Towards a generalized energy prediction model for machine tools.

Area of Science:

• Manufacturing Engineering
• Data Science
• Sustainable Manufacturing

Background:

• Accurate energy prediction for machine tools offers significant advantages in manufacturing, including energy-efficient process planning and enhanced machine monitoring.
• Traditional physics-based models struggle with energy prediction due to uncertainties in machine and environmental factors.
• The need for reliable energy consumption forecasting in machining operations is critical for optimizing resource utilization.

Purpose of the Study:

• To develop a data-driven methodology for predicting the energy consumption of machine tools.
• To create a generalized energy prediction model applicable to various parts and machining operations.
• To leverage machine learning for improved energy efficiency in manufacturing processes.

Main Methods:

• A methodology for efficient data collection and processing from machine tools and sensors was established.
• Gaussian Process (GP) Regression, a non-parametric machine learning technique, was employed to build the energy prediction model.
• The model was generalized across multiple process parameters and operations for broader applicability.

Main Results:

• A data-driven energy prediction model for a Mori Seiki NVD1500 machine tool was successfully developed.
• The model demonstrated the ability to predict energy consumption for machining generic and specific parts with uncertainty intervals.
• The generalized model proved effective in predicting energy usage for diverse machining scenarios.

Conclusions:

• Data-driven approaches, specifically Gaussian Process Regression, provide a reliable method for machine tool energy prediction, overcoming limitations of physics-based models.
• The developed model can be utilized for both real-time machine monitoring and proactive process planning to optimize energy efficiency.
• This research contributes to sustainable manufacturing by enabling more accurate energy consumption forecasting and reduction strategies.