Metals-additive manufacturing (MAM) is enabling unprecedented design freedom and the ability to produce significantly lighter weight parts with the same performance, offering the possibility of significant environmental and economic benefits in many different industries. However, the total production costs of MAM will need to be reduced substantially before it will be widely adopted across the manufacturing sector. Current topology optimization approaches focus on reducing total material volume as a means of reducing material costs, but they do not account for other production costs that are influenced by a part’s structure such as machine time and scrap. Moreover, concurrently optimizing MAM process variables with a part’s structure has the potential to further reduce production costs. This paper demonstrates an approach to use process-based cost modeling in MAM topology optimization to minimize total production costs, including material, labor, energy, and machine costs, using cost estimates from actual MAM operations. The approach is demonstrated in a simple case study of a Ti64 cantilever produced with electron beam melting (EBM). Results of a concurrent optimization of the part structure and EBM process variables are compared to an optimization of the part structure alone. The results show that, once process variables are considered, it is more cost effective to include more material in the part through a combination of (1) building additional thin trusses with a faster laser speed and (2) increasing the thickness of other truss members and decreasing laser velocity to create larger melt pools that reduce the number of passes required, thereby reducing build time. Concurrent optimization of the part’s structure and MAM process parameters leads to 7% lower estimated total production costs and approximately 50% faster build time than optimizing the part’s structure alone.
Runze Huang, Erva Ulu, Levent Burak Kara, Kate S. Whitefoot. (2017). Cost Minimization In Metal Additive Manufacturing Using Concurrent Structure And Process Optimization. ASME International Design Engineering Technical Conferences/DAC., 2017, Cleveland, O.H.