Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F44555601%3A13420%2F22%3A43897384" target="_blank" >RIV/44555601:13420/22:43897384 - isvavai.cz</a>

Result on the web

<a href="https://www.mdpi.com/2073-8994/14/5/1013" target="_blank" >https://www.mdpi.com/2073-8994/14/5/1013</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3390/sym14051013" target="_blank" >10.3390/sym14051013</a>

Result language

angličtina

Original language name

Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors

Original language description

In this paper, an assembly sequence planning system, based on artificial neural networks, is developed. The problem of artificial neural network itself is largely related to symmetry at every stage of its creation. A new modeling scheme, known as artificial neural networks, takes into account selected DFA (Design for Assembly) rating factors, which allow the evaluation of assembly sequences, what are the input data to the network learning and then estimate the assembly time. The input to the assembly neural network procedure is the sequences for assembling the parts, extended by the assemblys connection graph that represents the parts and relations between these parts. The operation of a neural network is to predict the assembly time based on the training dataset and indicate it as an output value. The network inputs are data based on selected DFA factors influencing the assembly time. The proposed neural network model outperforms the available assembly sequence planning model in predicting the optimum assembly time for the mechanical parts. In the neural networks, the BFGS (the Broyden Fletcher Goldfarb Shanno algorithm), steepest descent and gradient scaling algorithms are used. The network efficiency was checked from a set of 20,000 test networks with randomly selected parameters: activation functions (linear, logistic, tanh, exponential and sine), the number of hidden neurons, percentage set of training and test dataset. The novelty of the article is therefore the use of parts of the DFA methodology and the neural network to estimate assembly time, under specific production conditions. This approach allows, according to the authors, to estimate which mechanical assembly sequence is the most advantageous, because the simulation results suggest that the neural predictor can be used as a predictor for an assembly sequence planning system.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

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OECD FORD branch

20301 - Mechanical engineering

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Symmetry

ISSN

2073-8994

e-ISSN

2073-8994

Volume of the periodical

14

Issue of the periodical within the volume

5

Country of publishing house

CH - SWITZERLAND

Number of pages

13

Pages from-to

1-13

UT code for WoS article

000804262400001

EID of the result in the Scopus database

2-s2.0-85130739759