Stiffness optimization of 5-axis machine tool for improving surface roughness of 3D printed products View Full Text


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Article Info

DATE

2017-07

AUTHORS

Sukjin Ko, Donghun Lee

ABSTRACT

This paper deals with the design of a 3P2R structure based 5-axis machine tool used for finish cutting to improve the inherent poor surface roughness caused by the laminating based production method. A strong focus is placed on the constrained stiffness optimization. In the case of the finish cutting of Fused deposition modeling (FDM) workpieces produced with ABS, PLA, etc., the cutting forces should be significantly smaller than those of metallic materials generally observed in the metal cutting process. Thus, the main focus of this research is the reduced size and weight of the 5-axis machine tool for embedding into a small-sized FDM 3D printer by stiffness optimization while considering the allowable maximum displacement at the end-effector for the applied cutting forces. Thus, before finding the optimal stiffness of the 5-axis machine tool for the finish cutting process in FDM, the cutting forces in the finish cutting process of ABS and PLA are measured. The measured average cutting forces of ABS and PLA are 2.02 N and 3.50 N, respectively. In addition, it is confirmed that the surface roughness of workpieces produced by FDM process can be improved by finish cutting. A mathematical model of the spatial stiffness including both the structural stiffness derived using the Castigliano’s 2nd theorem and the actuator stiffness derived using the virtual work theorem, is then devised in this research. Genetic algorithm (GA) based optimization is then performed to minimize the difference between the displacement due to the applied force-moment and the maximum allowable displacement of 60 μm at the end-effector. After optimization, the displacements occurred by the cutting forces at the end-effector are considerably reduced compared with the initial design and are close to the target displacements. More... »

PAGES

3355-3369

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s12206-017-0625-z

DOI

http://dx.doi.org/10.1007/s12206-017-0625-z

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1090929549


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