Press Tonnage Signal Decomposition and Validation Analysis for Transfer or Progressive Die Processes
نویسندگان
چکیده
A transfer or progressive die process consists of multiple stations working simultaneously in each stroke. This paper aims to develop a new methodology that can decompose press tonnage signals to obtain individual station signals without using in-die sensors. In the paper, two different tonnage decomposition tests, as well as the associated data analysis algorithms, are developed. Statistical profile analyses and an in-die sensor test were conducted to validate the proposed methodology. @DOI: 10.1115/1.1831287#
منابع مشابه
Individual Station Monitoring Using Press Tonnage Sensors for Multiple Operation Stamping Processes
In multiple operation stamping processes, a press tonnage signal measured by press tonnage sensors installed on press linkages/uprights, is the summation of die forces at all stations. Different from the current practice of using a whole cycle of press tonnage signals to monitor the compound condition of all stations, this paper proposes a new method to use the partitioned monitoring segments o...
متن کاملAutomatic Tonnage Monitoring for Missing Part Detection in Multi-Operation Forging Processes
In multi-operation forging processes, the process fault due to missing parts from dies is a critical concern. The objective of this paper is to develop an effective method for detecting missing parts by using automatic classification of tonnage signals during continuous production. In this paper, a new feature selection and hierarchical classification method is developed to improve the classifi...
متن کاملComparative Analysis of Wavelet-based Feature Extraction for Intramuscular EMG Signal Decomposition
Background: Electromyographic (EMG) signal decomposition is the process by which an EMG signal is decomposed into its constituent motor unit potential trains (MUPTs). A major step in EMG decomposition is feature extraction in which each detected motor unit potential (MUP) is represented by a feature vector. As with any other pattern recognition system, feature extraction has a significant impac...
متن کاملOnline Multichannel Forging Tonnage Monitoring and Fault Pattern Discrimination Using Principal Curve
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 ...
متن کاملA Fault Diagnosis Method for Automaton based on Morphological Component Analysis and Ensemble Empirical Mode Decomposition
In the fault diagnosis of automaton, the vibration signal presents non-stationary and non-periodic, which make it difficult to extract the fault features. To solve this problem, an automaton fault diagnosis method based on morphological component analysis (MCA) and ensemble empirical mode decomposition (EEMD) was proposed. Based on the advantages of the morphological component analysis method i...
متن کامل