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

Summary, in English

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.

• Author: Olof Sörnmo; Björn Olofsson; Anders Robertsson; Rolf Johansson
• Department/s: Department of Automatic Control; ELLIIT: the Linköping-Lund initiative on IT and mobile communication
• Publishing year: 2016
• Language: English
• Publication/Series: Journal of Manufacturing Science and Engineering
• Volume: 138
• Issue: 1
• Full text: - 3 MB; Download statistics
• Links: http://dx.doi.org/10.1115/1.4030751
• Document type: Journal article
• Publisher: American Society Of Mechanical Engineers (ASME)
• Topic: Control Engineering
• Status: Published
• Project: LCCC; LU Robotics Laboratory; SMErobotics
• Research group: LCCC
• ISBN/ISSN/Other: ISSN: 1528-8935