Data Driven Modeling and Optimization for Energy Efficiency in Additive Manufacturing Process With Geometric Accuracy Consideration

Abstract

Despite of its tremendous merits in producing parts with complex geometry and functionally graded materials, additive manufacturing (AM) is inherently an energy expensive process. Prior studies have shown that process parameters, such as printing resolution, printing speed, and printing temperature, are correlated to energy consumption per part. Moreover, part geometric accuracy is another major focus in AM research, and extensive studies have shown that the geometric accuracy of final parts is highly dependent on those process parameters as well. Though both energy consumption and part geometric accuracy heavily depend on the process parameters in AM processes, jointly considering the dual outputs in AM process is not fully investigated. The proposed study aims to obtain a quantitative understanding of the impact of these process parameters on AM energy consumption given part quality requirements, such as geometric accuracy. The study utilizes a MakerGear M2 fused deposition modeling (FDM) 3D printer to complete the designed experiments. By implementing experimental design and statistical regression analysis technologies, the study quantifies the correlation between AM process parameters and energy consumption as well as the final geometric accuracy measure. An optimization framework is proposed to minimize the energy consumption per part. The Kuhn-Tucker non-linear optimization algorithm is used to identify the optimal process parameters. This study is of significance to AM energy consumption in terms of jointly considering energy consumption and final part geometric accuracy in the optimization framework.