Authors:
Beatriz Coutinho
1
;
Bruno Santos
1
;
Rita Gomes Mendes
2
;
Gil Gonçalves
1
and
Vítor H. Pinto
1
Affiliations:
1
SYSTEC ARISE, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, 4200-465 Porto, Portugal
;
2
ISQ&CTAG Automotive Technologies, Monção, Portugal
Keyword(s):
Stud Welding, Weld Quality Prediction, Sensorisation, Data Acquisition.
Abstract:
Drawn arc stud welding is extensively used in automotive assembly lines for attaching components to vehicle bodies. In these automated processes, low-quality welds can compromise structural integrity and cause production delays due to rework and maintenance. This paper describes the initial development stage of an artificial intelligence (AI)-based system for real-time weld quality prediction in automated stud welding. The focus of this first phase is on implementing sensorisation, developing a data acquisition system, and constructing a dataset that captures the most relevant process variables characterizing the welding process. A Flask-based application was developed to facilitate data collection, incorporating an automatic character recognition algorithm to extract parameters directly from the control unit display. Initial welding experiments produced a dataset of approximately 200 samples, with preliminary data analysis validating expected parameter trends. The results confirm th
e system’s capability to effectively capture relevant data, forming the basis for future development of a predictive model aimed at enhancing weld quality monitoring and minimizing assembly line interruptions.
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