Application of selected method of anomaly detection in signals acquired during welding process monitoring

2017 ◽  
Vol 54 (4) ◽  
pp. 249 ◽  
Author(s):  
Anna Bzymek
Keyword(s):  
Anomaly Detection ◽  
Process Monitoring ◽  
Welding Process

Process Monitoring in Ultrasonic Metal Welding of Lithium Batteries by Power Signals

2021 ◽  
pp. 1-22
Author(s):  
Xinhua Shi ◽  
Lin Li ◽  
Suiran Yu ◽  
Lingxiang Yun
Keyword(s):  
Electric Power ◽  
Process Monitoring ◽  
Lithium Batteries ◽  
Welding Process ◽  
Ultrasonic Metal Welding ◽  
Isometric Transformation ◽  
Welding Defects ◽  
Different Types ◽  
Metal Welding ◽  
Welding Processes

Abstract Ultrasonic metal welding is one of the key technologies in manufacturing lithium batteries, and the welding quality directly determines the battery performance. Therefore, an online welding process monitoring system is critical in identifying abnormal welding processes, detecting defects, and improving battery quality. Traditionally, the peak welding power is used to indicate abnormal process signals in welding process monitoring systems. However, since various factors have complex impacts on the electric power signals of ultrasonic welding processes, the peak power is inadequate to detect different types of welding defects. Therefore, a signal pattern matching method is proposed in this study, which is based on the electric power signal during the entire welding process and thus is capable of identifying abnormal welding processes in various conditions. The proposed method adopts isometric transformation and homogenization as signal pretreatment methods, and Euclidean distance is used to calculate the similarity metric for signal matching. The effectiveness and robustness of the proposed method are experimentally validated under different abnormal welding conditions.

scholarly journals Advanced die sinking EDM process monitoring based on anomaly detection for online identification of improper process conditions

Procedia CIRP ◽  
2020 ◽  
Vol 88 ◽  
pp. 381-386
Author(s):  
Alessandra Caggiano ◽  
Francesco Napolitano ◽  
Roberto Teti ◽  
Stefano Bonini ◽  
Umang Maradia
Keyword(s):  
Anomaly Detection ◽  
Process Monitoring ◽  
Process Conditions ◽  
Online Identification ◽  
Edm Process

CO2 laser tailored blank welding: process monitoring

1996 ◽  
Author(s):  
Giuseppe D'Angelo ◽  
Elena Borello ◽  
Nereo Pallaro
Keyword(s):  
Process Monitoring ◽  
Co2 Laser ◽  
Welding Process ◽  
Tailored Blank ◽  
Tailored Blank Welding

scholarly journals In-Process Monitoring of Lack of Fusion in Ultra-Thin Sheets Edge Welding Using Machine Vision

Sensors ◽  
2018 ◽  
Vol 18 (8) ◽  
pp. 2411 ◽  
Author(s):  
Yuxiang Hong ◽  
Baohua Chang ◽  
Guodong Peng ◽  
Zhang Yuan ◽  
Xiangchun Hou ◽  
...  
Keyword(s):  
Real Time ◽  
Weld Pool ◽  
Process Monitoring ◽  
Welding Process ◽  
Thin Sheets ◽  
Real Time Control ◽  
Image Processing Algorithm ◽  
Monitoring Method ◽  
Lack Of Fusion ◽  
Time Control

Lack of fusion can often occur during ultra-thin sheets edge welding process, severely destroying joint quality and leading to seal failure. This paper presents a vision-based weld pool monitoring method for detecting a lack of fusion during micro plasma arc welding (MPAW) of ultra-thin sheets edge welds. A passive micro-vision sensor is developed to acquire clear images of the mesoscale weld pool under MPAW conditions, continuously and stably. Then, an image processing algorithm has been proposed to extract the characteristics of weld pool geometry from the acquired images in real time. The relations between the presence of a lack of fusion in edge weld and dynamic changes in weld pool characteristic parameters are investigated. The experimental results indicate that the abrupt changes of extracted weld pool centroid position along the weld length are highly correlated with the occurrences of lack of fusion. By using such weld pool characteristic information, the lack of fusion in MPAW of ultra-thin sheets edge welds can be detected in real time. The proposed in-process monitoring method makes the early warning possible. It also can provide feedback for real-time control and can serve as a basis for intelligent defect identification.

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