Inductance Defect Identification Method
A defect identification and inductance technology, which is applied in the field of visual recognition of inductance surface defects, can solve the problems of poor inductance detection accuracy and achieve the effects of reducing resource waste, high detection accuracy, and easy implementation
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Embodiment 1
[0050] Carry out intelligent identification based on all the collected data of the key inspection surface, and classify and sort the defects of the detected inductors.
[0051] That is, all the individual data are intelligently identified, and the single data can be distinguished from qualified and unqualified according to the single data detection at most, and the screening is very delicate.
[0052] Under normal circumstances, good and bad products are screened according to the classification of attributes, which can be divided into single-item defect categories for each attribute category, unqualified categories for at least two types of defects, and good products for all individual items. kind.
[0053] Since there are many detection items, you can choose to deploy them more arbitrarily, and perform more detailed classification and screening.
Embodiment 2
[0055] Carry out intelligent identification according to the data collection sequence of the key detection surface, and classify and sort the good and bad products of the detected inductance.
[0056] That is, there is no need to intelligently identify all the collected data, and intelligent identification is carried out according to the sequence of the collected data. When there are identification defects in the previously collected data, it is directly judged as defective products and eliminated. This can greatly improve the screening speed and ensure the screening at the same time quality.
[0057] In general, the more data collected, the greater the range of intelligent identification of single data released.
[0058] Specifically, when the detection data is less, the tolerance range of individual data will be smaller. For example, when only the crack area data is detected, it may be considered as a defective product if it is controlled at 10 units. In this case, it can be...
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