论文标题
注入成型的有缺陷零件的质量分类
Quality Classification of Defective Parts from Injection Moulding
论文作者
论文摘要
该报告研究了机器学习算法，用于检测通过注射成型产生的塑料零件的短形成和编织。通过使用验证的模型并在我们的494个150 x 150个像素图像的样本的数据集上实现了转移学习。测试的模型是Xception，InceptionV3和Resnet-50。 Xception显示出最高的总体准确性（86.66％），其次是InceptionV3（82.47％）和Resnet-50（80.41％）。短形成是最简单的故障，每个模型的F1得分最高。
This report examines machine learning algorithms for detecting short forming and weaving in plastic parts produced by injection moulding. Transfer learning was implemented by using pretrained models and finetuning them on our dataset of 494 samples of 150 by 150 pixels images. The models tested were Xception, InceptionV3 and Resnet-50. Xception showed the highest overall accuracy (86.66%), followed by InceptionV3 (82.47%) and Resnet-50 (80.41%). Short forming was the easiest fault to identify, with the highest F1 score for each model.
