Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/55390
Title: An analysis of key process parameters for hybrid manufacturing by material extrusion and CNC machining
Authors: Paz, Rubén 
Santamarta, Javier
Monzón, Mario D. 
García Montagut, Carlos Joshua 
Pei, Eujin
UNESCO Clasification: 3313 Tecnología e ingeniería mecánicas
Keywords: Hybrid manufacturing
Material extrusion
Additive manufacturing
Subtractive manufacturing
Machining, et al
Issue Date: 2018
Journal: Bio-design and manufacturing 
Abstract: Hybrid Manufacturing combines the advantages of Additive Manufacturing (AM) and Subtractive Manufacturing on a single machine. Although previous research has provided a good background of the manufacturing parameters, the analysis is often carried out separately and there is a lack of combined knowledge. The purpose of this research is to examine the influence of the main manufacturing parameters involved in AM and subtractive processes using different variables. Particularly, the study is focused on the Material Extrusion process concerning the layer height, fill angle and fill density; as well as two factors related to machining, including stepover and pass direction. The parameters that were examined include the dimension, hardness, flatness, weight and roughness. A multifactorial Design of Experiments was proposed with a total of 64 samples. Next, a statistical analysis was carried out to assess the influence of the different groups on the response variables. Finally, a decision table presented and facilitated the selection of parameters depending on the desired objectives, leading to a framework that was applied to a case study for validation. This decision guide could enable designers and engineers to select the best strategy for a specific application, leading to a more efficient approach for manufacturing.
URI: http://hdl.handle.net/10553/55390
ISSN: 2096-5524
DOI: 10.1007/s42242-018-0023-0
Source: Bio-design and manufacturing [ISSN 2096-5524], v. 1 (4), p. 237-244
URL: https://api.elsevier.com/content/abstract/scopus_id/85062710473
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