Domain-Knowledge-Driven Explainable Product Quality Prediction

Domain-Knowledge-Driven Explainable Product Quality Prediction: Using prior knowledge to improve explanations of quality prediction models

Bos, Thomas (TU Delft Electrical Engineering, Mathematics and Computer Science)

de Weerdt, M.M. (mentor)
Demirović, E. (graduation committee)

Delft University of Technology

Computer Science

2022-06-15

Explainable artificial intelligence has in recent years allowed us to investigate how many machine learning methods are creating its predictions. This is especially useful in scenarios where the goal is not to predict a variable, but to explain what influences that variable. However, the methods that have been created thus far do not focus on specific domains and only give scientific relations between variables. In the case of a production process, there exist a multitude of related variables, constrained in different ways, thus requiring a more sophisticated method than those which are universally applicable. The target of this research is a bottle filling machine at a large carbonated drink manufacturer, of which a great amount of data has been stored. The product quality, in this case the amount of CO2 in the filled bottles, is subject to high amounts of fluctuation, leading to a lot of waste. To further contextualize the problem of bottle filling, an artificial dataset is created to serve as a basis for visualization and evaluation. Using literature on carbonated drink filling, multiple hypotheses were then formulated, independently from the thesis company, as a target for the evaluation process. To address the problem of determining what factors influence bottle CO2, a methodology is proposed which aims to provide explainable quality prediction of production process output, while using available domain knowledge to increase the plausibility of explanations. The methodology makes use of counterfactual explanations for tree ensembles and process state forecasting to show how a process state can be changed to yield better product quality. Evaluation on the artificial and real datasets show that the methodology is able to effectively predict the amount of CO2 in a bottle, while giving changes to the current process state which improve the product quality. It is also shown that the results produced by the methodology are in line with the formulated hypotheses. Bottle CO2 is influenced greatly by temperature changes in the filler and these temperature changes are caused by flow of cooled lemonade through the process. As these flows are dependent on the filling speed, irregular filling is suspected to be the root cause for the bottle CO2 fluctuations.

XAI
Production process
Process forecast
Quality prediction
Explainable AI
Explainability
Bottle filling
Carbonated drinks
counterfactual explanations
time-series forecasting

http://resolver.tudelft.nl/uuid:293a9973-9840-4cd8-81d8-0f7cf380a77b

Student theses

master thesis

© 2022 Thomas Bos