scholarly journals Analysis of a Sound Signal for Quality Monitoring in Laser Microlap Welding

2020 ◽  
Vol 10 (6) ◽  
pp. 1934
Author(s):  
Bo-Si Kuo ◽  
Ming-Chyuan Lu
Keyword(s):  
Standard Deviation ◽  
Monitoring System ◽  
Signal Analysis ◽  
Joint Strength ◽  
Metal Sheet ◽  
Sound Signal ◽  
Welding Quality ◽  
Signal Features ◽  
Metal Sheets ◽  
Laser Microwelding

This study focused on correlation analysis between welding quality and sound-signal features collected during microlaser welding. The study provides promising features for developing a monitoring system that detects low joint strength caused by a gap between metal sheets after welding. To obtain sound signals for signal analysis and develop the monitoring system, experiments for laser microlap welding were conducted on a laser microwelding platform by installing a microelectromechanical system (MEMS) microphone away from the welding point, and an acoustic emission (AE) sensor on the fixture. The gap between two metal sheet layers was controlled using clamp force, a pressing bar, and the appropriate installation of a thin piece of paper between the metal sheets. After sound signals from the microphone were collected, the correlation between features of time-domain sound signals and of welding quality was analyzed by categorizing the referred signals into eight sections during welding. After appropriately generating the features after signal analysis and selecting the most promising features for low-joint-strength monitoring on the basis of scatter index J, a hidden Markov model (HMM)-based classifier was applied to evaluate the performance of the selected sound-signal features. Results revealed that three sound-signal features were closely related to joint-strength variation caused by the gap between two metal-sheet layers: (1) the root-mean-square (RMS) value of the first section of sound signals, (2) the standard deviation of the first section of sound signals, and (3) the standard deviation to the RMS ratio of the second section of sound signals. In system evaluation, a 100% classification rate was obtained for normal and low-bonding-strength monitoring when the HMM-based classifier was developed on the basis of the three selected features.

scholarly journals Analysis of Acoustic Emission (AE) Signals for Quality Monitoring of Laser Lap Microwelding

2021 ◽  
Vol 11 (15) ◽  
pp. 7045
Author(s):  
Ming-Chyuan Lu ◽  
Shean-Juinn Chiou ◽  
Bo-Si Kuo ◽  
Ming-Zong Chen
Keyword(s):  
Stainless Steel ◽  
Acoustic Emission ◽  
Frequency Domain ◽  
Monitoring System ◽  
Welding Quality ◽  
Steel Sheets ◽  
Signal Features ◽  
Ae Signal ◽  
Laser Microwelding ◽  
Ae Signals

In this study, the correlation between welding quality and features of acoustic emission (AE) signals collected during laser microwelding of stainless-steel sheets was analyzed. The performance of selected AE features for detecting low joint bonding strength was tested using a developed monitoring system. To obtain the AE signal for analysis and develop the monitoring system, lap welding experiments were conducted on a laser microwelding platform with an attached AE sensor. A gap between the two layers of stainless-steel sheets was simulated using clamp force, a pressing bar, and a thin piece of paper. After the collection of raw signals from the AE sensor, the correlations of welding quality with the time and frequency domain features of the AE signals were analyzed by segmenting the signals into ten 1 ms intervals. After selection of appropriate AE signal features based on a scatter index, a hidden Markov model (HMM) classifier was employed to evaluate the performance of the selected features. Three AE signal features, namely the root mean square (RMS) of the AE signal, gradient of the first 1 ms of AE signals, and 300 kHz frequency feature, were closely related to the quality variation caused by the gap between the two layers of stainless-steel sheets. Classification accuracy of 100% was obtained using the HMM classifier with the gradient of the signal from the first 1 ms interval and with the combination of the 300 kHz frequency domain signal and the RMS of the signal from the first 1 ms interval.

scholarly journals Research on coal-rock recognition based on sound signal analysis

2018 ◽  
Vol 232 ◽  
pp. 04075
Author(s):  
Xihui Chen ◽  
Zenan Yang ◽  
Gang Cheng
Keyword(s):  
Signal Analysis ◽  
Singular Values ◽  
Sound Signal ◽  
Cutting Condition ◽  
Personal Safety ◽  
Signal Features ◽  
Mode Decomposition ◽  
Coal Rock ◽  
Learning Machine ◽  
Value Decomposition

The recognition of the cutting state of shearer is the key technology to realize variable speed cutting and mining automation. It is of great significance for improving shearer reliability, ensuring personal safety and improving coal quality. This paper proposed a coal-rock recognition method based on sound signal analysis. The original sound signal produced during the cutting process of shearer is decomposed by variational mode decomposition (VMD), and the obtained IMFs can construct a signal matrix. The signal matrix is processed by singular value decomposition (SVD), and a series of singular values can be obtained and defined as the signal features. Finally, the coal-rock recognition is realized by extreme learning machine (ELM) based on the extracted signal features. The experiment results show that the overall recognition accuracy is 91.7% under the actual cutting condition, which verifies the effectiveness of the proposed method in coal-rock recognition, and lays a theoretical foundation for the automation and intellectualization of shearer mining.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

scholarly journals Intelligent Starting Current-Based Fault Identification of an Induction Motor Operating under Various Power Quality Issues

Energies ◽  
2021 ◽  
Vol 14 (2) ◽  
pp. 304
Author(s):  
Sakthivel Ganesan ◽  
Prince Winston David ◽  
Praveen Kumar Balachandran ◽  
Devakirubakaran Samithas
Keyword(s):  
Standard Deviation ◽  
Power Quality ◽  
Monitoring System ◽  
Condition Monitoring ◽  
Classification Accuracy ◽  
Induction Motors ◽  
Quality Data ◽  
Starting Current ◽  
Quality Issues ◽  
Condition Monitoring System

Since most of our industries use induction motors, it is essential to develop condition monitoring systems. Nowadays, industries have power quality issues such as sag, swell, harmonics, and transients. Thus, a condition monitoring system should have the ability to detect various faults, even in the presence of power quality issues. Most of the fault diagnosis and condition monitoring methods proposed earlier misidentified the faults and caused the condition monitoring system to fail because of misclassification due to power quality. The proposed method uses power quality data along with starting current data to identify the broken rotor bar and bearing fault in induction motors. The discrete wavelet transform (DWT) is used to decompose the current waveform, and then different features such as mean, standard deviation, entropy, and norm are calculated. The neural network (NN) classifier is used for classifying the faults and for analyzing the classification accuracy for various cases. The classification accuracy is 96.7% while considering power quality issues, whereas in a typical case, it is 93.3%. The proposed methodology is suitable for hardware implementation, which merges mean, standard deviation, entropy, and norm with the consideration of power quality issues, and the trained NN proves stable in the detection of the rotor and bearing faults.

Quality Monitoring System for Seismic While Drilling

2013 ◽  
Vol 318 ◽  
pp. 572-575
Author(s):  
Li Li Yu ◽  
Yu Hong Li ◽  
Ai Feng Wang
Keyword(s):  
Data Acquisition ◽  
Monitoring System ◽  
Signal Analysis ◽  
Quality Evaluation ◽  
Quality Monitoring ◽  
Quality Of Data ◽  
Whole Process ◽  
Engineering Parameters ◽  
Network Status

In this paper a quality monitoring system for seismic while drilling (SWD) that integrates the whole process of data acquisition was developed. The acquisition equipment, network status and signals of accelerometer and geophone were monitored real-time. With fast signal analysis and quality evaluation, the acquisition parameters and drilling engineering parameters can be adjusted timely. The application of the system can improve the quality of data acquisition and provide subsequent processing and interpretation with high qualified reliable data.

Laser beam welding quality monitoring system based in high-speed (10 kHz) uncooled MWIR imaging sensors

2015 ◽  
Author(s):  
Rodrigo Linares ◽  
German Vergara ◽  
Raúl Gutiérrez ◽  
Carlos Fernández ◽  
Víctor Villamayor ◽  
...  
Keyword(s):  
Laser Beam ◽  
Monitoring System ◽  
High Speed ◽  
Laser Beam Welding ◽  
Quality Monitoring ◽  
Welding Quality ◽  
Imaging Sensors

Incremental Roller-Flanging of Thick Metal Sheets

2021 ◽  
Vol 883 ◽  
pp. 209-216
Author(s):  
Andrea Ghiotti ◽  
Benvenuto Mattia del Tito ◽  
Enrico Simonetto ◽  
Stefania Bruschi ◽  
Stefano Filippi
Keyword(s):  
Metal Forming ◽  
Incremental Forming ◽  
Single Point ◽  
Metal Sheet ◽  
High Tech ◽  
Economic Competitiveness ◽  
Sheet Surface ◽  
Large Size ◽  
Metal Sheets ◽  
Innovative Concept

Metal forming industry is frequently characterized by the demand of small-batch productions to manufacture highly customized products. Apart from the accuracy that is mandatory in high-tech applications, one of the main requirements remains the economic competitiveness that becomes critical in the case of the deformation of thick metal sheets due to the relevant forming loads and the large size of the machines that are required to perform such processes. These problems are partially solved by using incremental forming approaches, in which the deformation is gradually performed by the use of one (single point) or two (double-sided) tools that are usually made to slide on the metal sheet surface while they impose the desired deformation. The paper aims at introducing an innovative concept of incremental forming machine to perform double-sided incremental bends, specifically developed for thick metal sheets. The increased flexibility and the possibility to manufacture sound parts with reduced bending forces are shown and discussed.

scholarly journals Tool wear estimation in turning of Inconel 718 based on wavelet sensor signal analysis and machine learning paradigms

2020 ◽  
Vol 14 (5-6) ◽  
pp. 693-705
Author(s):  
Tiziana Segreto ◽  
Doriana D’Addona ◽  
Roberto Teti
Keyword(s):  
Machine Learning ◽  
Tool Wear ◽  
Inconel 718 ◽  
Signal Analysis ◽  
Elevated Temperatures ◽  
Wavelet Packet ◽  
Research Work ◽  
High Strength ◽  
Signal Features ◽  
Tool Wear Estimation

AbstractIn the last years, hard-to-machine nickel-based alloys have been widely employed in the aerospace industry for their properties of high strength, excellent resistance to corrosion and oxidation, and long creep life at elevated temperatures. As the machinability of these materials is quite low due to high cutting forces, high temperature development and strong work hardening, during machining the cutting tool conditions tend to rapidly deteriorate. Thus, tool health monitoring systems are highly desired to improve tool life and increase productivity. This research work focuses on tool wear estimation during turning of Inconel 718 using wavelet packet transform (WPT) signal analysis and machine learning paradigms. A multiple sensor monitoring system, based on the detection of cutting force, acoustic emission and vibration acceleration signals, was employed during experimental turning trials. The detected sensor signals were subjected to WPT decomposition to extract diverse signal features. The most relevant features were then selected, using correlation measurements, in order to be utilized in artificial neural network based machine learning paradigms for tool wear estimation.

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