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The International Journal of Advanced Manufacturing Technology

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17.11.2021 | ORIGINAL ARTICLE

Multi-objective optimization of binder jet additive manufacturing of Co-Cr-Mo using machine learning

verfasst von: Recep Onler, Ahmet Selim Koca, Baris Kirim, Emrecan Soylemez

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 1-2/2022

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Abstract

Binder jet additive manufacturing is a viable method that can be used with a variety of materials, such as metals and ceramics, and offer higher speed and reduced cost for high-volume production compared to powder bed fusion printing. However, the true potential of binder jet additive manufacturing has yet to be realized due to a significant knowledge gap in the material-process-property relationships. This study presents a robust approach to identifying ideal process parameters for binder jetting of Co-Cr-Mo alloy to enable accurate and reproducible manufacturing of defect-free products with high green density. An experimental investigation of the effect of process parameters on output quality was performed using the design of experiments, followed by the analysis of variance. Further characterization between process parameters and process outputs was revealed using machine learning techniques. The relationship between process parameters and density, dimensions and surface quality of green parts was established using artificial neural networks; qualitative features of green parts were classified by employing the weighted k-nearest neighbors algorithm. Furthermore, a genetic algorithm-based multi-objective optimization was employed to determine optimum process parameters, which predicted the part quality with greater than 90% accuracy. Collectively, the proposed optimization method is capable of identifying favorable process conditions for binder jetting of defect-free Co-Cr-Mo parts, with high dimensional accuracy, surface quality, and green density, and can be adapted to determine unique process settings for different material classes.

Vorheriger Artikel Comparative LCA of automotive gear hobbing processes with flood lubrication and MQL

Nächster Artikel Effects of tool wear on the microstructure evolution and mechanical properties of friction stir welded TA5 alloy

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Titel: Multi-objective optimization of binder jet additive manufacturing of Co-Cr-Mo using machine learning
verfasst von: Recep Onler
Ahmet Selim Koca
Baris Kirim
Emrecan Soylemez
Publikationsdatum: 17.11.2021
Verlag: Springer London
Erschienen in: The International Journal of Advanced Manufacturing Technology / Ausgabe 1-2/2022
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI: https://doi.org/10.1007/s00170-021-08183-z

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