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World Congress 2025 • Aug 20, 2025 • Session details

Solving the puzzle: Leveraging machine learning for effective root cause analysis

Bernhard , Varsha Venugopal

Are traditional statistical methods failing your complex manufacturing data? Discover how pairing LightGBM with Explainable AI cuts root cause analysis from days to under two hours.

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#1 about 5 min

Challenges of measuring high-precision semiconductor manufacturing components

Manufacturing extremely precise mirrors for lithography creates immense amounts of complex sensor and parameter data.

#2 about 3 min

Limitations of traditional statistical methods on high-dimensional data

Standard methods like linear regression struggle with non-linear relationships, missing data points, and imbalanced defect occurrence.

#3 about 3 min

Building user trust and finding insights via explainable AI

Explainable AI techniques turn black-box machine learning models into transparent tools that reveal hidden predictive patterns.

#4 about 3 min

Framing manufacturing root cause problems as machine learning tasks

Translating rising defect rates across mixed data streams into actionable modeling features enables deeper root-cause diagnostics.

#5 about 2 min

Handling mixed data types using the LightGBM framework

The LightGBM framework efficiently handles high-dimensional spaces, missing values, and non-linear interactions without extensive pre-processing.

#6 about 2 min

Extracting transparent feature influence using SHAP and game theory

Shapley additive explanations attribute influence to specific model outcomes to transform interpretability into causal pattern discovery.

#7 about 3 min

Identifying root causes through global and local SHAP plots

Global importance graphs and local dependency plots isolate specific parameter thresholds where machine defect probabilities sharply increase.

#8 about 3 min

Creating an internal self-service data investigation Python application

Combining human domain knowledge and data science into a Python toolkit enables engineers to safely conduct analyses independently.

#9 about 5 min

Key principles for adopting machine learning tools in engineering

Successful analytics tool adoption requires human-centric design, robust handling of real-world noise, and transparency regarding algorithmic limitations.

Matching moments

2:52 min

Introduction to safety-critical machine learning in automotive contexts

Jan Zawadzki · WWC 2022

1:20 min

Uncovering the hidden technical debt in machine learning

Nico Axtmann · WWC 2022

15:08 min

Audience questions on practical machine learning operational strategies

Lina Weichbrodt · LIVE

11:32 min

Limitations of traditional machine learning debugging approaches

Svetlin Penkov · LIVE

2:24 min

History and growth of explainable artificial intelligence

Karol Przystalski · LIVE

2:30 min

Embracing machine learning and predictive system behavior analytics

Mihaela-Roxana Ghidersa · LIVE

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