Abstract
The aim of this paper is to describe the methods used to adapt the robotic system as well as the design, simulation, digitization, and verification of the robotic workplace for intelligent welding of small-scale production. Small-scale production in small and medium-sized enterprises is characterized by a high level of type variability of products. It was a requirement to design and verify a robotic positioning and welding workplace with a high degree of ability to automatically adapt to processing of various objects. This paper deals with the design and verification of robotic smart systems that contribute to variability of a robotic workplace for intelligent welding of small-scale production such as positioning and holding of the to-be-welded parts by two synchronized robotic manipulators, robotic welding, robotic picking systems using 3D scanners, 2D laser scanner measurement of gap geometry, and quick-change system of robotic grippers with a force-torque sensor. Before testing the robotic manipulation and robotic welding of products of various sizes and shapes, the design of the workplace was verified using its digital twin. The robotic workplace for intelligent welding of small-scale production also includes tools for digitization.
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References
Löfving M, Almström P, Jarebrant C et al (2018) Evaluation of flexible automation for small batch production. Procedia Manufacturing 25:177–184. https://doi.org/10.1016/j.promfg.2018.06.072
Kusuda Y (2013) Honda develops robotized FSW technology to weld steel and aluminum and applied it to a mass‐production vehicle. Indust Robot Int J 40(3):208–212. https://doi.org/10.1108/01439911311309889
Ilesanmi D, Moses O, Khumbulani M, Samuel N (2019) Application of the fourth industrial revolution for high volume production in the rail car industry. Mass Production Processes. IntechOpen. https://www.intechopen.com/chapters/68532. https://doi.org/10.5772/intechopen.88703
Liu YK, Zhang YM (2015) Supervised learning of human welder behaviors for intelligent robotic welding. IEEE Trans Autom Sci Eng 14(3):1532–1541
Halim NNA, Shariff SSR, Zahari SM (2020) Modelling an automobile assembly layout plant using probabilistic functions and discrete event simulation. Int Conf Ind Eng Oper Manag 5:2726–2737
Kampker A, Hollah A, Triebs J, Löffler B (2019) Modular body shop with process-and component-integrated jig features. ATZproduction worldwide 6(1):10–15
Tao F, Zhang H, Liu A, Nee AYC (2019) Digital twin in industry: state-of-the-art. IEEE Trans Industr Inf 15(4):2405–2415
Norberto Pires J, Loureiro A, Bölmsjo G (2006) Welding robots, technology, system issues and applications. Springer-Verlag, London
Spong MW, Hutchinson S, Vidyasagar M (2005) Robot modeling and control vol. 7–8, John wiley & Sons, INC., New York/Chichester/Weinheim/Brisbane/Singapore/Toronto, p 355
Kah P, Shrestha M, Hiltunen E, Martikainen J (2015) Robotic arc welding sensors and programming in industrial applications. Int J Mech and Mater Eng 10:13
Savu ID, Baccarini C, Bibas H, Almeida RM, Rozanski M (2016) Robot Welding. IAB-34–13, Minimum requirements for the education, training, examination and qualification, Consortium to implement Project E+ 2014–1-RO01-KA202–002913. https://www.scribd.com/document/415567483/Book-Vol-3O-OrbitalWelding-22Jun#. Accessed 4 July 2019
Bologna F, Tannous M, Romano D, Stefanini C (2022) Automatic welding imperfections detection in a smart factory via 2-D laser scanner. J Manuf Process 73:948–960
Cibicik A, Tingelstad L, Egeland O (2021) Laser scanning and parametrization of weld grooves with reflective surfaces. Sensors 21:4791. https://doi.org/10.3390/s21144791
Manorathna RP, Phairatt P, Ogun P, Widjanarko T, Chamberlain M, Justham L, Marimuthu S, Jackson MR (2014) Feature extraction and tracking of a weld joint for adaptive robotic welding, 2014 13th International Conference on Control, Automation, Robotics & Vision, Marina Bay Sands, Singapore, 10–12th December 2014 (ICARCV 2014)
Bhat AA, Mohan V (2018) Goal-directed reasoning and cooperation in robots in shared workspaces: an internal simulation based neural framework. Cogn Comput 2018(10):558–576. https://doi.org/10.1007/s12559-018-9553-1
Duchon F, Dekan M, Babinec A, Chovanec L, Vitko A (2014) Detection of welds in automated welding. Appl Mech Mater 611:519–528. https://doi.org/10.4028/www.scientific.net/AMM.611.519
Pfeifer N, Briese C (2007) Laser scanning — principles and applications. Vienna University of Technology, Institute of Photogrammetry and Remote Sensing, Austria
Ch Liu P, Jiang W Jiang (2020) Web-based digital twin modeling and remote control of cyber-physical production systems. Robot Comput Integr Manuf 64:1–16. https://doi.org/10.1016/j.rcim.2020.101956
R Ferro, H Sajjad REC (2021) Ordonez, Steps for data exchange between real environment and virtual simulation environment, ICCMS ’21, Melbourne, VIC, Australia. https://doi.org/10.1145/3474963.3474988
Ding D, Shen C, Pan Z, Cuiuri D, Li H, Larkin N, van Duin S (2016) Towards an automated robotic arc-welding-based additive manufacturing system from CAD to finished part. Comput Aided Des 73(2016):66–75. https://doi.org/10.1016/j.cad.2015.12.003
Funding
This work was supported by the Operational Program Integrated Infrastructure for the project: “Robotic workplace for intelligent welding of small-scale production (IZVAR),” code ITMS2014 + : 313012P386, co-financed by the European Regional Fund Development and under the DIH2, grant agreement ID: 824964, and under the Better Factory, grant agreement ID: 951813.
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Zuzana Kovarikova: design and development of the robotic workplace for intelligent welding of small-scale production; Frantisek Duchon: verification and validation of the robotic workplace; Marek Trebula: development of automatic optimization of the weld gap and generation of welding trajectory; Frantisek Nagy: testing of the robotic workplace; Martin Dekan: data extraction from 2D laser scanner measurements; Dusan Labat: design and simulation of robotic grippers; Andrej Babinec: simulation of the robotic workplace.
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video of simulation: https://youtu.be/kWdImhZLZ2c.
video of verification: https://youtu.be/uktqNrnMPKI.
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Kovarikova, Z., Duchon, F., Trebula, M. et al. Prototyping an intelligent robotic welding workplace by a cyber-physic tool. Int J Adv Manuf Technol 125, 4855–4882 (2023). https://doi.org/10.1007/s00170-023-10986-1
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DOI: https://doi.org/10.1007/s00170-023-10986-1