dc.description.abstract | With the rapid development of information technology in recent years, many industries around the world are moving toward digital transformation, and many traditional manufacturing industries are also gradually developing Industry 4.0 smart factories. The factories intend to utilize advanced techniques such as the Internet of Things (IoT), big data, machine learning, and cloud computing, to reach the goal of smart manufacturing. In this manner, the product quality is better controlled and the yield rate is improved. This paper focuses on the prediction of the surface roughness (SR) of wire electrical discharge machining (WEDM) workpieces with transfer learning techniques for two different materials, denoted as material-A and material-B. The data of material-A workpiece are regarded as source domain data, whose amount is larger, whereas the data of material-B workpiece are regarded as target domain data, whose amount is smaller.
First, material-A workpiece data are applied to train neural network models for SR prediction. Specifically, static manufacturing parameters are used to train deep neural networks (DNNs) for SR prediction before manufacturing, whereas static manufacturing parameters along with dynamic manufacturing conditions are used to train gate recurrent unit (GRU) networks for SR prediction after manufacturing. Afterwards, two transfer learning methods are utilized. The first method is weight-freezing. It uses a small amount of material-B workpiece data to train a neural network model that can predict surface roughness of material-B workpiece on the basis of well-trained models that can predict surface roughness of material-A workpiece. The second method is multi-task learning. It uses both material-A workpiece data, whose amount is larger, and material-B workpiece data, whose amount is smaller, to train separate neural network models for SR prediction for material-A and material-B workpieces, respectively. The experimental results show that the two transfer learning methods both can efficiently improve prediction accuracy of source domain and target domain neural network models through using only a small amount of target domain data. | en_US |