Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors
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 select...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-05-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/14/5/1013 |
| _version_ | 1797495074272051200 |
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| author | Marcin Suszyński Katarzyna Peta Vít Černohlávek Martin Svoboda |
| author_facet | Marcin Suszyński Katarzyna Peta Vít Černohlávek Martin Svoboda |
| author_sort | Marcin Suszyński |
| collection | DOAJ |
| 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 assembly’s 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. |
| first_indexed | 2024-03-10T01:43:14Z |
| format | Article |
| id | doaj.art-5589728f818c4d5fbb56f86cb170ab73 |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-03-10T01:43:14Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-5589728f818c4d5fbb56f86cb170ab732023-11-23T13:19:59ZengMDPI AGSymmetry2073-89942022-05-01145101310.3390/sym14051013Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating FactorsMarcin Suszyński0Katarzyna Peta1Vít Černohlávek2Martin Svoboda3Institute of Mechanical Technology, Poznan University of Technology, 60-965 Poznan, PolandInstitute of Mechanical Technology, Poznan University of Technology, 60-965 Poznan, PolandFaculty of Mechanical Engineering, University of Jan Evangelista Purkyně in Ústí nad Labem, 400 96 Ústí nad Labem, Czech RepublicFaculty of Mechanical Engineering, University of Jan Evangelista Purkyně in Ústí nad Labem, 400 96 Ústí nad Labem, Czech RepublicIn 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 assembly’s 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.https://www.mdpi.com/2073-8994/14/5/1013design for assemblyartificial neural networksassembly |
| spellingShingle | Marcin Suszyński Katarzyna Peta Vít Černohlávek Martin Svoboda Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors Symmetry design for assembly artificial neural networks assembly |
| title | Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors |
| title_full | Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors |
| title_fullStr | Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors |
| title_full_unstemmed | Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors |
| title_short | Mechanical Assembly Sequence Determination Using Artificial Neural Networks Based on Selected DFA Rating Factors |
| title_sort | mechanical assembly sequence determination using artificial neural networks based on selected dfa rating factors |
| topic | design for assembly artificial neural networks assembly |
| url | https://www.mdpi.com/2073-8994/14/5/1013 |
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