An Approach for Predicting Stiffness of a 5-DOF Hybrid Robot for Friction Stir Welding. (September 2022)

• Record Type: Journal Article
• Authors: Yue, Wei; Liu, Haitao; Huang, Tian
• Is Part Of: Mechanism and machine theory. Volume 175(2022)
• Publication Date: 2022-09
• Subjects: Hybrid robot -- Equivalent model -- Stiffness prediction -- Biased estimation; Machine theory -- Periodicals; Machinery -- Periodicals; Machines -- Périodiques; Génie mécanique -- Périodiques; Machine theory; Machinery; Periodicals; 621.81
• DOI: 10.1016/j.mechmachtheory.2022.104941
• Languages: English
• ISSNs: 0094-114X

Abstract

Highlights: Stiffness prediction for a 5-DOF hybrid robot is presented using an equivalent system. Biased estimations are used to address the multicollinearity of the identification matrix. Experiments on a prototype machine illustrate the effectiveness of the approach. Abstract: This paper presents an approach for stiffness identification of a 5-DOF hybrid robot named TriMule for friction stir welding. The novelty of this approach is to visualize the realistic robotic system that considers the elastic deflections of all joints and links as a fictitious robotic system that only considers the elastic deflections of joints along their axes. Thus the number of parameters to be identified is reduced significantly. The stiffness identification is essentially implemented by the following four steps: (1) formulating the Cartesian stiffness model that maps the joint compliances of the fictitious system to the deflection twist of the end-effector, (2) parameterizing the joint compliances by a set of polynomial functions in terms of nominal actuated joint variables, (3) constructing the multiple linear regression equations by projecting the measured elastic deflections from the operational space onto the joint space, and (4) estimating the polynomial coefficients by robust biased estimation. The stiffness identification experiment and offline compensation experiment for the tool path deviations are carried out on a prototype machine to demonstrate the effectiveness of the proposed approach.