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

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

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

Analyzing the overfitting of boosted decision trees for the modelling of stencil printing

Stencil printing is one of the key steps in reflow soldering technology, and by the spread of ultra-fine-pitch components, analysis of this process is essential. The process of stencil printing has been investigated by a machine learning technique utilizing the ensemble method of boosted decision trees. The phenomenon of overfitting, which can alter the prediction error of boosted decision trees has also been analyzed in detail. The training data set was acquired experimentally by performing stencil printing using different printing speeds (from 20 to 120 mm/s) and various types of solder pastes with different particle sizes (particle size range 25–45 μm, 20–38 μm, 15–25 μm) and different stencil aperture sizes, characterized by their area ratio (from 0.35 to 1.7). The overfitting phenomenon was addressed by training by using incomplete data sets, which means that a subset of data corresponding to a particular input parameter value was excluded from the training. Four cases were investigated with incomplete data sets, by excluding the corresponding data subsets for: area ratios of 0.75 and 1.3, and printing speeds of 70 mm/s and 85 mm/s. It was found that the prediction error at input parameter values that have been excluded from the training can be lowered by eliminating the overfitting; though, the decrease in the prediction error depends on the rate of change in the output parameter in the vicinity of the respective input parameter value

Numerical Modeling in Civil and Mining Geotechnical Engineering

This Special Issue (SI) collects fourteen articles published by leading scholars of numerical modeling in civil and mining geotechnical engineering. There is a good balance in the number of published articles, with seven in civil engineering and seven in mining engineering. The software used in the numerical modeling of these article varies from numerical codes based on continuum mechanics to those based on distinct element methods or mesh-free methods. The studied materials vary from rock, soil, and backfill to tailings. The investigations vary from mechanical behavior to hydraulic and thermal responses of infrastructures varying from pile foundations to tailings dams and underground openings. The SI thus collected a diversity of articles, reflecting the state-of-the-art of numerical modeling applied in civil and mining geotechnical engineering

Business analytics in industry 4.0: a systematic review

Recently, the term “Industry 4.0” has emerged to characterize several Information Technology and Communication (ICT) adoptions in production processes (e.g., Internet-of-Things, implementation of digital production support information technologies). Business Analytics is often used within the Industry 4.0, thus incorporating its data intelligence (e.g., statistical analysis, predictive modelling, optimization) expert system component. In this paper, we perform a Systematic Literature Review (SLR) on the usage of Business Analytics within the Industry 4.0 concept, covering a selection of 169 papers obtained from six major scientific publication sources from 2010 to March 2020. The selected papers were first classified in three major types, namely, Practical Application, Reviews and Framework Proposal. Then, we analysed with more detail the practical application studies which were further divided into three main categories of the Gartner analytical maturity model, Descriptive Analytics, Predictive Analytics and Prescriptive Analytics. In particular, we characterized the distinct analytics studies in terms of the industry application and data context used, impact (in terms of their Technology Readiness Level) and selected data modelling method. Our SLR analysis provides a mapping of how data-based Industry 4.0 expert systems are currently used, disclosing also research gaps and future research opportunities.The work of P. Cortez was supported by FCT - Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/2020. We would like to thank to the three anonymous reviewers for their helpful suggestions

A Novel System to Increase Yield of Manufacturing Test of an RF Transceiver through Application of Machine Learning

Electronic manufacturing and design companies maintain test sites for a range of products. These products are designed according to the end-user requirements. The end user requirement, then, determines which of the proof of design and manufacturing tests are needed. Test sites are designed to carry out two things, i.e., proof of design and manufacturing tests. The team responsible for designing test sites considers several parameters like deployment cost, test time, test coverage, etc. In this study, an automated test site using a supervised machine learning algorithm for testing an ultra-high frequency (UHF) transceiver is presented. The test site is designed in three steps. Firstly, an initial manual test site is designed. Secondly, the manual design is upgraded into a fully automated test site. And finally supervised machine learning is applied to the automated design to further enhance the capability. The manual test site setup is required to streamline the test sequence and validate the control and measurements taken from the test equipment and unit under test (UUT) performance. The manual test results showed a high test time, and some inconsistencies were observed when the test operator was required to change component values to tune the UUT. There was also a sudden increase in the UUT quantities and so, to cater for this, the test site is upgraded to an automated test site while the issue of inconsistencies is resolved through the application of machine learning. The automated test site significantly reduced test time per UUT. To support the test operator in selecting the correct component value the first time, a supervised machine learning algorithm is applied. The results show an overall improvement in terms of reduced test time, increased consistency, and improved quality through automation and machine learning.Partial funding for open access charge: Universidad de Málag