Industry 4.0 Machine Learning to Monitor the Life Span of Cutting Tools in an Automotive Production Line

Abstract

The evolution of manufacturing processes in the global industrial scenario is correlated with the growing integration of information technologies, storage capacity and data processing, effective communication between sectors and the development of intelligent and autonomous lines that seek zero waste and quick take-up. decision. In the productive sphere, the use of these resources characterizes intelligent factories, where the manufacture of physical objects is integrated into the information network. Industry 4.0 provides a more flexible, sustainable and agile production chain prioritizing autonomous decision-making integrating hundreds of thousands of generated data and machine learning for problem solving, process improvement and agile and absolute productive monitoring. The present study seeks to prove how decision making through supervised machine learning programming models contributes to cost reduction, increased productivity, waste elimination and process improvement in monitoring tool life in cutting tools used in machining lines process for the manufacture cylinder blocks and cylinder heads of combustion engines in the automotive sector. The knowledge generated from this study reinforces the need and relevance of the concept’s dissemination of the fourth industrial revolution in the country, an industrial trend adopted globally in recent years.