Purpose The purpose of this paper is to present the establishment of a computational fluid dynamics model for investigating different non-Newtonian rheological models of solder pastes by simulating solder paste viscosity measurement. A combined material model was established which can follow the measured, apparent viscosity values with lower error. Design/methodology/approach The model included a parallel plate arrangement of rheometers. The diameter of the plate was 50 mm, whereas the gap between the plates was 0.5 mm. Only one quarter of the plate was modelled to enable using fine enough mesh, while keeping the calculation time low. Non-Newtonian properties were set using user defined function in Ansys, based on the Cross and Carreau–Yasuda material models. The viscosity values predicted by the mathematical models were compared to measured viscosity values of different types of solder pastes. Findings It was found that the Cross model predicts the apparent viscosity with a relatively high error (even approximately 50 per cent) at lower shear rates, whereas the Carerau–Yasuda model has higher errors at higher shear rates. The application of the proposed, combined model can result in a much lower error in the apparent viscosity between the calculated and measured viscosity values. Originality/value The error of Cross and Carreau–Yasuda material models has not been investigated yet in details. The proposed, combined material model can be applied for subsequent simulations via the described UDF, e.g. in the numerical modelling of the stencil printing. This can result in a more accurate modelling of the stencil printing process, which is inevitable considering the printing of solder paste for today fine-pitch, small size components.
Experiment was carried out for acquiring data regarding the transfer efficiency of stencil printing, and a machine learning-based technique (artificial neural network) was trained for predicting that parameter. The input parameters space in the experiment included the printing speed at five different levels (between 20 and120 mm/s) and the area ratio of stencil apertures from 0.34 to1.69. Three types of lead-free solder paste were also investigated as follows: Type-3 (particle size range is 20-45 µm), Type-4 (20-38 µm), Type-5 (10-25 µm). The output parameter space included the height and the area of the print deposits and the respective transfer efficiency, which is the ratio of the deposited paste volume to the aperture volume. Finally, an artificial neural network was trained with the empirical data using the Levenberg-Marquardt training algorithm. The optimal tuning factor for the fine-tuning of the network size was found to be approximately 9, resulting in a hidden neuron number of 160. The trained network was able to predict the output parameters with a mean average percentage error (MAPE) lower than 3%. Though, the prediction error depended on the values of the input parameters, which is elaborated in the paper in details. The research proved the applicability of machine learning techniques in the yield prediction of the process of stencil printing.
The pin-in-paste technology is an advancing soldering technology for assembling complex electronic products, which include both surface-mounted and through-hole components. A computational fluid dynamics model was established to investigate the stencil printing step of this technology, where the hole-filling by the solder pastes is the most critical factor for acquiring reliable solder joints. The geometry of the transient numeric model included the printing squeegee, the stencil, and the through-holes of a printed circuit board with different geometries and arrangements. A two-phase fluid model (solder paste + air) was applied, utilizing the Volume of Fluid method (VoF). The rheological properties of the solder paste were addressed by an exhaustive viscosity model. It was found that the set of through-holes affected the flow-field and yielded a decrease in the hole-filling if they were arranged in parallel with the travelling direction of the printing squeegee. Similar disturbance on the flow-field was found for oblong-shaped through-holes if they were arranged in parallel with the squeegee movement. The findings imply that the arrangement of a set of through-holes and the orientation of oblong-shaped through-holes should be optimized even in the early design phase of electronic products and during the set of assembly processes. The soldering failures in pin-in-paste technology can be reduced by these early design-phase considerations, and the first-pass yield of electronic soldering technologies can be enhanced.
By the spread of miniaturized components, like the 0201mm size-code (200 × 100 µm) passives, utilizing advanced optimization techniques becomes crucial in this field. A framework was established, which used machine-learning-based estimators to predict the yield of any manufacturing process in electronics technology. The framework includes using various methods, like artificial neural networks (ANN), decision trees and neuro-fuzzy inference systems. It can automatically split the input data into training and testing sets for each learning epoch to reach optimal performance and prevent possible overfitting at the same time. Besides, optimal structures and description functions are also determined automatically. To assess the prediction error, the framework calculates the MAE (Mean Absolute Error), the RMSE (Root Mean Square Error) and the MAPE (Mean Absolute Percentage Error) parameters to decide if the built estimator structure is appropriate. As an outcome, the framework can provide several parameters that the user can optionally select. Parameters like the predicted values of a process output parameter over different input process parameters are provided. Besides, KPI (Key Process Index) of the output parameters or the Desirability Function (which combines many output parameters) can be acquired. The applicability and the performance of the framework were analyzed on the stencil printing process by building an ANN structure.
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