Analyzing the Overfitting of Boosted Decision Trees for the Modelling of Stencil Printing

نویسندگان

چکیده

Stencil printing is one of the key steps in reflow soldering technology, and by spread ultra-fine-pitch components, analysis this process essential. The stencil has been investigated a machine learning technique utilizing ensemble method boosted decision trees. phenomenon overfitting, which can alter prediction error trees also analyzed detail. training data set was acquired experimentally performing using different speeds (from 20 to 120 mm/s) various types solder pastes with particle sizes (particle size range 25–45 µm, 20–38 15–25 µm) aperture sizes, characterized their area ratio 0.35 1.7). overfitting addressed incomplete sets, means that subset corresponding particular input parameter value excluded from training. Four cases were excluding subsets for: ratios 0.75 1.3, 70 mm/s 85 mm/s. It found at values have be lowered eliminating overfitting; though, decrease depends on rate change output vicinity respective value.

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ژورنال

عنوان ژورنال: Periodica polytechnica. Electrical engineering and computer science /

سال: 2022

ISSN: ['2064-5279', '2064-5260']

DOI: https://doi.org/10.3311/ppee.19274