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Learning Approach to Cycle-Time-Minimization of Wood Milling Using Adaptive Force Control

Sörnmo, Olof LU ; Olofsson, Björn LU ; Robertsson, Anders LU and Johansson, Rolf LU (2016) In Journal of Manufacturing Science and Engineering 138(1).
Abstract
A majority of the machining processes in the industry of today is performed using position-controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. By instead controlling the process forces, the feed rate, and consequently the material removal rate, can be maximized. In turn, this leads to decreased cycle times and cost savings. Furthermore, path planning with respect to time-minimization for milling processes, especially in non-isotropic materials, is not straightforward.



This paper presents a model-based adaptive force controller that achieves optimal feed rates, in combination with a learning algorithm to obtain the optimal machining path, in terms of... (More)
A majority of the machining processes in the industry of today is performed using position-controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. By instead controlling the process forces, the feed rate, and consequently the material removal rate, can be maximized. In turn, this leads to decreased cycle times and cost savings. Furthermore, path planning with respect to time-minimization for milling processes, especially in non-isotropic materials, is not straightforward.



This paper presents a model-based adaptive force controller that achieves optimal feed rates, in combination with a learning algorithm to obtain the optimal machining path, in terms of minimizing the milling duration. The proposed solution is evaluated in both simulation and experiments, where an industrial robot is used to perform rough-cut wood-milling. Cycle-time reductions of 14% using force control compared to position control were achieved, and on average an additional 28% cycle-time reduction with the proposed learning algorithm. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Journal of Manufacturing Science and Engineering
volume
138
issue
1
publisher
American Society Of Mechanical Engineers (ASME)
external identifiers
  • wos:000367715900013
  • scopus:84941247911
ISSN
1528-8935
DOI
10.1115/1.4030751
project
SMErobotics
language
English
LU publication?
yes
id
24603a31-5c17-4d91-a427-5d0f7fba2e48 (old id 8228072)
date added to LUP
2015-11-20 13:04:52
date last changed
2017-06-18 18:45:13
@article{24603a31-5c17-4d91-a427-5d0f7fba2e48,
  abstract     = {A majority of the machining processes in the industry of today is performed using position-controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. By instead controlling the process forces, the feed rate, and consequently the material removal rate, can be maximized. In turn, this leads to decreased cycle times and cost savings. Furthermore, path planning with respect to time-minimization for milling processes, especially in non-isotropic materials, is not straightforward.<br/><br>
<br/><br>
This paper presents a model-based adaptive force controller that achieves optimal feed rates, in combination with a learning algorithm to obtain the optimal machining path, in terms of minimizing the milling duration. The proposed solution is evaluated in both simulation and experiments, where an industrial robot is used to perform rough-cut wood-milling. Cycle-time reductions of 14% using force control compared to position control were achieved, and on average an additional 28% cycle-time reduction with the proposed learning algorithm.},
  articleno    = {011013},
  author       = {Sörnmo, Olof and Olofsson, Björn and Robertsson, Anders and Johansson, Rolf},
  issn         = {1528-8935},
  language     = {eng},
  number       = {1},
  publisher    = {American Society Of Mechanical Engineers (ASME)},
  series       = {Journal of Manufacturing Science and Engineering},
  title        = {Learning Approach to Cycle-Time-Minimization of Wood Milling Using Adaptive Force Control},
  url          = {http://dx.doi.org/10.1115/1.4030751},
  volume       = {138},
  year         = {2016},
}