Hybrid modelling method for the prediction and experimental validation of 3D printing resource consumption

Yanga, J. and Liu, Y. (2023) Hybrid modelling method for the prediction and experimental validation of 3D printing resource consumption. Journal of Manufacturing Processes, 101, pp. 1275-1300. (doi: 10.1016/j.jmapro.2023.06.030)

Abstract

In this study, a hybrid modelling scheme for 3D Printing (3DP) technology is proposed for predicting three resource consumption processes: production time, electric energy and material usage. First, the manufacturing process is classified into five machine operations: axial moving, material processing, unit heating, material feeding and auxiliary. Then, according to the machine behaviours, a Gantt chart and power profile (GP) diagram is generated for understanding operation processes and power variations. Based on the GP diagram, physical models and data-driven models are created for simulating the resource consumption level of each operation. The time and material consumed during part printing are physically modelled based on a computer numerical control (CNC) language: G-code. The power of each operation and the time consumed while preheating machine units are modelled through experimental measurements under different related process parameter values. The collected experimental data are regressed to obtain the functional relationships between the power, preheating time and process parameters. With the time and power submodels for all operations, the resource consumption models for the 3DP machine are assembled. To verify the effectiveness of this method, hybrid prediction modelling is demonstrated for two fused deposition modelling (FDM) machines, and the prediction accuracies are tested for real printing tasks. The proposed method can be adopted to other additive manufacturing processes by comprehensively considering related factors, including part geometries, process parameters, G-codes and machine behaviours.

• Item Type: Articles
• Additional Information: This work is jointly funded by Fundamental Research Funds for the Central Universities (Grant No. 3122022QD15).
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Liu, Dr Ying
• Authors: Yanga, J., and Liu, Y.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Journal of Manufacturing Processes
• Publisher: Elsevier
• ISSN: 1526-6125
• ISSN (Online): 2212-4616
• Published Online: 13 July 2023
• Copyright Holders: Copyright © 2023 The Authors
• First Published: First published in Journal of Manufacturing Processes 101: 1275-1300
• Publisher Policy: Reproduced under a Creative Commons License