Online Probabilistic Extreme Learning Machine for Distribution Modeling of Complex Batch Forging Processes
Xinjiang Lu, Chang Liu, Minghui Huang · IEEE Transactions on Industrial Informatics · 2015
An effective model of batch forging processes is crucial to ensure the quality conformance control of batch productions. However, obtaining this model has proven difficult due to a variety of the raw forgings produced by manufacturing error, material variation, geometric defects, etc. In this paper, a novel online probabilistic extreme learning machine (ELM) is proposed to model batch forging processes. A probabilistic ELM is first developed to extract the distribution information of the batch forging processes from the data. Due to the highly linear structure of the ELM, the stochastic property of the forging process is easily derived and processed. By using the characteristics of the online ELM, a strategy is then developed to update the distribution model as new forging process data are collected. Finally, case studies on complex batch forging processes demonstrate the effectiveness of the proposed online probabilistic ELM.