Tool wear monitoring and alarm system based on pattern recognition with Logical Analysis of Data
This paper presents a new tool wear monitoring and alarm system that is based on logical analysis of data (LAD).
LAD is a data-driven combinatorial optimization technique for knowledge discovery and pattern recognition.
The system is a nonintrusive online device that measures the cutting forces and relates them to tool wear through learned patterns.
It is developed during turning titanium metal matrix composites (TiMMCs).
These are a new generation of materials which have proven to be viable in various industrial fields such as biomedical and aerospace.
Since they are quite expensive, our objective is to increase the tool life by giving an alarm at the right moment.
The proposed monitoring system is tested by using the experimental results obtained under sequential different machining conditions.
External and internal factors that affect the turning process are taken into consideration.
The system’s alarm limit is validated and is compared to the limit obtained when the statistical proportional hazards model (PHM) is used.
The results show that the proposed system that is based on using LAD detects the worn patterns and gives a more accurate alarm for cutting tool replacement.