Family learning: A process modeling method for cyber-additive manufacturing network
Family learning: A process modeling method for cyber-additive manufacturing network
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DOI：
10.1080/24725854.2020.1851824
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发表时间：
2021-02
影响因子：
2.6
通讯作者：
Lening Wang;Xiaoyu Chen;D. Henkel;R. Jin
中科院分区：
文献类型：
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作者：
Lening Wang;Xiaoyu Chen;D. Henkel;R. Jin
摘要网络增材制造网络（CAMNet）将连接的增材制造过程与先进的数据分析集成为计算服务，以支持个性化的产品实现。然而，高度个性化的产品设计（例如，几何形状）限制了每个设计的样本大小，这可能导致计算服务的精度不令人满意，例如，质量建模的预测精度较低。受建模挑战的启发，我们提出了一个数据驱动的模型，称为家庭学习，通过量化这些产品在CAMNet中的共享信息，共同建模相似但不相同的产品作为家庭成员。具体地，通过基于设计因素优化相似度生成模型来估计每个产品的共享信息量，这直接提高了族学习模型的预测精度。所提出的方法的优点，说明了选择性激光熔化过程的模拟和真实的案例研究。这种族学习方法可以广泛应用于具有相似但不相同的连接系统的网络中的数据驱动建模。
Abstract A Cyber-Additive Manufacturing Network (CAMNet) integrates connected additive manufacturing processes with advanced data analytics as computation services to support personalized product realization. However, highly personalized product designs (e.g., geometries) in CAMNet limit the sample size for each design, which may lead to unsatisfactory accuracy for computation services, e.g., a low prediction accuracy for quality modeling. Motivated by the modeling challenge, we proposed a data-driven model called family learning to jointly model similar-but-non-identical products as family members by quantifying the shared information among these products in the CAMNet. Specifically, the amount of shared information for each product is estimated by optimizing a similarity generation model based on design factors, which directly improve the prediction accuracy for the family learning model. The advantages of the proposed method are illustrated by both simulations and a real case study of the selective laser melting process. This family learning method can be broadly applied to data-driven modeling in a network with similar-but-non-identical connected systems.