论文标题
AI增强数字金属组件
AI Augmented Digital Metal Component
论文作者
论文摘要
这项工作的目的是提出一种新的范式，该范式通过金属添加剂制造和人工智能（AI）的融合来赋予金属零件。我们的数字金属部分通过卷积神经网络（CNN）通过实时数据处理对状态进行分类。 CNN的训练数据是从激光粉末床融合过程中嵌入金属零件的应变表中收集的。我们使用添加剂制造实施了这种方法，展示了一个自我认知金属部分，识别部分螺钉松动，故障和外部影响对象。结果表明，金属零件可以通过重复态度识别多个固定状态的细微变化，并在测试集中精度为89.1％。提出的策略显示出有望有助于下一代数字金属机械系统的超连接性的潜力
The aim of this work is to propose a new paradigm that imparts intelligence to metal parts with the fusion of metal additive manufacturing and artificial intelligence (AI). Our digital metal part classifies the status with real time data processing with convolutional neural network (CNN). The training data for the CNN is collected from a strain gauge embedded in metal parts by laser powder bed fusion process. We implement this approach using additive manufacturing, demonstrate a self-cognitive metal part recognizing partial screw loosening, malfunctioning, and external impacting object. The results indicate that metal part can recognize subtle change of multiple fixation state under repetitive compression with 89.1% accuracy with test sets. The proposed strategy showed promising potential in contributing to the hyper-connectivity for next generation of digital metal based mechanical systems
