Combining Active Learning and Self-Paced Learning for Cost-Effective Process Design Intents Extraction of Process Data

Abstract

With the widespread use of computer-aided technologies like CAD/CAM/CAPP in the product manufacturing process, a large amount of process data is constantly generated, and data-driven process planning has shown promising potentials for effectively reusing the process knowledge. However, a lot of labeled data are needed to train a deep learning model for effectively extracting the embedded knowledge and experiences within these process data, and the labeling of process data is quite expensive and time-consuming. This paper proposes a cost-effective process design intents extraction approach for process data by combining active learning (AL) and self-paced learning (SPL). First, the process design intents inference model based on Bi-LSTM is generated by using a few pre-labeled samples. Then, the prediction uncertainty of each unlabeled sample is calculated by using a Bayesian neural network, which can assist in the identification of high confidence samples in SPL and low confidence samples in AL. Finally, the low confidence samples with manual-labels and the high confidence samples with pseudo-labels are incorporated into the training data for retraining the process design intents inference model iteratively until the model attains optimal performance. The experiments demonstrate that our approach can substantially decrease the number of labeled samples required for model training, and the design intents in the process data could be inferred effectively with dynamically undated training data.