Injection product defect classification method based on machine learning

Abstract

The invention provides an injection product defect classification method based on machine learning. The method comprises the following steps: collecting a training sample; inputting the training sample into a classifier for training to obtain a trained classifier; presetting a shooting time period for the shooting device, acquiring an image of the to-be-classified product and preprocessing the image; inputting an image of a to-be-classified product into the trained classifier to obtain a classification label, and obtaining a label matrix through one-hot coding and transcoding; stacking the label matrix to obtain a stacking matrix containing label information; performing data separation on the stacking matrix to obtain a low-rank matrix and a sparse matrix, and decomposing the sparse matrixinto noise of a training sample and noise of an image of a to-be-classified product; iterating the low-rank matrix, and taking a label matrix corresponding to the iterated optimal solution as an optimized label matrix; and the injection product system converting the optimized label matrix into a machine signal, so that a machine on a production line can control defect classification of injectionproducts.

Description

technical field [0001] The present invention relates to the technical field of product defect classification, and more specifically, relates to a method for classifying defects of injection molding products based on machine learning. Background technique [0002] At present, the defects of injection molding machine products are mainly caused by the design of the mold, the manufacturing accuracy and the degree of wear. Since the injection molding cycle itself is very short, if the process conditions are not well grasped, a large amount of waste products will be generated. [0003] There is an existing intelligent secondary detection method for injection molding products, which mainly judges whether the product is qualified by comparing preset image information, and then performs secondary detection on unqualified products to avoid false subtraction or missed detection. In this injection molding product detection method, although the secondary detection of unqualified products...

AI Technical Summary

Problems solved by technology

In this injection molding product detection method, although the secondary detection of unqualified products helps to improve the detection accuracy, it increases the time cost

Another type of classification for injection molding products is based on physical judgment of whether the shapes are consistent. Therefore, there is a shortcoming of too single classification. When there are cases where the classified products have the same shape, effective classification cannot be carried out.

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