Remaining Useful-Life Prediction of the Milling Cutting Tool Using Time–Frequency-Based Features and Deep Learning Models

Abstract

The milling machine serves an important role in manufacturing because of its versatility in machining. The cutting tool is a critical component of machining because it is responsible for machining accuracy and surface finishing, impacting industrial productivity. Monitoring the cutting tool’s life is essential to avoid machining downtime caused due to tool wear. To prevent the unplanned downtime of the machine and to utilize the maximum life of the cutting tool, the accurate prediction of the remaining useful life (RUL) cutting tool is essential. Different artificial intelligence (AI) techniques estimate the RUL of cutting tools in milling operations with improved prediction accuracy. The IEEE NUAA Ideahouse dataset has been used in this paper for the RUL estimation of the milling cutter. The accuracy of the prediction is based on the quality of feature engineering performed on the unprocessed data. Feature extraction is a crucial phase in RUL prediction. In this work, the authors considers the time–frequency domain (TFD) features such as short-time Fourier-transform (STFT) and different wavelet transforms (WT) along with deep learning (DL) models such as long short-term memory (LSTM), different variants of LSTN, convolutional neural network (CNN), and hybrid models that are a combination of CCN with LSTM variants for RUL estimation. The TFD feature extraction with LSTM variants and hybrid models performs well for the milling cutting tool RUL estimation.

Temperature Analysis in Cubic Boron Nitrate Cutting Tool during Minimum Quantity Lubrication Turning with a Coconut-Oil-Based Nano-Cutting Fluid Using Computational Fluid Dynamics

“…While using a nano-cutting fluid, the oil-penetrated particles stick to the machined surface and encourage plastic to flow on the chip in reverse. Consequently, MQL technology greatly enhances machinability, and when the cutting fluid nozzle is projected on the tool's flank [6] surface, a greater amount of cutting fluid can penetrate the tool [7,8].…”

Remaining Useful Life Prediction for Two-Phase Nonlinear Degrading Systems with Three-Source Variability

“…Generally, the RUL prediction approaches can be systematically classified into model-based methods, data-driven methods, and hybrid methods [ 7 ]. More detailed discussions about those three types of methods can be found in [ 8 , 9 , 10 ]. As one category of data-driven methods, the stochastic model-based methods have been widely used attributable to their great potential in characterizing the stochastic dynamic degradation process and providing the probability distribution of RUL [ 11 , 12 ].…”