Overcoming challenges: advancements in cutting techniques for high strength-toughness alloys in aero-engines

B Zhao, Y Wang, J Peng, X Wang, W Ding… - … Journal of Extreme …, 2024 - iopscience.iop.org
Aero-engines, the core of air travel, rely on advanced high strength-toughness alloys
(THSAs) such as titanium alloys, nickel-based superalloys, intermetallics, and ultra-high …

Artificial intelligence-based data-driven prognostics in industry: A survey

MA El-Brawany, DA Ibrahim, HK Elminir… - Computers & Industrial …, 2023 - Elsevier
In the age of Industry 5.0, prognostics and health management (PHM) is very important for
proactive and scheduled maintenance in industrial processes. The target of prognosis is the …

[HTML][HTML] Tool condition monitoring in the milling process using deep learning and reinforcement learning

D Kaliyannan, M Thangamuthu, P Pradeep… - Journal of Sensor and …, 2024 - mdpi.com
Tool condition monitoring (TCM) is crucial in the machining process to confirm product
quality as well as process efficiency and minimize downtime. Traditional methods for TCM …

A review of cutting tool life prediction through flank wear monitoring

M Das, VNA Naikan, SC Panja - International Journal of Quality & …, 2025 - emerald.com
Purpose The aim of this paper is to review the literature on the prediction of cutting tool life.
Tool life is typically estimated by predicting the time to reach the threshold flank wear width …

Estimation of surface quality for turning operations using machine learning approach

A Dewangan, VSN Neigapula… - … Materials, Surfaces & …, 2024 - journals.sagepub.com
The present article examines the effects of machining parameters on machined surfaces to
determine optimum turning parameters for AISI-316 under dry machining environment. L27 …

An online prediction method of three-dimensional machining residual stress field based on incepu-net

Y Wang, Z Zhao, W Ding, M Qiao, H Su - Measurement, 2025 - Elsevier
The magnitude and distribution of residual stress field inside thin-walled parts is a critical
factor influencing machining deformation. Traditional methods relying on offline data …

Tool condition monitoring of diamond-coated burrs with acoustic emission utilising machine learning methods

T Jessel, C Byrne, M Eaton, B Merrifield… - … International Journal of …, 2024 - Springer
Within manufacturing there is a growing need for autonomous Tool Condition Monitoring
(TCM) systems, with the ability to predict tool wear and failure. This need is increased, when …

[HTML][HTML] A Method for Predicting Tool Remaining Useful Life: Utilizing BiLSTM Optimized by an Enhanced NGO Algorithm

J Wu, J Wang, H Chen - Mathematics, 2024 - mdpi.com
Predicting remaining useful life (RUL) is crucial for tool condition monitoring (TCM) systems.
Inaccurate predictions can lead to premature tool replacements or excessive usage …

Novel spectral indices and transfer learning model in estimat moisture status across winter wheat and summer maize

Z Li, Q Cheng, L Chen, W Zhai, B Zhang, B Mao… - … and Electronics in …, 2025 - Elsevier
Timely and accurate estimation of crop moisture status is important for understanding crop
growth and development. It also provides guidance for irrigation strategies and precision …

[HTML][HTML] Exploring the Processing Paradigm of Input Data for End-to-End Deep Learning in Tool Condition Monitoring

C Wang, G Wang, T Wang, X **ong, Z Ouyang, T Gong - Sensors, 2024 - mdpi.com
Tool condition monitoring technology is an indispensable part of intelligent manufacturing.
Most current research focuses on complex signal processing techniques or advanced deep …