Determinants of occupational exposure to metals by gas metal arc welding and risk management measures: A biomonitoring study

2014 ◽  
Vol 231 (2) ◽  
pp. 135-141 ◽  
Author(s):  
Renaud Persoons ◽  
Damien Arnoux ◽  
Théodora Monssu ◽  
Olivier Culié ◽  
Gaëlle Roche ◽  
...  
Work ◽  
2020 ◽  
Vol 67 (3) ◽  
pp. 591-598
Author(s):  
Younes Mehrifar ◽  
Sara Karimi Zeverdegani ◽  
Masoud Rismanchian

BACKGROUND: Welding pollutants have potentially dangerous effects on the health of welders. Analysis of exposure risks is an appropriate method for industrial hygiene occupational exposure. OBJECTIVE: The present study aimed to determine the concentrations of exposure and risk evaluation of welders to fumes and gases in three common types of welding including Shielded Metal Arc Welding (SMAW), Gas Metal Arc Welding (GMAW) and Gas Tungsten Arc Welding (GTAW). METHODS: This cross-sectional study was carried out at a steel company. Samples were taken from manganese, chromium and nickel fumes with NIOSH 7300 method and for NO, NO2, CO and O3 gases using direct reading instruments. SQRCA method was used to assess the level of exposure risk. RESULTS: Our study showed that the highest and lowest concentrations of exposure to gases were observed in MIG and GTAW welding, respectively. Also, the highest and lowest concentrations of exposure to metals were observed in SMAW and GTAW processes, respectively. Mean exposure to M, Cr and Ni metals was 2.302, 3.195, and 1.241 mg/m3, respectively. Also, mean exposure to CO, NO, NO2 and O3 was 43.05, 27.88, 4.30, and 0.41 ppm, respectively. Results of risk analysis showed that O3, NO2 and Cr had high and very high risk levels in all welding processes. CONCLUSIONS: MIG and SMAW welders have a high occupational exposure to metal and toxic gases in welding. Preventive measures such as assessment of workplace air, installation of the ventilation systems, and providing appropriate respiratory protection devices for welders should be taken.


Data in Brief ◽  
2021 ◽  
Vol 35 ◽  
pp. 106790
Author(s):  
Rogfel Thompson Martinez ◽  
Guillermo Alvarez Bestard ◽  
Sadek C. Absi Alfaro

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 467
Author(s):  
Pamela Chiñas-Sanchez ◽  
Ismael Lopez-Juarez ◽  
Jose Antonio Vazquez-Lopez ◽  
Jose Luis Navarro-Gonzalez ◽  
Aidee Hernandez-Lopez

Industrial processes seek to improve their quality control, including new technologies and satisfying requirements for globalised markets. In this paper, we present an innovative method based on Multivariate Pattern Recognition (MVPR) and process monitoring in a real-world study case. By identifying a distinctive out-of-control multivariate pattern using the Support Vector Machines (SVM) and the Mahalanobis Distance D2 it is possible to infer the variables that disturbed the process; hence, possible faults can be predicted knowing the state of the process. The method is based on our previous work, and in this paper we present the method application for an automated process, namely, the robotic Gas Metal Arc Welding (GMAW). Results from the application indicate an overall accuracy up to 88.8%, which demonstrates the effectiveness of the method, which can also be used in other MVPR tasks.


2005 ◽  
Vol 10 (1) ◽  
pp. 67-75 ◽  
Author(s):  
G. Padmanabham ◽  
S. Pandey ◽  
M. Schaper

Sign in / Sign up

Export Citation Format

Share Document