Abstract
There are many items in the literature indicating that certain signal features (SFs) of cutting forces, vibrations or acoustic emission are useful for the diagnosis of tool wear in certain single experiments. There is no answer to whether these SFs are universal. The novelty of this article is an attempt to answer these questions and propose a large set of SFs related to tool wear, but without including superfluous SFs. The analysis of the usefulness of the signal properties for the state of the cutting tool in turning was carried out on a large experiment. A number of various SFs obtained for various signal analysis methods were selected for the study. It is found that no SF is always related to the tool wear, so we define many different signal characteristics that can be related to the tool wear (basic set) and automatically select those associated with it in a given machining case. To this end, the relationship between the measures and the wear of the tool was analyzed. Interrelated measures were excluded from it. The obtained results can be used to build a new generation of more effective tool wear diagnostics systems. One of the goals of the tool wear diagnosis system is to save the energy used. The results can also enable the refinement of existing algorithms that predict the energy consumption of a machine.
Highlights
The curves are presented as a function of the used part of the tool life (∆T [%]) for successive shelf-lives—each marked with a different color
This paper presents a universal methodology for testing the suitability of signal feature
signal feature (SF) for tool wear diagnostics, which can be applied to various machining operations [69]
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. The calculation of a sufficient number of SFs related to the tool and/or process conditions [20] is a key issue in machining monitoring systems Many laboratories are currently working on algorithms for diagnosing tool wear based on more complex models of dependence of SFs on the condition of the tool This model is not generic but is built for each new machining case on the basis of learning data. The research was carried out for four different turning operations selecting SFs characterized by high efficiency and universality
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