Data-driven knife sharpness meter for peeling lines

Abstract

The research focuses on analysing data from a peeling line to understand correlations leading to knife change events. The study starts from identifying knife change events based on downtimes and labelling data by associated knives and peeled meter steps. The primary purpose is to uncover how collected data represents the lifespan of lathe knives and to identify key factors influencing the decision-making process for knife changes. The methodology involves quantitative research, including data gathering, labelling, and group summarization as well as constructive research where trained models were investigated to unravel their attentions. The study successfully processed peeling line data, revealing that models trained on top features from individual data sources outperformed those trained on combined features. Additionally, the research identified inaccuracies in the raw data, emphasizing the necessity of optimizing and validating data collection processes for future investigations. Notably, the study shed light on key factors influencing knife sharpness in peeling lines, offering valuable in-sights for further exploration