Use of nearest neighbors (k–nn) algorithm in tool condition identification in the case of drilling in melamine faced particleboard

Abstract

The purpose of this study was to develop an automatic indirect (non-invasive) system to identify the condition of drill bits on the basis of the measurement of feed force, cutting torque, jig vibrations, acoustic emission and noise which were all generated during machining. The k-nearest neighbors algorithm classifier (k-NN) was used. All data analyses were carried out in MATLAB (MathWorks – USA) environment. It was assumed that the most simple (but sufficiently effective in practice) tool condition identification system should be able to recognize (in an automatic way) 3 different states of the tool, which were conventionally defined as “Green” (tool can still be used), “Red” (tool change is necessary) and “Yellow” (intermediate, warning state). The overall accuracy of classification was 76 % what can be considered a satisfactory result at this stage of studies.

Keywords

• Drilling
• melamine faced particleboard
• k-NN classifier
• tool condition identification