Online Probabilistic Extreme Learning Machine for Distribution Modeling of Complex Batch Forging Processes
Online Probabilistic Extreme Learning Machine for Distribution Modeling of Complex Batch Forging Processes
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DOI：
10.1109/tii.2015.2479852
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发表时间：
2015-09
影响因子：
12.3
通讯作者：
Xinjiang Lu;Chang Liu;Minghui Huang
中科院分区：
文献类型：
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作者：
Xinjiang Lu;Chang Liu;Minghui Huang
建立有效的批量锻造过程模型是保证批量产品质量一致性控制的关键。然而，获得这个模型已被证明是困难的，由于各种原锻件的制造误差，材料变化，几何缺陷，在本文中，提出了一种新的在线概率极端学习机（ELM）批量锻造过程建模。首先提出了一种概率ELM方法，用于从数据中提取批量锻造过程的分布信息。由于ELM的高度线性结构，锻造过程的随机特性很容易推导和处理。通过使用在线ELM的特点，然后开发一种策略，更新的分布模型，新的锻造工艺数据收集。最后，复杂批量锻造过程的案例研究表明，所提出的在线概率ELM的有效性。
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.