Tool wear classification using compact language models with multi-scale filter bank extractor and low-rank adaptation

Generative AI-Assisted Low-Code Pipelines for Tool Wear Preprocessing and Feasibility Assessment in CNC Milling

Detecting tool wear in CNC milling is a central challenge for data analytics and sensor integration in Industry 4.0, as gradual degradation increases the risk of tool breakage and downtime and often leads manufacturers to replace tools early and inefficiently. This contribution reviews existing tool wear detection appr...

A new data-driven framework for tool wear prediction using efficient PCA, and optimized SVR

Predicting Wear of NAO Brake Pad Using Scaled-Down FMVSS 135 Testing and Machine Learning

Brake pad wear is a major and growing source of non-exhaust particulate emissions, projected to reach 1.3 million tons annually by 2030 and contributing up to roughly 55% by mass of non-exhaust traffic-related PM10 in urban environments, underscoring the need for improved durability and material optimization. This stud...

Detection of tool wear in drilling using acoustic machine learning models through labeling-threshold sensitivity analysis

A method for predicting tool remaining useful life based on a physics-informed neural network

This paper proposes a Physics-Informed Neural Network (PINN)-based method for tool remaining useful life (RUL) prediction by incorporating physical wear evolution laws into a data-driven prediction framework. The method improves prediction accuracy and enhances cross-scenario adaptability through consistency with t...

Comparative study on small sample tool wear classification based on YOLO26 and vision transformer

In the process of machining, manual misjudgment is prone to occur in the detection of tool wear. To address this issue, this paper explores the performance of two models, YOLO26 and Vision Transformer (ViT), under the conditions of a small sample and binary classification tool wear dataset based on a deep learning clas...