Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table
Deburring is recognized as an ideal technology for robotic automation. However, since the low stiffness of the robot can affect the deburring quality and the performance of an industrial robot is generally inhomogeneous over its workspace, a cell setup must be found that allows the robot to track th...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-09-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/17/8213 |
| _version_ | 1797521662141267968 |
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| author | Janez Gotlih Miran Brezocnik Timi Karner |
| author_facet | Janez Gotlih Miran Brezocnik Timi Karner |
| author_sort | Janez Gotlih |
| collection | DOAJ |
| description | Deburring is recognized as an ideal technology for robotic automation. However, since the low stiffness of the robot can affect the deburring quality and the performance of an industrial robot is generally inhomogeneous over its workspace, a cell setup must be found that allows the robot to track the toolpath with the desired performance. In this work, the problems of robotic deburring are addressed by integrating components commonly used in the machining industry. A rotary table is integrated with the robotic deburring cell to increase the effective reach of the robot and enable it to machine a large workpiece. A genetic algorithm (GA) is used to optimize the placement of the workpiece based on the stiffness of the robot, and a local minimizer is used to maximize the stiffness of the robot along the deburring toolpath. During cutting motions, small table rotations are allowed so that the robot maintains high stiffness, and during non-cutting motions, large table rotations are allowed to reposition the workpiece. The stiffness of the robot is modeled by an artificial neural network (ANN). The results confirm the need to optimize the cell setup, since many optimizers cannot track the toolpath, while for the successful optimizers, a performance imbalance occurs along the toolpath. |
| first_indexed | 2024-03-10T08:15:43Z |
| format | Article |
| id | doaj.art-806f4cbd6912428b96cc1acf7a5d214e |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T08:15:43Z |
| publishDate | 2021-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-806f4cbd6912428b96cc1acf7a5d214e2023-11-22T10:23:22ZengMDPI AGApplied Sciences2076-34172021-09-011117821310.3390/app11178213Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary TableJanez Gotlih0Miran Brezocnik1Timi Karner2Faculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, SloveniaFaculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, SloveniaFaculty of Mechanical Engineering, University of Maribor, Smetanova ulica 17, 2000 Maribor, SloveniaDeburring is recognized as an ideal technology for robotic automation. However, since the low stiffness of the robot can affect the deburring quality and the performance of an industrial robot is generally inhomogeneous over its workspace, a cell setup must be found that allows the robot to track the toolpath with the desired performance. In this work, the problems of robotic deburring are addressed by integrating components commonly used in the machining industry. A rotary table is integrated with the robotic deburring cell to increase the effective reach of the robot and enable it to machine a large workpiece. A genetic algorithm (GA) is used to optimize the placement of the workpiece based on the stiffness of the robot, and a local minimizer is used to maximize the stiffness of the robot along the deburring toolpath. During cutting motions, small table rotations are allowed so that the robot maintains high stiffness, and during non-cutting motions, large table rotations are allowed to reposition the workpiece. The stiffness of the robot is modeled by an artificial neural network (ANN). The results confirm the need to optimize the cell setup, since many optimizers cannot track the toolpath, while for the successful optimizers, a performance imbalance occurs along the toolpath.https://www.mdpi.com/2076-3417/11/17/8213deburringrobotstiffnessartificial neural networkgenetic algorithm |
| spellingShingle | Janez Gotlih Miran Brezocnik Timi Karner Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table Applied Sciences deburring robot stiffness artificial neural network genetic algorithm |
| title | Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table |
| title_full | Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table |
| title_fullStr | Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table |
| title_full_unstemmed | Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table |
| title_short | Stiffness-Based Cell Setup Optimization for Robotic Deburring with a Rotary Table |
| title_sort | stiffness based cell setup optimization for robotic deburring with a rotary table |
| topic | deburring robot stiffness artificial neural network genetic algorithm |
| url | https://www.mdpi.com/2076-3417/11/17/8213 |
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