  The prediction of tool wear helps minimize costs and enhance product quality
in manufacturing. While existing data-driven models using machine learning and
deep learning have contributed to the accurate prediction of tool wear, they
often lack generality and require substantial training data for high accuracy.
In this paper, we propose a new data-driven model that uses Bayesian
Regularized Artificial Neural Networks (BRANNs) to precisely predict milling
tool wear. BRANNs combine the strengths and leverage the benefits of artificial
neural networks (ANNs) and Bayesian regularization, whereby ANNs learn complex
patterns and Bayesian regularization handles uncertainty and prevents
overfitting, resulting in a more generalized model. We treat both process
parameters and monitoring sensor signals as BRANN input parameters. We
conducted an extensive experimental study featuring four different experimental
data sets, including the NASA Ames milling dataset, the 2010 PHM Data Challenge
dataset, the NUAA Ideahouse tool wear dataset, and an in-house performed
end-milling of the Ti6Al4V dataset. We inspect the impact of input features,
training data size, hidden units, training algorithms, and transfer functions
on the performance of the proposed BRANN model and demonstrate that it
outperforms existing state-of-the-art models in terms of accuracy and
reliability.
