Online Multichannel Forging Tonnage Monitoring and Fault Pattern Discrimination Using Principal Curve

Author:

Kim Jihyun1,Huang Qiang2,Shi Jianjun3,Chang Tzyy-Shuh4

Affiliation:

1. Research Institute for Information and Communication Technology, Korea University, Seoul, Korea

2. Department of Industrial and Management Systems Engineering, The University of South Florida, Tampa, FL 33620

3. Department of Industrial and Operations Engineering, The University of Michigan, Ann Arbor, MI 48109

4. OG Technologies, Inc., 58 Parkland Plaza Suite 200, Ann Arbor, MI 48103

Abstract

Due to the late response to process condition changes, forging processes are normally exposed to a large number of defective products. To achieve online process monitoring, multichannel tonnage signals are often collected from the forging press. The tonnage signals contain significant amount of real time information regarding the product and the process conditions. In this paper, a methodology is developed to detect profile changes of multichannel tonnage signals for forging process monitoring and to classify fault patterns. The changes include global or local profile deviations, which correspond to deviations of a whole process cycle or process segment(s) within a cycle, respectively. The principal curve method is used to conduct feature extraction and discrimination of tonnage signals. The developed methodology is demonstrated with industry data from a crankshaft forging processes.

Publisher

ASME International

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Control and Systems Engineering

Reference16 articles.

1. Seem, J. E., and Knussmann, K. D., 1994, “Statistical Methods for On-Line Fault Detection in Press-Working Applications,” Signature Technology Technical Report.

2. Signature-Based Process Control & SPC Trending Evaluate Press Performance;Robbins;Metal Forming

3. Tonnage Signature Analysis Using the Orthogonal (Harr) Transforms;Koh;NAMRI/SME Transactions

4. Automatic Feature Extraction for In-Process Diagnostic Performance Improvement;Jin;J. Intell. Manuf.

5. Forging Process Monitoring Through Multivariate Analysis of Tonnage Signals;Kim

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