Bootstrap Aggregation Technique for Evaluating the Significance of Manufacturing Process Parameters in the Glass Industry
Łukasz Paśko
a:1:{s:5:"en_US";s:65:"Rzeszów University of Technology, Department of Computer Science";}Aneta Kuś
Rzeszów University of Technology, Department of Computer ScienceAbstract
The article presents the application of the bootstrap aggregation technique to create a set of artificial neural networks (multilayer perceptron). The task of the set of neural networks is to predict the number of defective products on the basis of values of manufacturing process parameters, and to determine how the manufacturing process parameters affect the prediction result. For this purpose, four methods of determining the significance of the manufacturing process parameters have been proposed. These methods are based on the analysis of connection weights between neurons and the examination of prediction error generated by neural networks. The proposed methods take into account the fact that not a single neural network is used, but the set of networks. The article presents the research methodology as well as the results obtained for real data that come from a glassworks company and concern a production process of glass packaging. As a result of the research, it was found that it is justified to use a set of neural networks to predict the number of defective products in the glass industry, and besides, the significance of the manufacturing process parameters in the glassworks company was established using the developed set of neural networks.
Keywords:
manufacturing process, glassworks, neural networks, bagging, manufacturing parametersReferences
BREIMAN L. 1996. Bias, variance and arcing classifiers. Technical Report TR 460. Dept. of Statistics. University of California, Berkeley, CA, USA. Google Scholar
ELSKEN T., METZEN J.H., HUTTER F. 2019. Neural Architecture Search: A Survey. Journal of Machine Learning Research, 20: 1-21. Google Scholar
FRANCIK S. 2009. Metoda prognozowania szeregów czasowych przy użyciu sztucznych sieci neuronowych. Inżynieria Rolnicza, 13(6): 53-59. Google Scholar
GOLKA W., ARSENIUK E., GOLKA A., GÓRAL T. 2020. Sztuczne sieci neuronowe i teledetekcja w ocenie porażenia pszenicy jarej fuzariozą kłosów. Biuletyn Instytutu Hodowli i Aklimatyzacji Roślin, 288: 67-75. Google Scholar
GÓRSKI M., KALETA J., LANGMAN J. 2008. Zastosowanie sztucznych sieci neuronowych do oceny stopnia dojrzałości jabłek. Inżynieria Rolnicza, 12(7): 53-56. Google Scholar
HEBDA T., FRANCIK S. 2006. Model twardości ziarniaków pszenicy wykorzystujący sztuczne sieci neuronowe. Inżynieria Rolnicza, 10(13): 139-146. Google Scholar
JASIŃSKI T., BOCHENEK A. 2016. Prognozowanie cen nieruchomości lokalowych za pomocą sztucznych sieci neuronowych. Studia i Prace WNEIZ US, 45(1): 317-327. Google Scholar
KURT I., TURE M., UNUBOL M., KATRANCI M., GUNEY E. 2014. Comparing Performances of Logistic Regression, Classification & Regression Trees and Artificial Neural Networks for Predicting Albuminuria in Type 2 Diabetes Mellitus. International Journal of Sciences: Basic and Applied Research (IJSBAR), 16(1): 173-187. Google Scholar
LEFIK M. 2005. Zastosowania sztucznych sieci neuronowych w mechanice i inżynierii. Zeszyty Naukowe. Rozprawy Naukowe, 341: 3-258. Google Scholar
NIEDBAŁA G., LENARTOWICZ T., KOZŁOWSKI J.R., ZABOROWICZ M. 2015. Modelowanie neuronowe jako metoda prognozowania zawartości skrobi w ziemniakach na potrzeby Porejestrowego Doświadczalnictwa Odmianowego i Rolniczego (PDOiR). Nauka Przyroda Technologie, 9(2): 1-7. Google Scholar
OPITZ D.W., MACLIN R.F. 1997. An empirical evaluation of bagging and boosting for artificial neural networks. Proceedings of International Conference on Neural Networks (ICNN’97), 3: 1401-1405. Google Scholar
PAŚKO Ł. 2020. Significance of Manufacturing Process Parameters in a Glassworks. Advances in Manufacturing Science and Technology, 44(2): 39-45. Google Scholar
REN P., XIAO Y., CHANG X., HUANG P., LI Z., CHEN X., WANG X. 2021. A Comprehensive Survey of Neural Architecture Search: Challenges and Solutions. ACM Computing Surveys, 54(4): 1-34. Google Scholar
RODZIEWICZ A., PERZYK M. 2016. Application of significance analysis to finding root causes of product defects in continuous casting of steel. Computer Methods in Materials Science, 16(4): 187-195. Google Scholar
ROJEK I. 2015. Sieci neuronowe w kontroli jakości procesu. Studies & Proceedings Polish Association for Knowledge Management, 74: 91-100. Google Scholar
TADEUSIEWICZ R., HADUCH B. 2015. Wykorzystanie sieci neuronowych do analizy danych i pozyskiwania wiedzy w systemie ekspertowym do oceny parametrów benzyn silnikowych. Nafta-Gaz, 71(10): 776-785. Google Scholar
ZHIBIN W., NIANPING L., JINQUNG P., HAIJIAO C., PENGLONG L., HONGQIANG L., XIWANG L. 2018. Using an ensemble machine learning methodology – Bagging to predict occupants’ thermal comfort in buildings. Energy and Buildings, 173: 117-127. Google Scholar
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Rzeszów University of Technology, Department of Computer Science