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This study introduces a novel method combining data science and physical simulations to address challenges in machine learning (ML) for industries with diverse products. The approach enhances data comparability for ML models, even with limited production variant data.

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Area of Science:

  • Data Science
  • Artificial Intelligence
  • Physical Simulations
  • Machine Learning

Background:

  • Growing demands for quality, sustainability, and digitalization increase the importance of data science and AI across industries.
  • Extensive product ranges present challenges, particularly for machine learning (ML) models with limited data per production variant.
  • Existing approaches struggle to effectively analyze and model production variants with low quantities.

Purpose of the Study:

  • To propose a methodology integrating data science and physical simulations for enhanced industrial data analysis.
  • To enable machine learning (ML) and statistical analyses on process data across different production variants, especially those with limited data.
  • To identify key parameters influencing product quality and explore their precise control using ML models.

Main Methods:

  • Utilizing results from finite element method (FEM) simulations to transform process data.
  • Developing a data transformation technique to ensure comparability of process data across diverse production variants.
  • Applying the transformed data to machine learning (ML) methods and statistical analyses.

Main Results:

  • Successfully demonstrated a method to effectively model production variants with very low quantities.
  • Enabled the identification of critical parameters influencing product quality that are not evident through alternative methods.
  • Illustrated the methodology's application using an aluminum production example.

Conclusions:

  • The proposed methodology effectively overcomes data limitations for ML in industries with extensive product variations.
  • This approach enhances production processes by identifying and enabling precise control over quality-influencing parameters.
  • Significant potential exists for optimizing industrial processes through the integration of physical simulations and ML, despite some challenges.