'Using Unsupervised Learning to Detect Defects in Manufacturing' artificialintelligence machinelearning
Such examples can be found in the food industry, textile, electronics, heavy manufacturing, and other branches. But, there are some specific problems in how we can approach defect detection algorithms with traditional machine learning.
In terms of a production line, collecting and labeling data can be impossible, since there is no way we can gather all the variations of cracks or dents on a product to ensure accurate detection by the model.
Concerning defect detection, this helps to resolve the problem with data labeling and collecting huge amounts of samples. So let’s see how unsupervised learning methods can be used for defect detection model training.Defect detection relates to the problem of anomaly detection in machine learning. While we don’t rely on labeling, there are other approaches in unsupervised learning that aim at grouping data and providing hints to the model.is grouping unlabeled examples according to similarity.
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