Machine learning-based prediction of component self-alignment in vapour phase and infrared soldering
Soldering & Surface Mount Technology
ISSN: 0954-0911
Article publication date: 31 May 2019
Issue publication date: 20 June 2019
Abstract
Purpose
This paper aims to investigate the self-alignment of 0603 size (1.5 × 0.75 mm) chip resistors, which were soldered by infrared or vapour phase soldering. The results were used for establishing an artificial neural network for predicting the component movement during the soldering.
Design/methodology/approach
The components were soldered onto an FR4 testboard, which was designed to facilitate the measuring of the position of the components both prior to and after the soldering. A semi-automatic placement machine misplaced the components intentionally, and the self-alignment ability was determined for soldering techniques of both infrared and vapour phase soldering. An artificial neural network-based prediction method was established, which is able to predict the position of chip resistors after soldering as a function of component misplacement prior to soldering.
Findings
The results showed that the component can self-align from farer distances by using vapour phase method, even from relative misplacement of 50 per cent parallel to the shorter side of the component. Components can self-align from a relative misplacement only of 30 per cent by using infrared soldering method. The established artificial neural network can predict the component self-alignment with an approximately 10-20 per cent mean absolute error.
Originality/value
It was proven that the vapour phase soldering method is more stable from the component’s self-alignment point of view. Furthermore, machine learning-based predictors can be applied in the field of reflow soldering technology, and artificial neural networks can predict the component self-alignment with an appropriately low error.
Keywords
Acknowledgements
The research reported in this paper was supported by the Higher Education Excellence Program of the Ministry of Human Capacities in the frame of Artificial Intelligence research area of Budapest University of Technology and Economics (BME FIKP-MI/SC).
Citation
Krammer, O., Martinek, P., Illes, B. and Jakab, L. (2019), "Machine learning-based prediction of component self-alignment in vapour phase and infrared soldering", Soldering & Surface Mount Technology, Vol. 31 No. 3, pp. 163-168. https://doi.org/10.1108/SSMT-11-2018-0045
Publisher
:Emerald Publishing Limited
Copyright © 2019, Emerald Publishing Limited