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Stiffness and mass optimization of parallel kinematic machine
KTH, School of Industrial Engineering and Management (ITM), Machine Design (Dept.). Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin 300350, China.
2018 (English)In: Mechanism and machine theory, ISSN 0094-114X, E-ISSN 1873-3999, Vol. 120, p. 73-88Article in journal (Refereed) Published
Abstract [en]

It has long been a challenge to carry out the optimal design of parallel kinematic machine (PKM) simultaneously considering stiffness and mass performances. This paper proposes the stiffness and mass optimization of PKM by settling performance indices, constraint conditions based on parameter uncertainty and cooperative equilibrium among performances. Firstly, instantaneous energy-based stiffness indices and mass in motion are defined as objectives. Instead of computationally expensive numerical analysis, analytical mapping models between objectives and parameters are investigated to improve optimization efficiency. Then, considering the effects of parameter uncertainty resulted from manufacturing errors during construction, constraint conditions are formulated by probabilistic method. Based on particle swarm optimization (PSO), a multi-objective optimization is implemented. A group of solutions are obtained to flag as Pareto frontier that reflects the competitive features between stiffness and mass performances. A cooperative equilibrium searching method is proposed to find out the final solution. Finally, this optimization approach is exemplified and validated by a five degree-of-freedom (DoF) PKM. Although its mass increases 17.17%, the stiffness is nearly 3 times better than before optimization.

Place, publisher, year, edition, pages
Elsevier Ltd , 2018. Vol. 120, p. 73-88
Keywords [en]
Analytical mapping model, Cooperative equilibrium point, Multi-objective optimization, Parallel kinematic machine (PKM), Pareto frontier, Probabilistic constraints, Degrees of freedom (mechanics), Flexible manufacturing systems, Kinematics, Mapping, Optimal systems, Particle swarm optimization (PSO), Stiffness, Analytical mapping, Cooperative equilibrium, Parallel kinematic machines, Pareto frontiers, Multiobjective optimization
National Category
Mechanical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-216796DOI: 10.1016/j.mechmachtheory.2017.09.014Scopus ID: 2-s2.0-85030149509OAI: oai:DiVA.org:kth-216796DiVA, id: diva2:1156859
Note

Export Date: 24 October 2017; Article; CODEN: MHMTA; Correspondence Address: Lian, B.; Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin UniversityChina; email: lianbinbin@tju.edu.cn; Funding details: 51475321, NSFC, National Natural Science Foundation of China; Funding details: 51675366, NSFC, National Natural Science Foundation of China; Funding text: This research work was supported by the National Natural Science Foundation of China (NSFC) under Grant Nos. 51475321 and 51675366, Tianjin Research Program of Application Foundation and Advanced Technology under Grant Nos. 15JCZDJC38900 and 16JCYBJC19300, and International Postdoctoral Exchange Fellowship Program 2017 by the Office of China Postdoctoral Council. QC 20171114

Available from: 2017-11-14 Created: 2017-11-14 Last updated: 2017-11-14Bibliographically approved

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