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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 symboli...
Contexts in source publication
Context 1
... enable automatic detection of mechanical design errors through the similarity to antipatterns (Fig. 3) [14,15,17], the classification algorithms have to enable concrete definition of the antipattern set. Such data set should contain data samples focusing on demonstrating the incorrect nature of the design. The multi-dimensional nature of the mechanical design does not allow for direct application of data formatted in KXML format. Each ...
Context 2
... of node class structure. Such model, allows to generate network activation based on local feature values, that through convolutions and sampling layers support identification and classification of patterns found in the symbolic description of the mechanical design. We are highlighting the ConvNet designs, as among other tested models (Fig. 3.), it allows for the highest level of direct association of the performed calculations to the multi-dimensional structure of the symbolic description of the construction. Compared to algebraic models, Hopfield, Hamming, and probabilistic networks, algorithmic extensions introduced by convolutional neural networks increase the depth of ...
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