A Feature-Engineering-Based Machine Learning Approach for Cutter Flank Wear Prediction under Data-Scarce Conditions

Abstract

Accurate estimation of tool wear in machining processes is essential to ensure product quality and optimize maintenance strategies. This work presents a Machine Learning methodology for the PHM-AP 2025 Data Challenge. The objective of the challenge is the cutter flank wear prediction in a CNC mill-turn machine using accelerometer, acoustic emission, and controller data. The training data consists of six datasets with a limited number of labeled samples, resulting in a few-shot learning scenario. To address these constraints, a manual feature extraction method is proposed. Features are computed by aggregating data from the controller and sensors in the time and frequency domains across five-cut intervals. In this way, the wear behavior is captured, and the sensitivity to missing data is reduced. Then, an optimization process is performed to select the most relevant features based on correlation values. These 14 identified features are used to fit a Multilayer Perceptron through a leave-one-dataset-out cross-validation process. Results reveal variability between training sets, with pronounced errors in the 17-21 cutting interval in four datasets. However, in the evaluation stage, the model achieved a competitive performance: RMSE of 11.486, MAPE of 8.518, and R² of 0.875, placing fourth in the challenge.