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Thiago E. Fernandes
Federal University de Juiz de Fora, Juiz de Fora, MG
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Matheus A. M. Ferreira
Federal University de Juiz de Fora, Juiz de Fora, MG
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Guilherme P. C. de Miranda
Federal University de Juiz de Fora, Juiz de Fora, MG
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Marcos V. G. R. da Silva
Federal University de Juiz de Fora, Juiz de Fora, MG
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Eduardo P. de Aguiar
Federal University de Juiz de Fora, Juiz de Fora, MG
Keywords:
Autonomous Learning, Empirical Data Analyses, Machine Learning, Machining Processes
Abstract
The majority of mechanical components went through a machining process during their manufacturing. Therefore, manufacturing processes with inadequate condition tools are likely to induce unexpected operational interruptions, accidents, product quality, and economic losses. Accordingly, the ability to classify fault imminences can result in cost reduction, along with productivity and safety increase. This paper aims to discuss an autonomous model based on the Self-Organised Direction Aware Data Partitioning Algorithm (SODA) and machine learning techniques, including time series Feature Extraction based on Scalable Hypothesis tests (TSFRESH), to solve this problem. The model proposed in this work can identify the patterns that distinguish the cutting tool’s flank wear in a multi-class scenario as adequate, intermediate, and inadequate conditions, achieving satisfactory performances in all cases and allowing to prevent fault occurrences.