Skill Transfer for Surface Finishing Tasks Based on Estimation of Key Parameters

Yitaek Kim; Christoffer Sloth; Aljaz Kramberger

doi:10.1109/CASE49997.2022.9926698

Skill Transfer for Surface Finishing Tasks Based on Estimation of Key Parameters

Yitaek Kim^*, Christoffer Sloth, Aljaz Kramberger

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

Abstract

This paper presents an approach for transferring surface finishing behaviors to new surfaces while preserving the quality of the process. The idea is to let a human demonstrate the desired grinding behavior on a planar surface and subsequently generate an equivalent grinding behavior on new surface geometry. The transfer of the process quality is accomplished by imitating the material removal rate of a human. This is achieved with an adaptive control that relies on the online estimation of the material removal rate, which depends on the contact area, normal force, tool speed, and tool wear. The proposed approach is verified in simulation and experimentally validated on the grinding of planar and curved surfaces.

Original language	English
Title of host publication	2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)
Publisher	IEEE
Publication date	2022
Pages	2148-2153
ISBN (Electronic)	9781665490429
DOIs	https://doi.org/10.1109/CASE49997.2022.9926698
Publication status	Published - 2022
Event	18th IEEE International Conference on Automation Science and Engineering, CASE 2022 - Mexico City, Mexico Duration: 20. Aug 2022 → 24. Aug 2022

Conference

Conference	18th IEEE International Conference on Automation Science and Engineering, CASE 2022
Country/Territory	Mexico
City	Mexico City
Period	20/08/2022 → 24/08/2022

Series	IEEE International Conference on Automation Science and Engineering
Volume	2022-August
ISSN	2161-8070

Documents & Links

10.1109/CASE49997.2022.9926698

1 Ph.D. thesis

Fast Setup of Robotic Material Processing with Quality Guarantees using Set Invariance
Kim, Y., 23. Apr 2024, Syddansk Universitet. Det Tekniske Fakultet. 110 p.
Research output: Thesis › Ph.D. thesis

Cite this

@inproceedings{d4fa53550b3243fe9a63e1554c7004e6,

title = "Skill Transfer for Surface Finishing Tasks Based on Estimation of Key Parameters",

abstract = "This paper presents an approach for transferring surface finishing behaviors to new surfaces while preserving the quality of the process. The idea is to let a human demonstrate the desired grinding behavior on a planar surface and subsequently generate an equivalent grinding behavior on new surface geometry. The transfer of the process quality is accomplished by imitating the material removal rate of a human. This is achieved with an adaptive control that relies on the online estimation of the material removal rate, which depends on the contact area, normal force, tool speed, and tool wear. The proposed approach is verified in simulation and experimentally validated on the grinding of planar and curved surfaces.",

author = "Yitaek Kim and Christoffer Sloth and Aljaz Kramberger",

note = "Funding Information: *This work was supported by MADE FAST. Yitaek Kim, Christoffer Sloth, Aljaz Kramberger are with The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Denmark Publisher Copyright: {\textcopyright} 2022 IEEE.; 18th IEEE International Conference on Automation Science and Engineering, CASE 2022 ; Conference date: 20-08-2022 Through 24-08-2022",

year = "2022",

doi = "10.1109/CASE49997.2022.9926698",

language = "English",

series = "IEEE International Conference on Automation Science and Engineering",

pages = "2148--2153",

booktitle = "2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)",

publisher = "IEEE",

address = "United States",

}

Kim, Y , Sloth, C & Kramberger, A 2022, Skill Transfer for Surface Finishing Tasks Based on Estimation of Key Parameters. in 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE, IEEE International Conference on Automation Science and Engineering, vol. 2022-August, pp. 2148-2153, 18th IEEE International Conference on Automation Science and Engineering, CASE 2022, Mexico City, Mexico, 20/08/2022. https://doi.org/10.1109/CASE49997.2022.9926698

Skill Transfer for Surface Finishing Tasks Based on Estimation of Key Parameters. / Kim, Yitaek ; Sloth, Christoffer ; Kramberger, Aljaz.
2022 IEEE 18th International Conference on Automation Science and Engineering (CASE). IEEE, 2022. p. 2148-2153 (IEEE International Conference on Automation Science and Engineering, Vol. 2022-August).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Skill Transfer for Surface Finishing Tasks Based on Estimation of Key Parameters

AU - Kim, Yitaek

AU - Sloth, Christoffer

AU - Kramberger, Aljaz

N1 - Funding Information: *This work was supported by MADE FAST. Yitaek Kim, Christoffer Sloth, Aljaz Kramberger are with The Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Denmark Publisher Copyright: © 2022 IEEE.

PY - 2022

Y1 - 2022

N2 - This paper presents an approach for transferring surface finishing behaviors to new surfaces while preserving the quality of the process. The idea is to let a human demonstrate the desired grinding behavior on a planar surface and subsequently generate an equivalent grinding behavior on new surface geometry. The transfer of the process quality is accomplished by imitating the material removal rate of a human. This is achieved with an adaptive control that relies on the online estimation of the material removal rate, which depends on the contact area, normal force, tool speed, and tool wear. The proposed approach is verified in simulation and experimentally validated on the grinding of planar and curved surfaces.

AB - This paper presents an approach for transferring surface finishing behaviors to new surfaces while preserving the quality of the process. The idea is to let a human demonstrate the desired grinding behavior on a planar surface and subsequently generate an equivalent grinding behavior on new surface geometry. The transfer of the process quality is accomplished by imitating the material removal rate of a human. This is achieved with an adaptive control that relies on the online estimation of the material removal rate, which depends on the contact area, normal force, tool speed, and tool wear. The proposed approach is verified in simulation and experimentally validated on the grinding of planar and curved surfaces.

U2 - 10.1109/CASE49997.2022.9926698

DO - 10.1109/CASE49997.2022.9926698

M3 - Article in proceedings

AN - SCOPUS:85141676110

T3 - IEEE International Conference on Automation Science and Engineering

SP - 2148

EP - 2153

BT - 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE)

PB - IEEE

T2 - 18th IEEE International Conference on Automation Science and Engineering, CASE 2022

Y2 - 20 August 2022 through 24 August 2022

ER -

Skill Transfer for Surface Finishing Tasks Based on Estimation of Key Parameters

Abstract

Conference

Documents & Links

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Related research output

Fast Setup of Robotic Material Processing with Quality Guarantees using Set Invariance

Cite this