Motor de análisis basado en técnicas  de aprendizaje automático para la  identificación de variables críticas en  procesos multietapa: aplicación a la  instalación de remaches ciegos

Authors

Quality control in manufacturing is a recurrent topic as the

ultimate goals are to produce high quality products with less

cost. Mostly, the problems related to manufacturing processes

are addressed focusing on the process itself putting aside other

operations that belong to the part’s history. This research work

presents a Machine Learning-based analysis engine for non-expert

users which identifies relationships among variables throughout

the manufacturing line. The developed tool was used to analyze the

installation of blind fasteners in aeronautical structures, with the

aim of identifying critical variables for the quality of the installed

fastener, throughout the fastening and drilling stages. The results

provide evidence that drilling stage affects to the fastening,

especially to the formed head’s diameter. Also, the most critical

phase in fastening, which is when the plastic deformation occurs,

was identified. The results also revealed that the chosen process

parameters, thickness of the plate and the fastener type influence

on the quality of the installed fastener.