Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/74781

TítuloA comparison of machine learning approaches for predicting in-car display production quality
Autor(es)Matos, Luís Miguel
Domingues, André
Moreira, Guilherme
Cortez, Paulo
Pilastri, André Luiz
Palavras-chaveAnomaly Detection
One-class learning
Automated Machine Learning
Deep Learning
Explainable artificial intelligence
Supervised Learning
Data2021
EditoraSpringer
RevistaLecture Notes in Computer Science
CitaçãoIn H. Yin et al. (Eds.), Intelligent Data Engineering and Automated Learning (IDEAL 2021), 22th International Conference}, Lecture Notes in Computer Science 13113, pp. 3-11, Manchester, UK, November 2021, Springer, ISBN 978-3-030-91607-7.
Resumo(s)In this paper, we explore eight Machine Learning (ML) approaches (binary and one-class) to predict the quality of in-car displays, measured using Black Uniformity (BU) tests. During production, the industrial manufacturer routinely executes intermediate assembly (screwing and gluing) and functional tests that can signal potential causes for abnormal display units. By using these intermediate tests as inputs, the ML model can be used to identify the unknown relationships between intermediate and BU tests, helping to detect failure causes. In particular, we compare two sets of input variables (A and B) with hundreds of intermediate quality measures related with assembly and functional tests. Using recently collected industrial data, regarding around 147 thousand in-car display records, we performed two evaluation procedures, using first a time ordered train-test split and then a more robust rolling windows. Overall, the best predictive results (92%) were obtained using the full set of inputs (B) and an Automated ML (AutoML) Stacked Ensemble (ASE). We further demonstrate the value of the selected ASE model, by selecting distinct decision threshold scenarios and by using a Sensitivity Analysis (SA) eXplainable Artificial Intelligence (XAI) method.
TipoArtigo em ata de conferência
URIhttps://hdl.handle.net/1822/74781
ISBN978-3-030-91607-7
DOI10.1007/978-3-030-91608-4_1
ISSN0302-9743
Versão da editoraThe original publication is available at https://doi.org/10.1007/978-3-030-91608-4_1
Arbitragem científicayes
AcessoAcesso aberto
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings

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