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

Abstract

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 approaches, including machine control data and additional sensors, and addresses the resulting need for effective data reduction and interpretation. An experimental setup on a CNC milling machine collected OPC-UA data and vibration signals, processed via a KNIME-based pipeline. Results show that, in the investigated proof-of-concept experiments, simple aggregated indicators (e.g., power consumption) allow a clear distinction between new-tool and end-of-life (EoL) states under stable process conditions. However, when cutting parameters vary, the evaluated conventional machine-learning classifiers do not achieve satisfactory discrimination. This limitation is associated with parameter-induced signal overlap, the limited training dataset, and the deliberately simple time-domain features used in this study. The study is therefore intended as an exploratory proof-of-concept rather than a comprehensive validation of continuous tool wear progression. Finally, the contribution highlights the potential of generative AI for data preprocessing, showing that large language models can efficiently clean, structure, and interpret raw manufacturing data, reducing engineering effort and improving accessibility of data analytics. The findings provide a feasibility baseline for future studies addressing intermediate wear states, richer feature extraction, and broader industrial validation.