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Updated: Jul 27, 2025

Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
Di-CNN:制造业质量预测领域知识信息化的卷积神经网络
Shenghan Guo1, Dali Wang2, Zhili Feng2
1The School of Manufacturing Systems and Networks, Arizona State University, Mesa, AZ 85212, USA.
本研究介绍了Di-CNN,这是一种新的卷积神经网络 (CNN) 模型,它将制造领域的知识与传感器数据集成在一起,以提高质量预测. 与传统的CNN相比,Di-CNN模型显著提高了准确性和可解释性.
科学领域:
- 制造业 工程 制造工程
- 人工智能的人工智能
- 材料科学 材料科学 材料科学
背景情况:
- 卷积神经网络 (CNN) 在制造业中普遍存在,用于使用图像传感器数据进行过程监测和质量预测.
- 纯数据驱动的CNN缺乏物理测量和领域知识的整合,限制了预测准确性和实际解释性.
- 整合制造领域知识对于提高CNN在工业应用中的性能至关重要.
研究的目的:
- 开发一种新的CNN模型,Di-CNN,利用制造领域的知识来提高质量预测的准确性和可解释性.
- 在模型训练期间以适应方式权衡设计阶段信息和实时传感器数据.
- 为了在现实世界制造案例研究中展示拟议的Di-CNN模型的卓越性能.
主要方法:
- 开发了一个新的Di-CNN模型,包括设计阶段的信息 (例如工作条件) 和实时传感器数据.
- 在培训期间,Di-CNN模型通过制造领域的知识来引导这些数据源进行自适应权衡.
- 通过六倍交叉验证,使用平均平方误差 (MSE) 对电阻点质量预测的性能进行了评估.
主要成果:
- 具有适应权重的Di-CNN实现了平均MSE6.8866和中位数MSE6.1916.
- 没有自适应权重的Di-CNN显示出明显更高的误差 (平均MSE:13.6171,中位数MSE:13.1343).
- 传统的CNN表现出最高的误差 (平均MSE:27.2935,中位数MSE:25.6117),证实了拟议模型的优越性.
结论:
- 拟议的Di-CNN模型,集成制造领域的知识和适应权衡数据源,在质量预测方面明显优于传统的CNN.
- 利用领域知识可以提高CNN模型在制造环境中的准确性和可解释性.
- 在复杂的工业过程中,Di-CNN方法为数据驱动的质量预测提供了一个有希望的方向,例如电阻点接.
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