ISBN
Formato digital
979-13-87837-54-9
Fecha de publicación
06-10-2025
Licencia
D. R. © Copyright 2025. Alma Y. Alanis, Jorge Galvez, Omar Avalos, Eduardo Méndez-Palos, Jorge D. Rios, Adriana Peña Perez-Negron & Gabriel Martínez Soltero
Todos los contenidos de esta obra se comparten bajo la licencia Creative Commons Atri-bución/Reconocimiento-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0). Esto implica que no está autorizado el uso comercial de la obra original ni de las eventuales obras derivadas, las cuales deberán distribuirse bajo la misma licencia que rige la obra original. No obstante, se permite a terceros compartir el contenido siempre y cuando se reconozca debidamente la autoría y la publicación original en esta editorial.
Eduardo Rangel Carrillo
College of Professionals in Information Systems of Jalisco, COPSIJAL
0000-0002-4506-6615
Alma Yolanda Alanis García
Universidad de Guadalajara
0000-0001-9600-779X
Acerca de
Electrical connectors are widely used in modern day technology and products and they play a key role in manufacturing facilities. Specifically in testing equipment in production lines, the contact reliability of electrical test connectors is crucial for obtaining correct measurements of a wide variety of signals coming from the unit under test (UUT) or from the test equipment (TE). Because modern production environments submit testing connectors to constant cycles of contact, which includes several grades of physical and electrical stress, the reliability of any given connector becomes a very important factor in production quality and continuity. Failure of contact may result in unreliable testing results, product failure or even physical damage to product or even testing equipment. Damage to connector terminals due to wearing may induce different degrees of contact failure of contact, which may result in unreliable testing results, product failure or even physical damage. As global modern production quality requirements become more prominent, test processes are expected to be increasingly reliable in modern day industry. In this paper, we provide an approach to identify failure of contact reliability in testing connectors using Deep Learning.
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