There is a growing need for industrial robots to undertake high tolerance operations in line with Industry 4.0 (I4.0) demands and requirements. This requires robotic accuracy to be understood and quantified within appropriate bounds, relative to their application area. One such area is complex aerospace assembly where ro-botic accuracy is of particular importance if the full potential and benefits of I4.0 are to be realised in the sector. The standards BS EN ISO 9283:1998 and ISO/TR 13,309:1995 outline the requirements for calculating an in-dustrial robot’s accuracy. The standards highlight equipment capable of achieving this and they note that the robot base frame (RBF) must be determined as part of the process. The RBF is an exact position within the robot, and therefore it should be established with both accuracy and precision. However, a specific process or approach is not provided in either standard for the determination of a RBF. This has resulted in the use of bespoke methods by various researchers for the determination of their RBF using metrological equipment and associated software. These ad-hoc methods are not globally applied and the rationale and justification for their use remains un-published. Previous research that presented a process used to construct the RBF, resulted in a varying RBF origin position when repeated. The work presented in this paper provides the basis for a common approach to the determination of a RBF which integrates metrology hardware with a Design of Experiments (DOE) approach to select an appropriate measurement routine. The DOE approach investigates how different factors (e.g. robot axis used, number of point positions used, their positions, and repetitions of their occurrence) influence the repeatability in establishing the RBF, by measuring the positions of points that the robot attains using different levels for each factor. This study used a Universal Robot to develop and demonstrate the proposed method for RBF determination using the factors that were found to affect point repeatability. This new method was validated by comparing the outcome of the applied process to four methods that used random combinations of factors. The approach was found to increase the repeatability in establishing the RBF origin point by 93.4%, compared to a previous method that used arbitrarily chosen factors.
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McGarry, L. (Creator), Queen's University Belfast, Sept 2022
Machine learning methods to improve the accuracy of an articulated robot for a cyber-physical production systemAuthor: McGarry, L., Dec 2022
Student thesis: Doctoral Thesis › Doctor of PhilosophyFile