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2026 Kabasele ANN Binder Jetting Printer Settings 10.1007 s40962 026 01925 3

Jonathan Kabadjundi Kabasele, Kasongo Didier Nyembwe

2026enbinder jettingsand castingadditive manufacturingneural networksprocess parametersmould strength

Abstract

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This study employs artificial neural network (ANN) sensitivity analysis to rank the impact of key binder jetting parameters, namely AFS grain fineness, printhead speed, drop mass, and print resolution (DX), on the strength of 3D-printed sand moulds. Results indicate that AFS grain fineness accounts for more than 70% of the influence on mould strength, with the remaining parameters contributing 30%. Leveraging these findings, an efficient categorization was developed. By ranking parameters through cumulative scoring, this classification highlights the relative importance of each variable. The resulting classification offers foundries a strategic tool to optimize binder jetting processes, adaptable to different machines and parameters. This approach advances innovation in the foundry industry, aligns with Fourth Industrial Revolution (4IR) technologies, and supports the United Nations Sustainable Development Goal 9, promoting industry, innovation, and infrastructure development.

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Cite This Work

@article{87040eeb-ff67-4fff-8ae3-d2ebaa94f97f,
  title={2026 Kabasele ANN Binder Jetting Printer Settings 10.1007 s40962 026 01925 3},
  author={Jonathan Kabadjundi Kabasele and Kasongo Didier Nyembwe},
  year={2026},
  language={en}
}
TY  - JOUR
TI  - 2026 Kabasele ANN Binder Jetting Printer Settings 10.1007 s40962 026 01925 3
AU  - Jonathan Kabadjundi Kabasele
AU  - Kasongo Didier Nyembwe
PY  - 2026
LA  - en
ER  -

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