A qualitative model for predicting energy consumption of rapid prototyping processes-a case of fused deposition modeling processe

Abstract

Fused deposition modeling (FDM) technology is a method of additive manufacturing that is growing with widely application. Due to the increasing tense of energy situation, it is also timely to consider the economic and environmental issues of growth in rapid prototyping technology. However, the question of how to model the functional relationship between printing parameters and energy consumption has received little attention. Only few researchers deal with the process optimization including the energy aspects. This paper explores how the printing parameters affect the energy demand for FDM process based on Group Method of Data Handling (GMDH) method. Further experimentations were designed, and an evaluation model of the energy demand based on GMDH algorithm has been proposed, in which the ANOVA revealed that the model is very significant. Meanwhile, by the ANOVA method, the printing system's energy consumption is analyzed to find the critical factors, in order to make best improvement in the system level. It is concluded that worktable temperature and layering thickness obviously have a significant influence on energy demand of the printing process. Finally, using differential evolution (DE) algorithm, optimal process parameters have been found to achieve good energy consumption simultaneously for the response. The results showed that the energy demand of the FDM process is improved by optimizing the printing parameters. Hence, the approach presented in this paper provides an important addition to existing additive manufacturing processes energy evaluating methods.