Machine learning-based prediction of component self-alignment in vapour phase and infrared soldering

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.

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