A review of cutting tool life prediction through flank wear monitoring

NomenclatureRUL

remaining useful life

CBM

condition-based maintenance

N

spindle speed

V_c

cutting speed

f

feed rate

f_z

feed per tooth

a_p

depth of cut during turning

a_a

axial depth of cut during milling

a_r

radial depth of cut during milling

CNC

computer numerical controlled

ANN

artificial neural network

CNN

convolutional neural network

LSTM

long short-term memory

SBULSTM

stacked bi-directional and uni-directional long short-term memory

SDV

standard deviation value

RMS

root mean square

IMF

intrinsic mode function

EMD

empirical mode decomposition

HHT

Hilbert-Huang Transform

PCA

principal component analysis

RSM

response surface methodology

PSO

particle swarm optimization

ANOVA

analysis of variance

MCS

Monte Carlo simulation

FORM

first order reliability method

SSM

state space model

HMM

hidden Markov model

AFT

accelerated failure time

MAPE

mean absolute percentage error

AI

artificial intelligence

MIMO

multi-input-multi-output

FIS

fuzzy inference system

SVR

support vector regression

SVM

support vector machine

CGA

chaotic genetic algorithm

BPNN

back propagation neural network

SAE

sparse autoencoder

DTL

deep transfer learning

DBiLSTM

deep bidirectional long short-term memory

MAE

mean absolute error

RMSE

root mean square error

SAL

sparse augmented Lagrangian

GPR

Gaussian process regression

BRNN

bidirectional recurrent neural network

BiLSTM

bidirectional long short-term memory

BiGRU

bidirectional gated recurrent unit

DT

digital twin

BNN

Bayesian neural network

CSBLSTM

convolutional stacked bidirectional long short-term memory

TSAM

time-space attention mechanism

1. Introduction

In machining operations such as turning, milling, drilling and so on, the cutting tool serves a significant role. Tool failures in terms of tool wear and breakage affect the manufacturing process adversely (Li et al., 2020). Generally, a machining process consists of two crucial components, i.e. the machine tool and the cutting tool (Salonitis and Kolios, 2020). Despite the developments of machine tools and coating technologies, the failure of the tool in the form of wear creates major challenges in cutting processes as tool wear impacts product quality, production time and machining cost adversely (Zaretalab et al., 2020). The main purpose of the life prediction of the cutting tool is to ensure the maximum possible usage of the tool before failure (Salonitis and Kolios, 2020). Cutting tool life assessment enables prompt replacement of degraded tools. It reduces waste and tool costs. A better surface finish can be obtained if the tool...

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