Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
Applied System Innovation
; 5(4):86, 2022.
Article
in English
| ProQuest Central | ID: covidwho-2023109
ABSTRACT
Additive manufacturing (AM) technologies are growing more and more in the manufacturing industry;the increase in world energy consumption encourages the quantification and optimization of energy use in additive manufacturing processes. Orientation of the part to be printed is very important for reducing energy consumption. Our work focuses on defining the most appropriate direction for minimizing energy consumption. In this paper, twelve machine learning (ML) algorithms are applied to model energy consumption in the fused deposition modelling (FDM) process using a database of the FDM 3D printing of isovolumetric mechanical components. The adequate predicted model was selected using four performance criteria mean absolute error (MAE), root mean squared error (RMSE), R-squared (R2), and explained variance score (EVS). It was clearly seen that the Gaussian process regressor (GPR) model estimates the energy consumption in FDM process with high accuracy R2 > 99%, EVS > 99%, MAE < 3.89, and RMSE < 5.8.
Technology: Comprehensive Works; additive manufacturing (AM); fused deposition modelling (FDM); machine learning; energy use; Gaussian process regression; Fused deposition modeling; Design of experiments; Deep learning; Deposition; Optimization; Three dimensional printing; Manufacturing; Mechanical components; Energy consumption; Additive manufacturing; COVID-19; Case studies; Artificial intelligence; Rapid prototyping; Root-mean-square errors; Three dimensional models; Medical research; 3-D printers; Gaussian process; Algorithms; Methods
Full text:
Available
Collection:
Databases of international organizations
Database:
ProQuest Central
Type of study:
Prognostic study
Language:
English
Journal:
Applied System Innovation
Year:
2022
Document Type:
Article
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