Combining acti v e learning and self-paced learning for cost-effecti v e process design intents extraction of process data

Abstract

With the widespread use of computer-aided technologies like C AD/C AM/C APP 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 kno wledge. Ho wever, a lot of labeled data are needed to train a deep learning model for effecti v el y extracting the embedded knowledge and experiences within these process data and the labeling of process data is quite expensi v e 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 pr e-la beled 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 man ual-la bels and the high confidence samples with pseudo-la bels ar e incorpor ated into the tr aining data for retr aining 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.