Comprehensive correlation of inline inspection data for the evaluation of defects in heterogeneous electronic assemblies

Härter, Stefan; Klinger, Tobias; Franke, Jörg; Beer, Detlef

doi:10.1109/panpacific.2016.7428408

Cited by 8 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Out of 21 articles deemed relevant from the 12 academic database searches, only one directly attempted to employ SPI parametric data with downline defects, using ANOVA-style statistical analysis to correlate SPI and PCB attribute data with defects identified at the AOI station [11]. The distinction between [11] and this research is that this research is concerned with ICT defects, not AOI defects. Additionally, the SPI data explored in this research is not attributional but rather parametric.…”

Section: Discussion Of Related Workmentioning

confidence: 99%

Predicting contact-without-connection defects on printed circuit boards employing ball grid array package types: a data analytics case study in the smart manufacturing environment

2020

View full text Add to dashboard Cite

This research presents an exploratory data analytics case study in defect prediction on printed circuit boards (PCB) employing ball grid array (BGA) package types during assembly. BGA package types are of interest because defects are difficult to identify and costly to rework. While much of the existing research is dedicated to techniques to identify and diagnose BGA defects, this research attempts to preempt them by using parametric data measured by solder paste inspection (SPI) machines as input data to applied machine learning models. Two modeling approaches are explored: one approach to analyze individual solder paste deposits and the other approach to holistically analyze all solder paste deposits on a single PCB location. The latter approach employs feature generation to extract a broad set of features from the arrays of SPI data and feature selection techniques for dimensionality reduction. Models trained on the reduced feature sets provide encouraging initial results, with precision, recall, and f1 score metrics exceeding 0.82, 0.50, and 0.62 respectively for each of two datasets analyzed.

show abstract

Section: Discussion Of Related Workmentioning

confidence: 99%

Predicting contact-without-connection defects on printed circuit boards employing ball grid array package types: a data analytics case study in the smart manufacturing environment

2020

View full text Add to dashboard Cite

show abstract

“…Such failures could make the electronic device dysfunctional, thus those PCBs after SMT process need to be examined, to guarantee that those electronic components are correctly installed on the PCB. A number of approaches are proposed for examining the PCB, including X-ray based approach [2], infrared thermal signatures based approach [3], correlation of inline data based approach [4] and automatic optical inspection (AOI) based approach [5]. Among these methods, AOI has been proved effective and efficient thus widely used in practical factories.…”

Section: Introductionmentioning

confidence: 99%

Improving accuracy of automatic optical inspection with machine learning

Tong

Tian

et al. 2021

Front. Comput. Sci.

View full text Add to dashboard Cite

Electronic devices require the printed circuit board (PCB) to support the whole structure, but the assembly of PCBs suffers from welding problem of the electronic components such as surface mounted devices (SMDs) resistors. The automated optical inspection (AOI) machine, widely used in industrial production, can take the image of PCBs and examine the welding issue. However, the AOI machine could commit false negative errors and dedicated technicians have to be employed to pick out those misjudged PCBs. This paper proposes a machine learning based method to improve the accuracy of AOI.In particular, we propose an adjacent pixel RGB value based method to pre-process the image from the AOI machine and build a customized deep learning model to classify the image. We present a practical scheme including two machine learning procedures to mitigate AOI errors. We conduct experiments with the real dataset from a production line for three months, the experimental results show that our method can reduce the rate of misjudgment from 0.3%-0.5% to 0.02%-0.03%, which is meaningful for thousands of PCBs each containing thousands of electronic components in practice.

show abstract