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

2025-11-30

AI Summary

To address predictive maintenance challenges for milling machines in smart manufacturing, this study proposes an end-to-end machine learning framework. It integrates eight classifiers—including logistic regression, SVM, random forest, and XGBoost—trained on the AI4I 2020 dataset to predict equipment failures. A novel contribution is the integration of SHAP-based interpretability analysis to quantitatively characterize the nonlinear effects of machining temperature, torque, and rotational speed on failure likelihood. Systematic data preprocessing, comparative model evaluation, and result visualization further enhance model transparency and industrial deployability. Experimental results demonstrate that XGBoost and random forest achieve the highest accuracy, significantly improving early fault detection capability. The framework provides a reusable, interpretable, and operationally viable methodology to support cost reduction, efficiency enhancement, and digital transformation in manufacturing.

Technology Category

• Machine Learning: Transparent, Interpretable, Explainable ML
• Humans and AI: Human-in-the-loop Machine Learning
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Machine-in-the-loop, human agency and autonomy

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.

Problem

• Predicts milling machine faults using machine learning models
• Identifies key failure factors via SHAP analysis
• Provides a predictive maintenance framework for intelligent manufacturing

Innovation

• Combines machine learning models for predictive maintenance
• Uses SHAP analysis to identify key failure factors
• Integrates AI and manufacturing for digital transformation