Research on Milling Machine Predictive Maintenance Based on Machine Learning and SHAP Analysis in Intelligent Manufacturing Environment

TL;DR

This study develops a comprehensive AI-based predictive maintenance process for milling machines, utilizing machine learning models and SHAP analysis to identify key failure factors, thereby enhancing maintenance accuracy and efficiency in intelligent manufacturing.

Contribution

It introduces an integrated predictive maintenance framework combining AI techniques and SHAP analysis, providing practical insights for fault prediction and feature influence in manufacturing.

Findings

XGBoost and random forest outperform other models in fault prediction

SHAP analysis reveals temperature, torque, and speed as key failure factors

The method improves maintenance decision-making and reduces costs

Abstract

In the context of intelligent manufacturing, this paper conducts a series of experimental studies on the predictive maintenance of industrial milling machine equipment based on the AI4I 2020 dataset. This paper proposes a complete predictive maintenance experimental process combining artificial intelligence technology, including six main links: data preprocessing, model training, model evaluation, model selection, SHAP analysis, and result visualization. By comparing and analyzing the performance of eight machine learning models, it is found that integrated learning methods such as XGBoost and random forest perform well in milling machine fault prediction tasks. In addition, with the help of SHAP analysis technology, the influence mechanism of different features on equipment failure is deeply revealed, among which processing temperature, torque and speed are the key factors affecting failure. This study combines artificial intelligence and manufacturing technology, provides a methodological reference for predictive maintenance practice in an intelligent manufacturing environment, and has practical significance for promoting the digital transformation of the manufacturing industry, improving production efficiency and reducing maintenance costs.