A.I. Algo

Good/Bad Classification

This algorithm classifies “good” from “defective” and is used in artificial intelligence and machine vision to identify and distinguish objects or products that meet established quality criteria (good) from those with defects or that do not meet required standards (defective).

LEARNING

The main goal is to analyze the features and properties of objects or products through image processing, assigning them to the “good” or “defective” category based on specific evaluation criteria.

AUTOMATIC LEARNING MODELS

Anomaly Detection uses machine learning models, such as neural networks or supervised learning algorithms, to train a classifier capable of recognizing and distinguishing features associated with good or defective products.

REAL-TIME CLASSIFICATION

Once trained, the Anomaly Detection algorithm can be applied to classify new objects or products in real-time. The algorithm processes the image or product data, evaluates relevant features, and assigns a classification of “good” or “defective” based on the thresholds or decision criteria defined during the training phase.

Applications

VISUAL INSPECTION

Visual inspection of materials, such as metal sheets or electronic components, can use the algorithm to identify defects or contaminations, classifying areas with defects as defective.

PRODUCTION CONTROL

The algorithm can be used to automatically identify defective or non-compliant products. For example, in an electronic component manufacturing company, the algorithm can detect faulty soldering on motherboards or damaged components.

AUTOMATIC SEPARATION

The algorithm can automatically classify products based on their quality or compliance. For example, a fruit production line can automatically separate defective or unripe fruits from high-quality ones.
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