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Elasto-dynamicoptimization of a 5-DoF parallel kinematic machine considering parameteruncertainty
Tianjin University.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.).ORCID iD: 0000-0002-6747-8565
Tianjin University.
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.), Machine Design (Div.).ORCID iD: 0000-0001-5703-5923
2019 (English)In: IEEE/ASME transactions on mechatronics, ISSN 1083-4435, E-ISSN 1941-014X, no 1, p. 315-325Article in journal (Refereed) Published
Abstract [en]

Geometric errors, vibration, and elastic deformation are the main causes for inaccuracy of parallel kinematic machines (PKMs). Instead of tackling these inaccuracies after the prototype has been built, this paper proposes a design optimization method to minimize vibration and deformation considering the effects of geometric errors before constructing the PKM. In this paper, geometric errors are described as parameter uncertainty because they are unknown in design stage. A five degree-of-freedom (DoF) PKM is taken to exemplify this method. Elastodynamic model is first formulated by a step-by-step strategy. On this basis, dynamic performances, including natural frequency, elastic deformation, and maximum stress, are analyzed. These analytical results are verified by finite-element simulation and experiment. Then, the necessity of concerning parameter uncertainty in optimization is addressed. Next, parameter uncertainty is added to the formulation of objectives and constraints by Monte Carlo simulation and response surface method. Finally, elastodynamic optimization of the 5-DoF PKM is implemented to rebuild a prototype which is robust to geometric errors and has minimal vibration and deformation. The proposed method can also be applied to accuracy improvement of any machines in practical applications.

Place, publisher, year, edition, pages
IEEE, 2019. no 1, p. 315-325
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-249502DOI: 10.1109/tmech.2019.2891355ISI: 000458807900031Scopus ID: 2-s2.0-85062077621OAI: oai:DiVA.org:kth-249502DiVA, id: diva2:1304510
Note

QC 20190507

Available from: 2019-04-12 Created: 2019-04-12 Last updated: 2019-05-07Bibliographically approved

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Lian, BinbinFeng, Lei

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