Abstract:
Online determination of a cutter’s health status is crucial for the attainment
of condition-based automated tool change in computer numerically controlled
(CNC) machining. Due to the impracticalities associated with direct condition
measurements, data-based modeling of monitoring signals provides a viable
practical route. However, the highly noisy and redundant nature of the associ ated data impacts negatively on model’s accuracy and typically calls for addi tional initial preprocessing before modeling. Additionally, the long sequential
data entails widely varying condition distributions exhibited by different cutters,
even from the same batch on similar machining parameters, posing a challenge
to model generalization. An end-to-end model has thus been developed to work
directly on unprocessed data to establish global sensitive features from varying
distributions for online tool wear estimation in CNC machining. The model uti lizes three main functional blocks. First, a data denoising and feature selection
block automatically processes raw multisensor data directly, dispensing with
scaling or preprocessing of inputs as conventionally done. Each sensor chan nel’s independence is preserved at initial processing ensuring complementary
information from different sensors is utilized while simultaneously minimizing
existing redundancies. The weighted denoised data is then processed through
a transformer encoder block for determination of global dependencies in the
time-series sequence, regardless of the time-step position. The learned features
are then fed to an upper supervised learning block for association with the mon itored wear condition. The developed model works directly on raw noisy data
irrespective of scaling differences, saving on preprocessing computational cost.
The global associations extracted on long sequences by the transformer-encoder
allow for model generalization to varying wear distributions. The parallel pro cessing structure of all channels ensures complementary information is utilized
minimizing unforeseen model bias. The model’s performance as evaluated on
