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Research Papers

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

[+] Author and Article Information
Olof Sörnmo

Department of Automatic Control,
LTH,
Lund University,
Lund SE-221 00, Sweden
e-mail: olof.sornmo@control.lth.se

Björn Olofsson, Rolf Johansson

Department of Automatic Control,
LTH,
Lund University,
Lund SE-221 00, Sweden

Anders Robertsson

Department of Automatic Control,
LTH,
Lund University,
Lund SE-221 00, Sweden

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received October 28, 2014; final manuscript received May 26, 2015; published online September 9, 2015. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 138(1), 011013 (Sep 09, 2015) (11 pages) Paper No: MANU-14-1557; doi: 10.1115/1.4030751 History: Received October 28, 2014

A majority of the machining processes in the industry of today are performed using position-controlled machine tools, where conservative feed rates have to be used in order to avoid excessive process forces. Instead of 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 nonisotropic 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.

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References

Figures

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Fig. 1

Block diagram of the proposed modeling approach, where h(fx,fy) = sign(fx)fx2+fy2

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Fig. 3

Example of three different milling types, with different coverages of the tool and cutting directions

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Fig. 2

Block diagram of the proposed force-control structure

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Fig. 8

Milling path for simulation 3, where the dashed lines represent the transitions between pockets. Pockets 1–3 are shown from right to left.

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Fig. 5

Force-controller performance simulation. The top panel shows |fd|, fx, fy, and fN. The second panel shows the actual and estimated κ and the third panel shows the actual and estimated β. The bottom panel displays the feed rate vr.

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Fig. 6

Force-controller performance during several milling experiment segments in oak

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Fig. 7

Milling path for simulation 1, where the dashed lines represent the transitions between training and/or pockets. The training phase is shown to the right, followed by pocket 1 and 2 from right to left.

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Fig. 4

Experimental setup for performing force-controlled milling with an ABB IRB140 robot

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Fig. 9

Milling path for simulation 5, where the dashed black lines represent the transitions between pockets. Pockets 1–3 are shown from right to left.

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Fig. 10

Milling path for experiment 5, where the dashed lines represent the transitions between pockets. Pockets 1–3 are shown from right to left.

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Fig. 11

The resulting workpiece after performing several milling experiments

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