Energy Consumption Prediction of Additive Manufactured Tensile Strength Parts Using Artificial Intelligence.

Area of Science:

• Manufacturing Engineering
• Materials Science
• Computer Science

Background:

• Additive Manufacturing (AM) is gaining traction in manufacturing.
• Optimizing energy consumption in rapid prototyping is crucial due to increasing energy demands.

Purpose of the Study:

• To minimize energy consumption in 3D printing by identifying optimal production parameters.
• To develop accurate predictive models for energy usage in fused deposition modeling (FDM).

Main Methods:

• Four machine learning algorithms were employed to model energy consumption.
• Real-time data from test samples were used to train prediction models.
• Model performance was evaluated using MSE, MAE, RMSE, R-squared, and EVS.

Main Results:

• Gaussian Process Regression (GPR) demonstrated high accuracy in predicting energy consumption.
• GPR achieved R-squared = 0.99, EVS = 0.99, MAE = 0.016, RMSE = 0.022, and MSE = 0.00049.

Conclusions:

• Machine learning, particularly GPR, is effective for optimizing energy consumption in AM.
• Accurate energy consumption prediction enables more efficient 3D printing processes.