Positioning Error Compensation of a Flexible Track Hybrid Robot for Aircraft Assembly Based on Response Surface Methodology and Experimental Study

With the development of aviation industry, more stringent demands are put forward for the performance and manufacturing level of aircraft. Moreover, the automation and precision of aircraft assembly determine the efficiency and quality of aircraft production. In order to improve the positioning precision of the flexible track hybrid robots which are applied to the flexible automatic assembly of aircraft, a precision compensation method based on response surface methodology was proposed in this paper. Firstly, the global positioning error model, optimized by characteristics of error data, was constructed to predict the positioning errors of the flexible track hybrid robot. Secondly, the predicted errors are utilized to realize the compensation of the target points at drilling workspace on nose and front fuselage assembly areas. Finally, a series of experiments of the flexible track hybrid robot with no-load and drilling scenarios are implemented to validate the proposed precision compensation method. The experiment of a hybrid robot for aircraft assembly shows that the mean value of the absolute positioning precision of the end-effector was promoted from 0.081 mm to 0.025 mm, maximum error reduced from 0.143 mm to 0.039 mm., respectively, which means that the position accuracy of the robot is increased by 69.1% and 72.7% for two experimental conditions.

Knowledge-enabled digital twin for smart designing of aircraft assembly line

Purpose The purpose of this paper is to provide a knowledge-enabled digital twin for smart design (KDT-SD) of aircraft assembly line (AAL) to enhance the AAL efficiency, performance and visibility. Modern AALs usually need to have capabilities such as digital-physical interaction and self-evaluation that brings significant challenges to traditional design method for AAL. The digital twin (DT) combining with reusable knowledge, as the key technologies in this framework, is introduced to promote the design process by configuring, understanding and evaluating design scheme. Design/methodology/approach The proposed KDT-SD framework is designed with the introduction of DT and knowledge. First, dynamic design knowledge library (DDK-Lib) is established which could support the various activities of DT in the entire design process. Then, the knowledge-driven digital AAL modeling method is proposed. At last, knowledge-based smart evaluation is used to understand and identify the design flaws, which could further improvement of the design scheme. Findings By means of the KDT-SD framework proposed, it is possible to apply DT to reduce the complexity and discover design flaws in AAL design. Moreover, the knowledge equips DT with the capacities of rapid modeling and smart evaluation that improve design efficiency and quality. Originality/value The proposed KDT-SD framework can provide efficient design of AAL and evaluate the design performance in advance so that the feasibility of design scheme can be improved as much as possible.