Neural Model of Manufacturing Process as a Way to Improve Predictability of Manufacturing
Lecture Notes in Mechanical Engineering, ISSN: 2195-4364, Page: 24-38
2022
- 4Citations
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations4
- Citation Indexes4
- CrossRef4
Conference Paper Description
The aim of this paper is to present the concept of a neural model of a manufacturing process. The goal of the model is to forecast the number of defective products based on the historical values of the manufacturing process parameters and the historical values of the number of defective products. The paper describes the creation of the model on the example of a manufacturing process in a glass factory. The use of the model allowed for a more accurate prediction of the number of defective products, which is treated as the improvement of the manufacturing predictability mentioned in the title of the paper. The model includes several NARX artificial neural networks. Each NARX network considers data from a different part of the production line. The forecast results on four test data sets are also presented. These results were compared with the classic approach, which uses a single neural network. The created model allowed for a significant reduction in the prediction error in four test data sets considered.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85128931959&origin=inward; http://dx.doi.org/10.1007/978-3-031-00805-4_3; https://link.springer.com/10.1007/978-3-031-00805-4_3; https://dx.doi.org/10.1007/978-3-031-00805-4_3; https://link.springer.com/chapter/10.1007/978-3-031-00805-4_3
Springer Science and Business Media LLC
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