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

Ewa Dostatni, Filip Osiński, Dariusz Mikołajewski, Alžbeta Sapietová and Izabela Rojek ()
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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

Sustainability, 2024, vol. 16, issue 19, 1-20

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.

Keywords: 3D printing; additive manufacturing; particle matter emission; sustainable development of new technologies; neural networks; environmental impact (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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