... machine learning ( ML ) methodologies are transforming solder joint engineering by enabling accelerated materials discovery, predictive reliability assessment and microstructure-informed design. Design/methodology/approach This paper provides a comprehensive synthesis of recent advances in ML...

...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...

... Combining (3b) and (4) will yield the use case for all the three models in the masking process: 14 08 2024 09 09 2024 11 09 2024 © Emerald Publishing Limited 2024 Emerald Publishing Limited Licensed re-use rights only Machine learning Voiding Electronic packaging Flip...

... Licensed re-use rights only Electronic packaging Flip-chips Underfill encapsulation Voiding Machine learning BJIM USMIndustry Matching Research 1001.PMEKANIK.8070022 USM-Western Digital Corp 311/PMEKANIK/4402055 The miniaturisation of components such as a flip-chip uses...

...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. (2) N = S α ⋅ [ dim ⁡ ( I ) + dim...

... 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...