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

Wen Zhao, Jiawen Ding, Xueting Huang, Yibo Zhang

TL;DR

The paper tackles predictive maintenance for milling machines in an intelligent manufacturing setting using the AI4I 2020 dataset. It develops an end-to-end workflow with data preprocessing, multi-model training, evaluation, hyperparameter tuning, SHAP-based interpretability, and result visualization, and benchmarks eight algorithms. Gradient Boosting emerges as the top performer, with Random Forest and XGBoost closely competitive, while SHAP reveals processing temperature, torque, speed, and tool wear as key risk drivers. The study contributes actionable guidance for maintenance planning and demonstrates how interpretable AI can support the digital transformation of smart factories.

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.

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

TL;DR

The paper tackles predictive maintenance for milling machines in an intelligent manufacturing setting using the AI4I 2020 dataset. It develops an end-to-end workflow with data preprocessing, multi-model training, evaluation, hyperparameter tuning, SHAP-based interpretability, and result visualization, and benchmarks eight algorithms. Gradient Boosting emerges as the top performer, with Random Forest and XGBoost closely competitive, while SHAP reveals processing temperature, torque, speed, and tool wear as key risk drivers. The study contributes actionable guidance for maintenance planning and demonstrates how interpretable AI can support the digital transformation of smart factories.

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.

Paper Structure

This paper contains 12 sections, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Methods
  3. Dataset Introduction
  4. Experimental process
  5. Model Tuning and Optimization
  6. Feature Importance Analysis
  7. Experiments
  8. Feature Correlation Analysis
  9. Model Performance Comparison
  10. SHAP Value Analysis
  11. Fault Type Prediction Analysis
  12. Conclusion

Figures (5)

  • Figure 1: Experiment Workflow.
  • Figure 2: Experiment Workflow.
  • Figure 3: Actual vs Predicted Values using Gradient Boosting.
  • Figure 4: Residual Distribution (Gradient Boosting).
  • Figure 5: SHAP Summary Plot: Feature importance and value impact.