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Patching process optimization in an agent-controlled timber mill

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Abstract

Repair and patching of wood defects is a costly process of inline production in timber industry. A large variety of plain as well as laminated wooden products demands for offline human interaction and skilled handcrafting in order to achieve the desired quality of the final products. The EU FP7 project Hol-I-Wood PR demonstrates the transformation of a traditional wood patching line for shuttering panels into a fully automated, flexible patching plant. The focus of this paper is set on the optimization of the different production steps of a patching robot, which comprises optimal patch placement, path planning and trajectory generation. Based on this, the processing time of each workpiece can be accurately estimated. These computations serve as an input for advanced panel scheduling, which assigns panels to one of several identical parallel patching lines in a throughput-optimal manner. In order to ensure high modularity of the components and scalability for various wood mills, an agent-based approach was chosen for the implementation of the automation system.

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Notes

  1. Approximately 1,500 of these patching tools are in use in Europe.

  2. For details refer to our partner company MiCROTEC, see http://www.microtec.eu/en.

  3. The open-source platform Java Agent DEvelopment framework (JADE) (Jade 2014) is used for development and execution of the PC. When using the JADE framework, the task of the SDA can be performed by JADE’s Directory Facilitator agent.

  4. Start and end node are already fixed.

  5. Since \(G\) is a complete graph, the feasible neighborhood does not depend on the current node \(i\) but only on the previously visited nodes, see (10).

  6. If \(G\) was not complete, it might happen that the feasible neighborhood \(\mathcal {N}_h^m =\{\}\) before the construction process is finished.

  7. The implication being that a subset \(\tilde{\lambda } \le \lambda \) of the arcs may be contained in both sets and thus remain the same.

  8. The rightmost node \(\psi ^{l2r}_n\) always remains the last node, so it is not part of any optimization problem.

  9. It is worth mentioning that the most suitable start solution for the LSRHA is the nearest-neighbor path. Usually, the nearest-neighbor path is extremely good except for a few nodes that are left behind.

  10. The data is provided by our partner saw mill Lip Bled, see http://en.lip-bled.si.

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Acknowledgments

The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 284573.

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Authors and Affiliations

  1. Automation and Control Institute, Vienna University of Technology, Gußhausstraße 27-29/E376, 1040, Vienna, Austria

    Matthias Wolfgang Hofmair, Martin Melik-Merkumians, Martin Böck, Munir Merdan, Georg Schitter & Andreas Kugi

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  1. Matthias Wolfgang Hofmair
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  2. Martin Melik-Merkumians
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  3. Martin Böck
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  4. Munir Merdan
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  5. Georg Schitter
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Correspondence to Matthias Wolfgang Hofmair or Martin Melik-Merkumians.

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Hofmair, M.W., Melik-Merkumians, M., Böck, M. et al. Patching process optimization in an agent-controlled timber mill. J Intell Manuf 28, 69–84 (2017). https://doi.org/10.1007/s10845-014-0962-z

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