of 0.4 mm) help minimize these emissions. Using multilayer perceptron (MLP) neural networks, predictive models with an accuracy of up to 95.6% were developed, allowing for a precise optimization of 3D printing processes. The MLP 7-19-6 model showed a strong correlation (0.956) between input parameters and emissions, offering a robust tool for reducing the environmental footprint of additive manufacturing. By optimizing 3D printing settings, this study contributes to more sustainable practices by lowering harmful emissions. These findings are crucial for advancing sustainable development goals by providing actionable strategies for minimizing health risks and promoting eco-friendly manufacturing processes. Ultimately, this research supports the transition to greener technologies in the field of additive manufacturing.

Suggested Citation

  • Ewa Dostatni & Filip Osiński & Dariusz Mikołajewski & Alžbeta Sapietová & Izabela Rojek, 2024. "Neural Networks for Prediction of 3D Printing Parameters for Reducing Particulate Matter Emissions and Enhancing Sustainability," Sustainability, MDPI, vol. 16(19), pages 1-20, October.
  • Handle: RePEc:gam:jsusta:v:16:y:2024:i:19:p:8616-:d:1492274
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    1. Dariusz Krajewski & Mariusz Oleksy & Rafał Oliwa & Katarzyna Bulanda & Kamil Czech & Damian Mazur & Grzegorz Masłowski, 2022. "Methods for Enhancing the Electrical Properties of Epoxy Matrix Composites," Energies, MDPI, vol. 15(13), pages 1-18, June.
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