Abstract
Die-casting is a popular manufacturing process that produces precise metal parts with excellent dimensional accuracy and smooth cast surfaces. Recently die-casting process condition data can be acquired to be used as input for machine learning techniques for fault detection. The rapid development of complex and accurate machine learning algorithms, such as tree ensembles and deep learning, allows the accurate detection of faulty products. However, interpreting and explaining black-box models is crucial in the die-casting industry because the predictions provided by the machine learning solution can be adopted in practice only after understanding the internal decision mechanism of the model. To solve this problem, rule extraction methods generate simple rule-based predictive models from complex tree ensembles. Nevertheless, rulesets may contain numerous complex rules with redundant conditions, and the standard structure of rulesets does not clearly show the hierarchical relationships and frequent interactions among their elements. For this reason, in this study, a visualization tool based on formal concept analysis, called RuleLat (Rule Lattice), is proposed, which generates simple visual representations of rule-based classifiers. The generated models depict the hierarchical relationships of interactions among conditions, rules, and predicted classes in a modified concept lattice that is easy to analyze and understand. To demonstrate the applicability of the proposed method, a case study using real-world manufacturing data collected from a die-casting company in Korea is presented. RuleLat is adopted as a tool for interpretable machine learning, and the process conditions of three types of defects (porosity, material, and imprint) are analyzed and discussed.
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Abbreviations
- CL:
-
Control limit
- FCA:
-
Formal concept analysis
- LCL:
-
Lower control limit
- LGBM:
-
Light gradient boosting machine
- RF:
-
Random forest
- SMOTE:
-
Synthetic minority oversampling technique
- UCL:
-
Upper control limit
- XAI:
-
EXplainable artificial intelligence
- XGB:
-
EXtreme gradient boosting
- \(\mathcal{R}\) :
-
Rule-based classifier
- \({\mathbb{R}}\) :
-
Rule context
- \({\mathbb{B}}\) :
-
Set of all rule concepts
- \({\mathbb{G}}\) :
-
Rule lattice
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Acknowledgements
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (Grant Nos. 2017H1D8A2031138, 2019R1F1A1064125).
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Obregon, J., Jung, JY. Rule-based visualization of faulty process conditions in the die-casting manufacturing. J Intell Manuf 35, 521–537 (2024). https://doi.org/10.1007/s10845-022-02057-1
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DOI: https://doi.org/10.1007/s10845-022-02057-1