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Investigating Machine Learning Techniques for Predicting the Process Characteristics of Stencil Printing.

Péter Martinek1, Balázs Illés1, Norocel Codreanu2

  • 1Department of Electronics Technology, Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Műegyetem rkp. 3., H-1111 Budapest, Hungary.

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Summary

Machine learning models accurately predict solder paste deposition in stencil printing. Regression trees outperformed other methods, offering a robust approach for electronics manufacturing optimization.

Keywords:
artificial neural networkhyperparameter optimizationmachine learningregression treesstencil printing

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Area of Science:

  • Materials Science and Engineering
  • Manufacturing Processes
  • Artificial Intelligence in Engineering

Background:

  • Stencil printing is critical for mass assembly of electronic circuits via reflow soldering.
  • Predicting solder paste deposition characteristics (area, thickness, volume) is essential for process control.
  • Existing methods may lack the precision required for optimizing complex printing parameters.

Purpose of the Study:

  • To investigate and compare various machine learning (ML) methods for predicting stencil printing outcomes.
  • To identify the most effective ML model for accurately forecasting solder paste deposition.
  • To establish a robust methodology for ML model optimization and validation in electronics manufacturing.

Main Methods:

  • Experimental data generation by varying printing speed, stencil aperture size, and solder paste particle size.
  • Assessment of multiple ML algorithms including Adaptive Neuro-Fuzzy Inference Systems (ANFIS), Artificial Neural Networks (ANN), boosted trees, regression trees, and Support Vector Machines (SVM).
  • Hyperparameter optimization and cross-validation were employed to fine-tune models and prevent overfitting.

Main Results:

  • Regression trees demonstrated the highest performance in modeling stencil printing characteristics.
  • Artificial Neural Networks (ANN) with Bayesian regularization learning showed comparable, slightly lower performance.
  • The study successfully optimized and compared diverse ML approaches for predicting solder paste deposition.

Conclusions:

  • Machine learning, particularly regression trees, offers a powerful tool for predicting and optimizing stencil printing processes.
  • The developed methodology for ML model fine-tuning and comparison is adaptable to various electronics manufacturing applications.
  • Accurate prediction of solder paste deposition can enhance the reliability and efficiency of electronic circuit assembly.