In surface mount technology (SMT), mounted components on soldered pads are subject to move during reflow process. This capability is known as self-alignment and is the result of fluid dynamic behaviour of molten solder paste. This capability is critical in SMT because inaccurate self-alignment causes defects such as overhanging, tombstoning, etc. while on the other side, it can enable components to be perfectly self-assembled on or near the desire position. The aim of this study is to develop a machine learning model that predicts the components movement during reflow in and -directions as well as rotation. Our study is composed of two steps: (1) experimental data are studied to reveal the relationships between self-alignment and various factors including component geometry, pad geometry, etc. (2) advanced machine learning prediction models are applied to predict the distance and the direction of components shift using support vector regression (SVR), neural network (NN), and random forest regression (RFR). As a result, RFR can predict components shift with the average fitness of 99%, 99%, and 96% and with average prediction error of 13.47 (µ ), 12.02 (µ ), and 1.52 (deg.) for component shift in , , and rotational directions, respectively. This enhancement provides the future capability of the parameters' optimization in the pick and placement machine to control the best placement location and minimize the intrinsic defects caused by the self-alignment.
Surface mount technology (SMT) is an enhanced method in electronic packaging in which electronic components are placed directly on soldered printing circuit board (PCB) and are permanently attached on PCB with the aim of reflow soldering process. During reflow process, once deposited solder pastes start melting, electronic components move in a direction that achieve their highest symmetry. This motion is known as self-alignment since can correct potential mounting misalignment. In this study, two noticeable machine learning algorithms, including support vector regression (SVR) and random forest regression (RFR) are proposed as a prediction technique to (1) diagnose the relation among component self-alignment, deposited solder paste status and placement machining parameters, (2) predict the final component position on PCB in , , and rotational directions before entering in the reflow process. Based on the prediction result, a non-linear optimization model (NLP) is developed to optimize placement parameters at initial stage. Resultantly, RFR outperforms in terms of prediction model fitness and error. The optimization model is run for 6 samples in which the minimum Euclidean distance from component position after reflow process from ideal position (i.e., the center of pads) is outlined as 25.57 ( ) regarding defined boundaries in model.
In pick and place (P&P) process of surface mount technology (SMT) the placed component can shift from its ideal (or designed) position on the wet solder paste. The solder paste with some fluid properties could slump and the unbalance between different sides of solder paste can lead to other forces on the components as well. Though the shifts are usually considered to be negligible and can be made up to some extent by the following self-alignment during the process of soldering reflow, it should be attracted attention as its importance for addressing the quality of the printed circuit board (PCB) in SMT. To minimize or control the component shifts, whose relationship with the characteristics of the solder paste (e.g., offset, volume) should be studied initially. In this paper, we design a comprehensive experiment and collect the data from a state-of-the-art SMT assembly line. Then we use support vector regression (SVR) model to predict the component shifts based on different situations of solder paste and placement settings. Also, two kernel functions, linear (SVR-Linear) and radial basis function (SVR-RBF), are employed. The achieved results indicate that the component shift in P&P process is significant, and the SVR model is highly qualified for the forecast of the component shifts. Particularly, the SVR-RBF model outperforms the SVR-Linear model considering the prediction error.
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