Robust assessments of printability limits in complex geometries represents a key point for enabling the adoption and the spreading in industry of innovative Additive Manufacturing (AM) technologies. The paper presents a novel solution to assess a printability map in metal AM able to capture the probability of producing a defect-free complex geometries embedding all the printing constraints and the geometrical specifications. The approach involves logistic regression as tool to assess the likelihood of obtaining defect-free complex geometries, depending on the process and the material at hands. Besides proposing a new methodology which can be adopted for any printed geometry, the paper investigates the printing capability of a new emerging AM technologies based on extrusion of metal feedstock, such as the Bound Metal Deposition from Desktop Metal, for defect-free fabrication of an emerging lattice-based shape, known as Schoen gyroid. The proposed method is based on combining quality data labeled by experts with failure mode analysis of the 3D printing process within a logistic regression model. The approach provides a final probabilistic map, in the design parameters space of the gyroids, where the likelihood of defectiveness is available at each location of the design space. The proposed methodology and the presented results support the development of robust defect- and waste-free part design approaches for AM.

A new solution for assessing the printability of 17-4 PH gyroids produced via extrusion-based metal AM

Parenti, Paolo;Puccio, Dario;Colosimo, Bianca Maria;Semeraro, Quirico
2022-01-01

Abstract

Robust assessments of printability limits in complex geometries represents a key point for enabling the adoption and the spreading in industry of innovative Additive Manufacturing (AM) technologies. The paper presents a novel solution to assess a printability map in metal AM able to capture the probability of producing a defect-free complex geometries embedding all the printing constraints and the geometrical specifications. The approach involves logistic regression as tool to assess the likelihood of obtaining defect-free complex geometries, depending on the process and the material at hands. Besides proposing a new methodology which can be adopted for any printed geometry, the paper investigates the printing capability of a new emerging AM technologies based on extrusion of metal feedstock, such as the Bound Metal Deposition from Desktop Metal, for defect-free fabrication of an emerging lattice-based shape, known as Schoen gyroid. The proposed method is based on combining quality data labeled by experts with failure mode analysis of the 3D printing process within a logistic regression model. The approach provides a final probabilistic map, in the design parameters space of the gyroids, where the likelihood of defectiveness is available at each location of the design space. The proposed methodology and the presented results support the development of robust defect- and waste-free part design approaches for AM.
2022
Additive manufacturing, Gyroid, Lattice, Bound metal deposition, Extrusion-based AM, Regression
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1194213
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