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- W4323786183 abstract "The quality of a product, i.e. a set of its properties, is determined by its technical perfection, which is laid down in the design and ensured during production. To ensure the required level of quality, appropriate technological systems are developed to produce the necessary products in accordance with the technical documentation, moreover, the production of complex products requires the preparation of multiple technological systems, each of which is characterized by the variability of its state during operation. Thus, between the indicators of product quality and technological system parameters there is a certain relationship that reflects the manufacturing process. The relevance of this work is due to the ability to predict the quality of products and timely adjust the technological process by building a model that implements this relationship. The implementation of such a model in the process control system will increase the efficiency of production due to the possibility of developing preventive actions on the control object. Thus, it will be possible to reduce the number of products of inadequate quality level and obtain economic benefits with minimal investment. However, building models based on formal relationships is often difficult due to the complexity of the nature of the processes. The article shows that neural networks are the most suitable method for modeling the technological process. An adaptive neural network model of the technological process for predicting product quality indicators based on the indicators of the technological system has been developed." @default.
- W4323786183 created "2023-03-11" @default.
- W4323786183 creator A5081571603 @default.
- W4323786183 date "2023-01-01" @default.
- W4323786183 modified "2023-09-25" @default.
- W4323786183 title "Neural network modeling of the technological process" @default.
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- W4323786183 doi "https://doi.org/10.1063/5.0125074" @default.
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