A Computationally-efficient Semi-supervised Learning Model for the Estimation of State Degradation of a Milling Tool

TY - GEN

T1 - A Computationally-efficient Semi-supervised Learning Model for the Estimation of State Degradation of a Milling Tool

AU - Sharifirad, Iman

AU - Boudjadar, Jalil

PY - 2024/1/25

Y1 - 2024/1/25

N2 - This paper proposes a computation-efficient semisupervised machine learning model for the near-to-real-time state of health estimation for a Milling tool. To secure high reliability of the health state estimation, we have adopted the two-stage classification Beta Variational Auto-Encoder (β-VAE) architecture. The computation efficiency is achieved through the correlation between the latent space and input space classification processes of β-VAE for healthy states, where the input space decoder classification is triggered only when the latent space classifies the state to be unhealthy. This can be seen as a double check process due to the high cost related to processing the false-positive maintenance decisions. We have implemented the proposed model and tested it on the UC Berkeley milling data set. The experiment results show that our state of health estimation model can reduce the computation cost by up to 15% while achieving high classification accuracy.

AB - This paper proposes a computation-efficient semisupervised machine learning model for the near-to-real-time state of health estimation for a Milling tool. To secure high reliability of the health state estimation, we have adopted the two-stage classification Beta Variational Auto-Encoder (β-VAE) architecture. The computation efficiency is achieved through the correlation between the latent space and input space classification processes of β-VAE for healthy states, where the input space decoder classification is triggered only when the latent space classifies the state to be unhealthy. This can be seen as a double check process due to the high cost related to processing the false-positive maintenance decisions. We have implemented the proposed model and tested it on the UC Berkeley milling data set. The experiment results show that our state of health estimation model can reduce the computation cost by up to 15% while achieving high classification accuracy.

KW - State of health estimation

KW - classification

KW - Variational Auto-Encoder

KW - computation efficiency

KW - Tool Wear

KW - Semi-supervised learning

KW - Representation learning

UR - http://www.scopus.com/inward/record.url?scp=85186766218&partnerID=8YFLogxK

U2 - 10.1109/SAMI60510.2024.10432881

DO - 10.1109/SAMI60510.2024.10432881

M3 - Article in proceedings

SN - 979-8-3503-1721-3

SP - 187

EP - 192

BT - 2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics, SAMI 2024 - Proceedings

PB - IEEE

ER -