The revolutionary advances in integrated components in current automotive industry have led to a sharply rising demand for aluminum alloy castings. Targeted quality inspection is thus proposed for components manufacturers to achieve high responsiveness and low operational cost. This suggests casting machine manufacturers to integrate advanced quality prediction functions into the next generation of intelligent casting machines. However, acquiring ample quality inspection data is essential for implementing such functions, which is often challenging, if not infeasible, due to practical issues such as data proprietorship or privacy. Self-training is a good candidate for dealing with scarce labeled data, and XGBoost is commonly used as the base classifier. However, misclassification of unlabeled data happens using XGBoost, which could lead to incorrect pseudo-label assignments, eventually resulting in higher misclassification rate. To address this challenge, a self-training and improved XGBoost-based aluminum alloy casting quality prediction approach is proposed. This approach integrates the classification loss of unlabeled data in the objective function as a new regularization term and considers first and second partial derivatives of the classification loss function for unlabeled data in the leaf node's weight score. The proposed approach penalizes those classification models that misclassify unlabeled data, thereby improves quality prediction performance. To evaluate the effectiveness of our approach, a casting machine manufacturer was collaborated to conduct a case study. The results on three-type casting quality prediction demonstrate that our approach could achieve an accuracy, precision, recall and F1 score of 93.2 %, 90 %, 64.2 %, and 0.75, respectively, outperforming all compared approaches. The approach supports casting machine manufacturers to pre-train a casting quality prediction models with scarce labeled data, enabling swift deployment and customization for targeted quality inspection.

中文翻译：

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基于自训练的方法和改进的 XGBoost 用于铝合金铸件质量预测


当前汽车行业集成部件的革命性进步导致对铝合金铸件的需求急剧增长。因此，为组件制造商提出了有针对性的质量检查，以实现高响应能力和低运营成本。这建议铸造机制造商将先进的质量预测功能集成到下一代智能铸造机中。然而，获取充足的质量检查数据对于实现此类功能至关重要，由于数据所有权或隐私等实际问题，这通常是具有挑战性的，如果不是不可行的话。Self-training 是处理稀缺标记数据的良好候选者，XGBoost 通常用作基本分类器。但是，使用 XGBoost 会对未标记数据进行错误分类，这可能会导致伪标签分配不正确，最终导致更高的错误分类率。为了应对这一挑战，提出了一种基于XGBoost的自训练和改进的铝合金铸件质量预测方法。这种方法将目标函数中未标记数据的分类损失整合为一个新的正则化项，并在叶节点的权重分数中考虑未标记数据的分类损失函数的一阶和二阶偏导数。所提出的方法对那些对未标记数据进行错误分类的分类模型进行了惩罚，从而提高了质量预测性能。为了评估我们方法的有效性，与一家铸造机制造商合作进行了一项案例研究。三种铸件质量预测的结果表明，我们的方法可以实现 93.2 %、90 %、64.2 % 和 0 的准确率、精密度、召回率和 F1 分数。75 个，优于所有比较方法。该方法支持铸造机制造商使用稀缺的标记数据预先训练铸造质量预测模型，从而实现快速部署和定制以实现有针对性的质量检查。