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.

中文翻译：

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注塑不良品的质量分类

本报告研究了机器学习算法，用于检测注塑成型生产的塑料部件中的短成型和编织。迁移学习是通过使用预训练模型并在我们的 150 x 150 像素图像的 494 个样本数据集上对它们进行微调来实现的。测试的模型是 Xception、InceptionV3 和 Resnet-50。Xception 的总体准确率最高（86.66%），其次是 InceptionV3（82.47%）和 Resnet-50（80.41%）。短成形是最容易识别的故障，每个模型的 F1 分数最高。