Assembly Sequence Planning Using Artificial Neural Networks for Mechanical Parts Based on Selected Criteria
Autor: | Katarzyna Peta, Marcin Suszyński |
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Rok vydání: | 2021 |
Předmět: |
Technology
QH301-705.5 Computer science QC1-999 modelling General Materials Science Sine Biology (General) Cluster analysis QD1-999 Instrumentation Network model Fluid Flow and Transfer Processes Sequence Basis (linear algebra) Artificial neural network Physics Process Chemistry and Technology Hyperbolic function General Engineering assembly sequence planning (ASP) Engineering (General). Civil engineering (General) Computer Science Applications Chemistry Method of steepest descent TA1-2040 artificial neural networks Algorithm |
Zdroj: | Applied Sciences, Vol 11, Iss 10414, p 10414 (2021) Applied Sciences Volume 11 Issue 21 |
ISSN: | 2076-3417 |
Popis: | 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. |
Databáze: | OpenAIRE |
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