Abstract
Proposed method allows for early detection of mistakes in designs of mechanical constructions. It is based on a numerical classification of a symbolic representation of construction’s features against a set of defined antipatterns (known, incorrect, repeatable data patterns). We present an approach to identify antipatterns described using a symbolic language KXML and a method of intelligent quality assessment enabling calculation of the similarity of the tested element with the antipattern data set. Additionally, we highlight selected properties of numerical models directly supporting analysis of the structure of mechanical constructions.
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References
Duer, S.: Diagnostic system with an artificial neural network in diagnostics of an analogue technical object. Neural Comput. Appl. 19(1), 55–60 (2010)
Duer, S.: Diagnostic system for the diagnosis of a reparable technical object, with the use of an artificial neural network of RBF type. Neural Comput. Appl. 19(5), 691–700 (2010)
Duer, S., Duer, R.: Diagnostic system with an artificial neural network which determines a diagnostic information for the servicing of a reparable technical object. Neural Comput. Appl. 19(5), 755–766 (2010)
Duer, S.: Artificial neural network in the control process of object’s states basis for organization of a servicing system of a technical objects. Neural Comput. Appl. 21(1), 153–160 (2012)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biol. Cybern. 43(1), 59–69 (1982)
Lippmann, R.P., Gold, B., Malpass, M.L.: A comparison of Hamming and Hopfield neural nets for pattern classification. Massachusetts Institute of Technology, Lincoln Laboratory (1987)
Pearl, J.: Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann Publishers, Burlington (1988)
Weld, D.S., de Kleer, J.: Readings in Qualitative Reasoning About Physical Systems. Morgan Kaufmann Publishers, Burlington (1990)
Koenig, A.: Patterns and antipatterns. J. Object-Oriented Program. 08, 46–48 (1995)
Lung-Wen, T.: Mechanism Design: Enumeration of kinematic Structures According to Function. CRC Press, Boca Raton (2001)
Knosala, R.: Applications of artificial intelligence methods in production engineering. WNT, Warsaw (2002). (in Polish)
Vazirani, V.V.: Approximation algorithms. WNT, Warsaw (2005). (in Polish)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2009)
Zhou, Y., Cheng, H., Xu Yu, J.: Graph clustering based on structural/attribute similarities. J. Proc. VLDB Endow. 2(1), 718–729 (2009)
Farabet, C., Couprie, C., Najman, L., LeCun, Y.: Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1915–1929 (2013)
Fei-wei, Q., Lu-ye, L., Shu-ming, G., Xiao-ling, Y., Xiang, C.: A deep learning approach to the classification of 3D CAD models. J. Zhejiang Univ. Sci. 15(2), 91–106 (2014)
Tuchołka, A., Majewski, M., Kacalak, W.: Object-oriented, symbolic notation for design features, relations and structures. Mach. Eng. 1(20), 112–120 (2015)
Kacalak, W., Majewski, M., Tuchołka, A.: Intelligent assessment of structure correctness using antipatterns. In: The Proceedings of the International Conference on Computational Science and Computational Intelligence CSCI’2015, Las Vegas, pp. 559–564. IEEE Xplore Digital Library (2015)
Kacalak, W., Majewski, M., Tuchołka, A.: A method of object-oriented symbolical description and evaluation of machine elements using antipatterns. J. Mach. Eng. 16(4), 46–69 (2016)
Sabour, S., Frost, N., Hinton, G.E.: Dynamic routing between capsules. Computer Vision and Pattern Recognition. arXiv:1710.09829 (2017)
Tuchołka, A., Majewski, M., Kacalak, W., Budniak, Z.: A method for intelligent quality assessment of a gearbox using antipatterns and convolutional neural networks. In: Silhavy, R. (ed.) CSOC 2018. Advances in Intelligent Systems and Computing, vol. 764, pp. 57–68. Springer, Cham. (2018)
Tuchołka, A., Majewski, M., Kacalak, W., Budniak, Z.: Comparison of numerical models used for automated analysis of mechanical structures. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) CoMeSySo 2018. Advances in Intelligent Systems and Computing, vol. 859, pp. 341–352. Springer, Cham. (2019)
Kacalak, W., Majewski, M., Budniak, Z.: Worm gear drives with adjustable backlash. J. Mech. Robot. 8(1), 014504 (2015)
Kacalak, W., Majewski, M., Budniak, Z.: Innovative design of non-backlash worm gear drives. Arch. Civil Mech. Eng. 18(3), 983–999 (2018)
Majewski, M., Kacalak, W.: Smart control of lifting devices using patterns and antipatterns. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds.) CSOC 2017. Advances in Intelligent Systems and Computing, vol. 573, pp. 486–493. Springer, Cham (2017)
Kacalak, W., Budniak, Z., Majewski, M.: Computer aided analysis of the mobile crane handling system using computational intelligence methods. In: Silhavy, R., Silhavy, P., Prokopova, Z. (eds.) CoMeSySo 2017. Advances in Intelligent Systems and Computing, vol. 662, pp. 250–261. Springer, Cham (2018)
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Tuchołka, A., Majewski, M., Kacalak, W., Budniak, Z. (2020). A Method for Applying Antipatterns and Neural Networks to Automate Detection of Errors in Designs of Mechanical Constructions. In: Majewski, M., Kacalak, W. (eds) Innovations Induced by Research in Technical Systems. IIRTS 2019. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-030-37566-9_12
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