INDUSTRY QUALITY CONTROL

In collaboration with a team of Mechanical Engineers, we have applied Computer Vision and Machine Learning techniques for successfully assessing surface roughness automatically without using any contact device, or estimating life of cutting tools in milling processes with no need of human intervention, to predict the best time to replace a worn tool. Currently, we are trying to apply it to assess the surface quality of tools built using additive manufacturing with metallic and ceramic materials.

For more than 5 years we have collaborated with the research group TAFI of the University of León in projects related to quality control inspection in Mechanical Engineering-related industry. This collaboration has made GVIS and TAFI groups to be considered as a Consolidated Research Unit (Unidad de Investigación Consolidada – UIC) by the Regional government of Castilla y León.

In this context, Computer Vision and Machine Learning techniques have been very useful for successfully assessing surface roughness automatically without using any contact device, or estimating the life of cutting tools in milling processes with no need of human intervention, to predict the best time to replace a worn tool.

Recently, we have started two funded national and regional projects where Computer Vision and Machine Learning will be assessed to estimate the surface quality of tools built using additive manufacturing with metallic and ceramic materials.

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