Affiliation:
1. The University of Sheffield - AMRC
Abstract
<div class="section abstract"><div class="htmlview paragraph">Robotic arms are widely known to fall short in achieving the tolerances required when it comes to the metal machining industry, especially for the aerospace sector. Broadly speaking, two of the main reasons for that are a lack of stiffness and a lack of accuracy. Robotic arm manufacturers have responded to the lack of stiffness challenge by producing bigger robots, capable of holding high payloads (e.g., Fanuc M-2000iA/2300) or symmetric robots (e.g., ABB IRB6660). Previous research proved that depending on the application and the material being machined, lack of stiffness will still be an issue, even for structurally bigger robotic arms, due to their serial nature. The accuracy issue has been addressed to a certain extent by using secondary encoders on the robotic arm joints. The encoder enhanced robotic arm solutions tend to be expensive and prior knowledge proves that there are still limitations when it comes to achieved accuracy. The current work aims to provide a performance analysis of the path following capabilities of two robotic machining platforms, namely the Accurate Robotic Milling System (ARMS) and the MABI MAX-100-2.25P. Both platforms are equipped with secondary encoders (optical and inductive, respectively) and Siemens 840 D sl controllers and have been designed to be used in machining applications. The performance analysis will be demonstrated with a novel path that takes into consideration the BS EN ISO 9283:1998 standards for manipulating industrial robots while utilizing machining specific feed rates and feasible working volumes for both platforms. Furthermore, an accuracy study is performed for the 840 D sl controller Sinumerik Trace tool capabilities and verified by using a Leica Absolute AT960 laser tracker to assess its reliability for usage in accuracy analysis. This would remove the need to use expensive external metrology equipment for tracking path accuracy.</div></div>
Reference16 articles.
1. International Federation of Robotics
World Robotics 2023 Frankfurt IFR Press 2023
2. McGarry , L. ,
Butterfield , J. ,
Murphy , A. , and
Higgins , C.
Machine Learning Methods to Improve the Accuracy of Industrial Robots SAE Int. J. Adv. & Curr. Prac. in Mobility 5 2023 5 1900 1918 https://doi.org/10.4271/2023-01-1000
3. Vocetka , M. ,
Huňady , R. ,
Hagara , M. ,
Bobovský , Z.
et al.
Influence of the Approach Direction on the Repeatability of an Industrial Robot Applied Sciences 10 23 2020 8714 10.3390/jmmp4030079
4. Vocetka , M. ,
Huňady , R. ,
Hagara , M. ,
Bobovský , Z.
et al.
Influence of the Approach Direction on the Repeatability of an Industrial Robot Applied Sciences 10 23 2020 8714
5. MABI Robotic
2023 https://www.mabi-robotic.com/files/Produkt/MAX-100-225-P_Datasheet_en_V2.pdf