A Novel Tool (Single-Flute) Condition Monitoring Method for End Milling Process Based on Intelligent Processing of Milling Force Data by Machine Learning Algorithms

Abstract

Tool condition monitoring is deemed as an essential technology of the intelligent manufacturing. Tool wear which directly affects the tool life makes a negative influence on the quality and dimensional accuracy of the machined surface, even leads to tool breakage, machine downtime, and other severe problems. Therefore, an available tool condition monitoring system is essential for the machining process to guarantee the processing quality and improve the machining efficiency. This paper proposes a new tool condition monitoring method based on the general judgment of cutting force. Milling force from a single-flute is predicted by deducing a theoretical formula based on un-deformed chip thickness. Based on the formula, cutting force samples used for machine learning paradigms are generated through time domain translation and Gaussian distribution. Nonlinear manifold learning methods are applied in the visualization of high dimensional data. Principal component analysis as a practical feature extraction method is used to reduce the large dimensionality of the sample set. The performance of respectively linear kernel, polynomial kernel, radial basis function and sigmoid kernel are self-compared to estimate the classification results via support vector machine. Experiments are carried out on an annealed Ti–6Al–4V alloy to measure the feasibility of this method.