Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria

The proposed model of the neural network describes the task of planning the assembly sequence on the basis of predicting the optimal assembly time of mechanical parts. In the proposed neural approach, the k-means clustering algorithm is used. In order to find the most effective network, 10,000 netwo...

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Autores principales:	Marcin Suszyński, Katarzyna Peta
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	assembly sequence planning (ASP) modelling artificial neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/0dbe4c5ebfe34499ace77773711ef69b
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id	oai:doaj.org-article:0dbe4c5ebfe34499ace77773711ef69b
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spelling	oai:doaj.org-article:0dbe4c5ebfe34499ace77773711ef69b2021-11-11T15:24:04ZAssembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria10.3390/app1121104142076-3417https://doaj.org/article/0dbe4c5ebfe34499ace77773711ef69b2021-11-01T00:00:00Zhttps://www.mdpi.com/2076-3417/11/21/10414https://doaj.org/toc/2076-3417The proposed model of the neural network describes the task of planning the assembly sequence on the basis of predicting the optimal assembly time of mechanical parts. In the proposed neural approach, the k-means clustering algorithm is used. In order to find the most effective network, 10,000 network models were made using various training methods, including the steepest descent method, the conjugate gradients method, and Broyden–Fletcher–Goldfarb–Shanno algorithm. Changes to network parameters also included the following activation functions: linear, logistic, tanh, exponential, and sine. The simulation results suggest that the neural predictor would be used as a predictor for the assembly sequence planning system. This paper discusses a new modeling scheme known as artificial neural networks, taking into account selected criteria for the evaluation of assembly sequences based on data that can be automatically downloaded from CAx systems.Marcin SuszyńskiKatarzyna PetaMDPI AGarticleassembly sequence planning (ASP)modellingartificial neural networksTechnologyTEngineering (General). Civil engineering (General)TA1-2040Biology (General)QH301-705.5PhysicsQC1-999ChemistryQD1-999ENApplied Sciences, Vol 11, Iss 10414, p 10414 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	assembly sequence planning (ASP) modelling artificial neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999
spellingShingle	assembly sequence planning (ASP) modelling artificial neural networks Technology T Engineering (General). Civil engineering (General) TA1-2040 Biology (General) QH301-705.5 Physics QC1-999 Chemistry QD1-999 Marcin Suszyński Katarzyna Peta Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria
description	The proposed model of the neural network describes the task of planning the assembly sequence on the basis of predicting the optimal assembly time of mechanical parts. In the proposed neural approach, the k-means clustering algorithm is used. In order to find the most effective network, 10,000 network models were made using various training methods, including the steepest descent method, the conjugate gradients method, and Broyden–Fletcher–Goldfarb–Shanno algorithm. Changes to network parameters also included the following activation functions: linear, logistic, tanh, exponential, and sine. The simulation results suggest that the neural predictor would be used as a predictor for the assembly sequence planning system. This paper discusses a new modeling scheme known as artificial neural networks, taking into account selected criteria for the evaluation of assembly sequences based on data that can be automatically downloaded from CAx systems.
format	article
author	Marcin Suszyński Katarzyna Peta
author_facet	Marcin Suszyński Katarzyna Peta
author_sort	Marcin Suszyński
title	Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria
title_short	Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria
title_full	Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria
title_fullStr	Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria
title_full_unstemmed	Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria
title_sort	assembly sequence planning using artificial neural networks for mechanical parts based on selected criteria
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0dbe4c5ebfe34499ace77773711ef69b
work_keys_str_mv	AT marcinsuszynski assemblysequenceplanningusingartificialneuralnetworksformechanicalpartsbasedonselectedcriteria AT katarzynapeta assemblysequenceplanningusingartificialneuralnetworksformechanicalpartsbasedonselectedcriteria
_version_	1718435350364291072

Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria

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