| dc.description.abstract | The cyber-physical system of the manufacturing industry will build a numerical model for
prediction in the cloud, and then will train the numerical model with the data collected automatically
from the factory, and continuously update the model. To avoid the inaccurate predictions caused by
the data poisoning of the updated numerical model due to the mixing of the wrong data with the
correct data, an error data filter is needed to classify the data and maintain the performance of the
cyber-physical system.
The filter in this study consists of Auto-Encoder and a classifier. First, use the correct data to
train the front-end Auto-Encoder so that it can initially identify correct and incorrect data. Then, the
classifier is built at the back-end to distinguish the correctness of the data, which consists of Support
Vector Machines, Random Forest, a K-Nearest Neighbor and Ensemble Learning. The input data of
the classifier includes the input and output of the Auto-Encoder, and can also include quantifiers of
the difference between them. Finally, the classifier classifies the data into correct or incorrect
categories. Only correct data can be used for updating the numerical model of the cyber-physical
system.
In this study, two process cases are used to validate and adapt the research method; the first case
is Laser Direct Metal Deposition, and the second case is Modeling and Parameter Optimization of 3D
Printing Process with Bio-material. The impact of data poisoning on the numerical model was
evaluated, and then the method developed in this study was used to filter out the wrong data and
analyze the classification; true positive and false positive were the key indicator of data poisoning.
The results show that the classification accuracy rate of this study is greater than 94.7%, and the error
rate is less than 3.3%. Therefore, the method proposed in this study can indeed avoid the influence of
data poisoning on the cyber-physical model.
| en_US |