首页|Data from Portland State University Advance Knowledge in Machine Learning (Identification of Solder Joint Failure Modes Using Machine Learning)
Data from Portland State University Advance Knowledge in Machine Learning (Identification of Solder Joint Failure Modes Using Machine Learning)
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Investigators publish new report on Machine Learning. According to news originating from Portland, Oregon, by NewsRx correspondents, research stated, “The reliability of solder joints is one of the most critical factors that determine the lifecycle of electronic devices, and the identification of solder joint failure modes is necessary to enhance the performance and durability of electronic devices. In this study, solder joint failure modes were identified using the fine-tuned visual geometry group 19 (VGG 19) pretrained model.” Financial support for this research came from Ministry of Trade, Industry, and Energy (MOTIE) in South Korea, through the Fostering Global Talents for Innovative Growth Program supervised by the Korea Institute for Advancement of Technology (KIAT). Our news journalists obtained a quote from the research from Portland State University, “Raw images (57 images) were augmented into 428 images by sectioning to classify the solder joint failure mode into two classes (good or not-good mode) for the binary classification model, and 265 not-good data points obtained from the binary classification were employed as input to classify solder joint failure mode into six classes (failure modes 1-6) for the multiclass classification model. The binary and multiclass classification models were trained and validated, achieving 99% accuracy. The binary model classified shadows and small voids as defects, identifying the failure mode as ‘not-good.’ The multiclass model occasionally misclassified the failure modes due to the multiple modes or difficulty in classification. The trained binary and multiclass classification models were further verified using 102 and 64 third-party experimental data points, respectively, confirming 100% accuracy.”
PortlandOregonUnited StatesNorth and Central AmericaCyborgsEmerging TechnologiesMachine LearningPortland State University
NETL
NSTL
IEEE
