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Physically feasible decomposition of Engino® toy models: A graph-theoretic approach

Published online by Cambridge University Press:  04 March 2018


E. N. ANTONIOU
Affiliation:
Department of Information Technology, Alexander Technological Educational Institute of Thessaloniki, 57400, Thessaloniki, Greece email: antoniou@it.teithe.gr
A. ARAÚJO
Affiliation:
CMUC, Department of Mathematics, University of Coimbra, Coimbra, Portugal email: alma@mat.uc.pt
M. D. BUSTAMANTE
Affiliation:
Institute for Discovery, School of Mathematics and Statistics, University College Dublin, Belfield, Dublin 4, Ireland email: miguel.bustamante@ucd.ie
A. GIBALI
Affiliation:
Department of Mathematics, ORT Braude College, P.O. Box 78, Karmiel 2161002, Israel email: avivg@braude.ac.il

Abstract

During the 125th European Study Group with Industry held in Limassol, Cyprus, 5–9 December 2016, one of the participating companies, Engino.net Ltd, posed a very interesting challenge to the members of the study group. Engino.net Ltd is a Cypriot company, founded in 2004, that produces a series of toy sets – the Engino® toy sets – consisting of a number of building blocks, which can be assembled by pupils to compose toy models. Depending on the contents of a particular toy set, the company has developed a number of models that can be built utilizing the blocks present in the set; however, the production of a step-by-step assembly manual for each model could only be done manually. The goal of the challenge posed by the company was to implement a procedure to automatically generate the assembly instructions for a given toy. In the present paper, we propose a graph-theoretic approach to model the problem and provide a series of results to solve it by employing modified versions of well-established algorithms in graph theory. An algorithmic procedure to obtain a hierarchical, physically feasible decomposition of a given toy model, from which a series of step-by-step assembly instructions can be recovered, is proposed.


Type
Papers
Copyright
Copyright © Cambridge University Press 2018 

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Footnotes

†MDB acknowledges support from Science Foundation Ireland under research Grant number 12/IP/1491.


References

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