Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance
The aim of this paper is to develop an approach to increase the accuracy of industrial robots for machining processes. During machining tasks, process forces displace the end effector of the robot. A simulation of the various process influences is therefore necessary to ensure stable machining durin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-04-01
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| Series: | Journal of Manufacturing and Materials Processing |
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| Online Access: | https://www.mdpi.com/2504-4494/9/4/126 |
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| _version_ | 1850143741013131264 |
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| author | Minh Trinh Michael Königs Lukas Gründel Marcel Beier Oliver Petrovic Christian Brecher |
| author_facet | Minh Trinh Michael Königs Lukas Gründel Marcel Beier Oliver Petrovic Christian Brecher |
| author_sort | Minh Trinh |
| collection | DOAJ |
| description | The aim of this paper is to develop an approach to increase the accuracy of industrial robots for machining processes. During machining tasks, process forces displace the end effector of the robot. A simulation of the various process influences is therefore necessary to ensure stable machining during production planning in optimizing the process parameters. Realistic simulations require precise dynamics and stiffness models of the robot. Regarding the dynamics, the frictional component is highly complex and difficult to model. Therefore, this paper follows a grey-box approach to combine the advantages of the state-of-the-art Lund–Grenoble model (white-box) with those of a data-driven one (black-box) in the first part. The resulting grey-box LuGre model proves to be superior to the white- and black-box models. In the second part, a model-based simulation planning assistance tool is developed, which makes use of the grey-box LuGre model. The simulation assistance provides the manufacturing planner with process knowledge using the identified robot and cutting force models. Furthermore, it provides optimization methods such as a switching point analysis. Finally, the assistance tool gives predictions about the machining result and a process evaluation. The third part of the paper shows the evaluation of the simulation assistance on a real machining process and workpiece, showing an increase in accuracy using the tool. |
| format | Article |
| id | doaj-art-f044dc14a08b41bc8b3a891ee93063d7 |
| institution | OA Journals |
| issn | 2504-4494 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Manufacturing and Materials Processing |
| spelling | doaj-art-f044dc14a08b41bc8b3a891ee93063d72025-08-20T02:28:36ZengMDPI AGJournal of Manufacturing and Materials Processing2504-44942025-04-019412610.3390/jmmp9040126Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning AssistanceMinh Trinh0Michael Königs1Lukas Gründel2Marcel Beier3Oliver Petrovic4Christian Brecher5Laboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, GermanyResearch Association for Programming Languages for Production Facilities, 52062 Aachen, GermanyLaboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, GermanyResearch Association for Programming Languages for Production Facilities, 52062 Aachen, GermanyLaboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, GermanyLaboratory for Machine Tools and Production Engineering, RWTH Aachen University, 52074 Aachen, GermanyThe aim of this paper is to develop an approach to increase the accuracy of industrial robots for machining processes. During machining tasks, process forces displace the end effector of the robot. A simulation of the various process influences is therefore necessary to ensure stable machining during production planning in optimizing the process parameters. Realistic simulations require precise dynamics and stiffness models of the robot. Regarding the dynamics, the frictional component is highly complex and difficult to model. Therefore, this paper follows a grey-box approach to combine the advantages of the state-of-the-art Lund–Grenoble model (white-box) with those of a data-driven one (black-box) in the first part. The resulting grey-box LuGre model proves to be superior to the white- and black-box models. In the second part, a model-based simulation planning assistance tool is developed, which makes use of the grey-box LuGre model. The simulation assistance provides the manufacturing planner with process knowledge using the identified robot and cutting force models. Furthermore, it provides optimization methods such as a switching point analysis. Finally, the assistance tool gives predictions about the machining result and a process evaluation. The third part of the paper shows the evaluation of the simulation assistance on a real machining process and workpiece, showing an increase in accuracy using the tool.https://www.mdpi.com/2504-4494/9/4/126robot machiningcomputer-integrated manufacturingmodel-based compensation |
| spellingShingle | Minh Trinh Michael Königs Lukas Gründel Marcel Beier Oliver Petrovic Christian Brecher Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance Journal of Manufacturing and Materials Processing robot machining computer-integrated manufacturing model-based compensation |
| title | Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance |
| title_full | Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance |
| title_fullStr | Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance |
| title_full_unstemmed | Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance |
| title_short | Accuracy Optimization of Robotic Machining Using Grey-Box Modeling and Simulation Planning Assistance |
| title_sort | accuracy optimization of robotic machining using grey box modeling and simulation planning assistance |
| topic | robot machining computer-integrated manufacturing model-based compensation |
| url | https://www.mdpi.com/2504-4494/9/4/126 |
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