Sentence examples for classification of defect from inspiring English sources

Defect detection and classification algorithm processing time can broadly be divided in two parts: a) time taken by the system from capturing an image to identification of RoI and b) feature extraction and classification of defect type from identified RoI.

In order to improve the comprehensive performance of solder joints inspection in three aspects, i.e. high recognition rate, detailed classification of defect types and fast inspection speed, a new detection and classification algorithm of the chip solder joints based on color grads and Boolean rules is developed in this paper.

Classification of defects in steel surfaces is important for identifying and subsequently correcting causative factors.

It is shown that all the known types of defects are naturally included in the presented classification of defects.

A formal generalization of classification of defects is developed to include the defects of arbitrary finite level.

Within three sets of trials, best result from MLP was obtained (97%/93% for training/test set) during classification of defects from non-defects.

Although an ultrasonic wave has the capability to detect defects, the type classification of defects and material characterization of components continue to be challenging tasks.

To solve this problem, we describe the definition of each type of defect (as defined in Table 1) so practitioners can understand the classification of defects that the technique helps to identify.

This article is divided into two main parts: (i) a possible exhaustive classification of defects in cutting inserts and (ii) the design of an automated sensor to recognise defects and to measure wear.

This paper proposes a new neuro-fuzzy classifier that combines neural networks and concepts of fuzzy logic for the classification of defects by extracting features in segmented buried pipe images.

The classification of defects and the recording of characteristic information are shown in Table 2. Table 2 Defect characteristic information Defect Defect types Size Rectangle degree Ratio of length to width Induced density A Scar 537 0.623 1.582 7.046 B Crackle 204 0.451 18.696 28.961 C Scar 186 0.745 18693 5.864.