Abstract: In order to promote the application of big data technology in manufacturing workshops, aiming at the problem that the temporality and strong noise of massive manufacturing data have an effect on prediction accuracy of cycle time in complex wafer manufacturing process, a cycle time prediction method based on improved particle swarm optimization-long short term memory (IPSO-LSTM) considering feature learning is proposed. A combination of denoising auto-encoder and sparse auto-encoder are used to construct a deep auto-encoder to enhance the abilities of feature learning and noise resisting. IPSO algorithm is used to optimize the parameters of LSTM, overcoming time dependence and improving the performance of the prediction model. Experiments verifies that the prediction accuracy of the proposed approach is better than that of traditional machine learning methods, of which the average absolute error is less than 3%. The effectiveness of the feature learning method is analyzed, which is introduced into traditional prediction methods, such as support vector regression and multi-layer perceptron, resulting an R-square being increased by 1.46% and 1.05%, respectively, and providing a new solution for effective prediction of wafer processing cycle time.

Key words: particle swam optimization algorithm, production cycle time, auto-encoder, long short-term memory network, feature learning