...Lichang Huang; Yingjun Zhang; Yuting Tong; Xiaobin Xu; Jinxing Chen; Sha Xu; Yu Zhang Purpose The purpose of this study is to implement a hybrid finite element analysis (FEA) and machine learning framework. A deep neural network (DNN) surrogate model was trained on parametric FEA data for rapid...

.../methodology/approach Experimental data of the through-scan acoustic microscope’s dispensing parameters are collected, cleaned and segregated for training and testing purposes. Three machine learning models, support vector machine, random forest and linear regression, are used and evaluated to generate...

.... Originality/value BGA void formation in a flip-chip underfilling process can be captured quantitatively with advanced image segmentation. © Emerald Publishing Limited 2024 Emerald Publishing Limited Licensed re-use rights only Machine learning Voiding Electronic packaging Flip-chips Underfill...

.... The experiment is done randomly with the same flip-chip type and varied dispensing parameters to minimise bias in the collected data. Each dispensing parameter of the TSAM is recorded and saved, which will be used in the machine learning study. As the cost of the experiment is expensive and the images collected...

...Vicente-Segundo Ruiz-Jacinto; Karina-Silvana Gutiérrez-Valverde; Abrahan-Pablo Aslla-Quispe; José-Manuel Burga-Falla; Aldo Alarcón-Sucasaca; Yersi-Luis Huamán-Romaní Purpose This paper aims to present the novel stacked machine learning approach (SMLA) to estimate low-cycle fatigue (LCF) life...

...Maitri Mistry; Rahul Gupta; Swati Jain; Jaiprakash V. Verma; Daehan Won Purpose The purpose of this paper is to develop a machine learning model that predicts the component self-alignment offsets along the length and width of the component and in the angular direction. To find the best performing...

...Sung Yi; Robert Jones Purpose This paper aims to present a machine learning framework for using big data analytics to predict the reliability of solder joints. The purpose of this study is to accurately predict the reliability of solder joints by using big data analytics. Design/methodology...

... 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. © Emerald Publishing Limited 2019 Emerald Publishing...

... is able to predict the hole-filling in pin-in-paste technology for different through-hole diameters. Originality/value No research works are available in current literature regarding machine learning techniques for pin-in-paste technology. Therefore, we decided to develop a method using decision tree...