Machine learning based track height prediction for complex tool paths in direct metal deposition

Knüttel, Daniel and Baraldo, Stefano and Valente, Anna and Bleicher, Friedrich and Wegener, Konrad and Carpanzano, Emanuele (2022) Machine learning based track height prediction for complex tool paths in direct metal deposition. CIRP Annals 2022 - Manufacturing Technology.

Text 2022_Knüttel_Machine learning based track height prediction for complex toolpaths in DMD.pdf - Published Version Restricted to Registered users only Download (855kB)

Abstract

The tooling industry persistently demands for advanced techniques to boost the tools performances over their lifecycle. Direct Metal Deposition (DMD) presents key opportunities in the tool refurbishment. However, the typical tool paths via DMD consist of alternated smooth segments and sharp corners. Here, the fluctuation of energy density and powder quantities often cause critical geometrical deviations to the tool restored sections. This work presents a novel machine learning based prediction approach that characterizes paths using features associated to process parameters and performed geometry. The benefits of the approach have been validated on toolpaths, which typically characterize a tool refurbishment process.

Item Type:	Scientific journal article, Newspaper article or Magazine article
Subjects:	Engineering Engineering > Production & manufacturing engineering Engineering > Production & manufacturing engineering > Manufacturing systems engineering Engineering > Production & manufacturing engineering > Manufacturing systems engineering > Production processes
Department/unit:	Dipartimento tecnologie innovative > Istituto sistemi e tecnologie per la produzione sostenibile
Depositing User:	Anneke Katharina Orlandini
Date Deposited:	16 Mar 2023 08:07
Last Modified:	16 Mar 2023 08:10
URI:	http://repository.supsi.ch/id/eprint/13795

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