Artificial Evolution and Design for Multi-Material Additive Manufacturing.

Limitations of the traditional manufacturing methods often force engineered components to be made of single material systems. However, this is going through changes due to the advent of additive manufacturing (AM) methods, as the point-by-point consolidation allows for a possible change of the material constitution within a given part domain. This will give rise to a plethora of new material and property options for the designers, where just human perception may fail to realize the full benefits. Automated design tools integrating material choice, dispersion, analysis, and optimization algorithms need to be developed to assist in finding the optimal multi-material dispersion solutions achieving given performance criteria sets. Considering the fact that the multi-material manufacturing systems are only recently coming into use, design solutions targeting optimal placement of multiple materials are not common. This article addresses this gap, evaluating a numerical model integrated with different optimization schemes to find the optimal material solutions achieving certain preset performance criteria such as combinations of natural frequencies in different degrees of freedom. A case study of three different metaheuristic optimization schemes based on genetic algorithms indicates, first, that it is possible to create a beam with six uniformly spaced natural frequencies and to change these frequencies without modifying the structural geometry; and second that the basic genetic algorithm generally outperforms neural net-based alternatives for this problem. This tailoring of the structural resonance spectrum demonstrates that evolutionary computing combined with multi-material AM can be used to unlock previously unavailable structural functionality.