Numerical investigation on the effect of solder paste rheological behaviour and printing speed on stencil printing

Purpose of this paper was to investigate the effect of different viscosity models (Cross and Al-Ma’aiteh) and different printing speeds on the numerical results (e.g., pressure over stencil) of a numerical model regarding stencil printing. A finite volume model was established for describing the printing process. Two types of viscosity models for non-Newtonian fluid properties were compared. The Cross model was fitted to the measurement results in the initial state of a lead-free solder paste, and the parameters of a Al-Ma’aiteh material model were fitted in the stabilised state of the same paste. Four different printing speeds were also investigated from 20 to 200 mm/s. Noteworthy differences were found in the pressure between utilising the Cross model and the Al-Ma’aiteh viscosity model. The difference in pressure reached 33–34% for both printing speeds of 20 and 70 mm/s, and reached 31% and 27% for the printing speed of 120 and 200 mm/s. The variation in the difference was explained by the increase in the rates of shear by increasing printing speeds. Parameters of viscosity model should be determined for the stabilised state of the solder paste. Neglecting the thixotropic paste nature in the modelling of printing can cause a calculation error of even ~30%. By using the Al-Ma’aiteh viscosity model over the stabilised state of solder pastes can provide more accurate results in the modelling of printing, which is necessary for the effective optimisation of this process, and for eliminating soldering failures in highly integrated electronic devices

Predicting the Transfer Efficiency of Stencil Printing by Machine Learning Technique

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

Introduction to Surface-Mount Technology

In chapter 1, the surface-mount technology and reflow soldering technology are overviewed. A brief introduction is presented into the type of electronic components, including through-hole- and surface-mounted ones. Steps of reflow soldering technology are outlined, and details are given regarding the properties of solder material in this technology. The rheological behavior of solder pastes is detailed, and some recent advancements in addressing the thixotropic behavior of this material are summarized. The process of stencil printing is detailed next, which is the most crucial step in reflow soldering technology; since even 60–70% of the soldering failures can be traced back to this process. The topic includes the structures of stencils, discussion of the primary process parameters, and process optimization possibilities by numerical modeling. Process issues of component placement are presented. The critical parameter (process and machines capability), which is used extensively for characterizing the placement process is studied. In connection with the measurement of process capability, the method of Gage R&R (repeatability and reproducibility) is detailed, including the estimation of respective variances. Process of the reflow soldering itself is detailed, including the two main phenomena taking place when the solder is in the molten state, namely: wetting of the liquid solder due to surface tension, and intermetallic compound formation due to diffusion. Solder profile calculation and component movements during the soldering (e.g., self-alignment of passive components) are presented too. Lastly, the pin-in-paste technology (reflow solder of through-hole components) is detailed, including some recent advancements in the optimization of this technology by utilizing machine learning techniques

Non-Newtonian numerical modelling of solder paste viscosity measurement

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 0.5mm. 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

Investigating the effect of viscosity models on the stencil printing by numerical modelling

In this paper, the effect of different viscosity models on the numerical results of stencil printing modelling was addressed. The numerical model was established by defining the geometry of the printing squeegee, the stencil, and the shape of the rolling solder paste. The squeegee had a 53° attack angle, which was determined by prior measurements, and the blade height was 20 mm. The output parameters of the model included the shear rate distribution and velocity field within the rolling solder paste, and the pressure profile on the stencil. The material properties of the solder paste were set by measuring the rheological behaviour of Type3 (particle size: 20–45 µm), Type4 (particle size: 20–38 µm), Type5 (particle size: 15–25 µm) solder pastes and by fitting viscosity models onto the measurement results. Two different material models were compared, the Cross and the Carreau-Yasuda models, where the effect of the different models on the numerical results was evaluated The results showed a significant difference in the pressure during stencil printing between the cases using the viscosity of solder pastes in initial state (Cross model) and in steady state (Carreu-Yasuda model). The difference was about 25% in the case of Type-3, Type-4, and Type-5 solder pastes respectively. These differences proved that appropriate material models should be used in the numerical models of the stencil printing, to be able to apply that in early-stage prediction in the concept of Industry 4.0

Transient Numerical Modelling of the Pin-in-Paste Technology

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

Establishing a Machine learning Based Framework for Optimising Electronics Assembly

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