Abstract
The paper presents a comparative analysis of selected algorithms for prediction and data analysis. The research was based on data taken from a computerized numerical control (CNC) milling machine. Methods of knowledge extraction from very large datasets, characteristics of classical analytical methods used in datasets and knowledge discovery in database (KDD) processes were also described. The aim of the study is a comparative analysis of selected algorithms for prediction and data analysis to determine the time and degree of tool usage in order to react early enough and avoid unwanted incidents affecting production effectiveness. The research was based on K-nearest neighbor, decision tree and linear regression algorithms. The influence of the rate of learning and testing set sizes were evaluated, which may have an important impact on the optimization of the time and quality of computation. It was shown that precision decreases with the increase of the K value of the average group, while the percentage of the number of classes in a given set (recall) increases. The harmonic mean for the group mean also increases with increasing K, while a significant decrease in these values was observed for the standard deviations of the group. The numerical value of accuracy decreases with increasing K.
Highlights
Tool health condition monitoring is of great interest to researchers in the era of Internet of Things (IoT) and Industry 4.0 development
The research is based on K-nearest neighbor, decision tree and
The response extraction of statistical parameters time and frequency domainThe from the conducted analyses showed that there is a need for a detailed study of the nature of the with cutting force signals allowed the determination of an effective and efficient classifier signal and itsresponse relationship to which the tooliscondition, in the case anTCM
Summary
Tool health condition monitoring is of great interest to researchers in the era of IoT and Industry 4.0 development. The interest in tool condition monitoring stems from the fact that we are trying to make production unmanned. This is possible only if we provide an appropriate method of monitoring tool wear and tool damage detection. Tool replacement is based on conservative estimates of tool life derived from documentation provided by the manufacturer. Such solutions are not optimal because they involve too many changes, as the full tool life is not taken into account, and valuable production time is lost
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