Abstract
Cutting tool condition prognosis is critical to process stability and quality assurance, but affected by complex material-process interactions. This paper presents a hybrid machine learning method that integrates heterogeneous data (structured process parameters and unstructured power profiles and tool wear images) for tool condition prognosis. Surface and wear images are first analyzed by a convolutional neural network to identify surface roughness and wear severity. The results are subsequently fed into a recurrent neural network to reveal the relationship between tool condition degradation and power profiles. The fidelity of the method is validated in milling of H13 steel and Inconel 718.
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