A popular soldering technique for printed circuit boards (PCB) is the so-called surface-mounted technology. After the soldering process an automated optical inspection (AOI) is the common method determining whether a PCB shall go to a manual inspection and rework station (MIS) or can directly go further to the next process step. Thereby, the AOI is a vision-based system deriving user defined physical measurements from a camera image. Based on these pre-defined measurements associated with static specification limits, the AOI labels each inspected soldering spot on a PCB as non-defect or defect. However, a large majority of PCBs are wrongly labelled defect, so-called false calls, causing a major manual labour effort at the MIS. This dataset contains a 132-days recording of PCBs going through the MIS labelled as true defect or false call with the physical measurement by the AOI. Furthermore, the dataset may contain various distribution drifts of unknown type that can be explained by the high sensitivity of electronic production to small external factors that may change unrecognized and additionally the dataset has an unknown percentage of label error due the human labelling process.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10847760PMC
http://dx.doi.org/10.1016/j.dib.2024.110110DOI Listing

Publication Analysis

Top Keywords

automated optical
8
optical inspection
8
surface-mounted technology
8
electronic production
8
data automated
4
inspection surface-mounted
4
technology electronic
4
production popular
4
popular soldering
4
soldering technique
4

Similar Publications

Purpose: To evaluate the posterior scleral stiffness of different regions in high myopic eyes and to explore its associations with macular choroidal and peripapillary retinal nerve fiber layer (pRNFL) thickness and vasculature.

Methods: Thirty subjects with high myopic eyes and 30 subjects with low myopic eyes were included in this study. The elastic modulus of the macular and peripapillary sclera at the temporal, nasal, superior and inferior regions were determined via shear wave elastography (SWE).

View Article and Find Full Text PDF

Background: The increasing bureaucratic burden in everyday clinical practice impairs doctor-patient communication (DPC). Effective use of digital technologies, such as automated semantic speech recognition (ASR) with automated extraction of diagnostically relevant information can provide a solution.

Objective: The aim was to determine the extent to which ASR in conjunction with semantic information extraction for automated documentation of the doctor-patient dialogue (ADAPI) can be integrated into everyday clinical practice using the IVI routine as an example and whether patient care can be improved through process optimization.

View Article and Find Full Text PDF

Purpose: The diagnosis of fungal keratitis using potassium hydroxide (KOH) smears of corneal scrapings enables initiation of the correct antimicrobial therapy at the point-of-care but requires time-consuming manual examination and expertise. This study evaluates the efficacy of a deep learning framework, dual stream multiple instance learning (DSMIL), in automating the analysis of whole slide imaging (WSI) of KOH smears for rapid and accurate detection of fungal infections.

Design: Retrospective observational study.

View Article and Find Full Text PDF

Optical coherence tomography angiography (OCTA) is an emerging, non-invasive technique increasingly utilized for retinal vasculature imaging. Analysis of OCTA images can effectively diagnose retinal diseases, unfortunately, complex vascular structures within OCTA images possess significant challenges for automated segmentation. A novel, fully convolutional dense connected residual network is proposed to effectively segment the vascular regions within OCTA images.

View Article and Find Full Text PDF

SD-LayerNet: Robust and label-efficient retinal layer segmentation via anatomical priors.

Comput Methods Programs Biomed

January 2025

Christian Doppler Laboratory for Artificial Intelligence in Retina, Department of Ophthalmology and Optometry, Medical University of Vienna, Vienna, Austria; Institute of Artificial Intelligence, Center for Medical Data Science, Medical University of Vienna, Vienna, Austria.

Background And Objectives: Automated, anatomically coherent retinal layer segmentation in optical coherence tomography (OCT) is one of the most important components of retinal disease management. However, current methods rely on large amounts of labeled data, which can be difficult and expensive to obtain. In addition, these systems tend often propose anatomically impossible results, which undermines their clinical reliability.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!