Improving energy performance in the product design for additive manufacturing using a multi-player competition algorithm

To assess and confirm the environmental benefits of additive manufacturing (AM), it is crucial to consider environmental issues during the design stage. The energy use of AM processes can be a great contributor to the overall environmental impacts in the cradle-to-gate stage, and therefore, the assessment and improvement of the energy performance for AM during the design stage is now an emerging research task. In the literature, the energy performance is usually improved during process planning or the process chain/network design, whereas the product design for AM (DfAM) is rarely considered in the energy performance improvement so far. Therefore, the chances that would be contributed to energy performance improvement by changing product features are not fully exploited. To address this issue, we propose a framework to enable energy performance-oriented DfAM. The core of our framework is a computation procedure called a 'multi-player competition algorithm (MPCA)'. In MPCA, the energy performance improvement in DfAM is modeled as a game of multiple players picking up design variants from a given domain with the aim of finding out the design variant leading to the highest energy performance. Based on two use cases, energy demand reduction rates of up to 3.9% and 5.8% by MPCA during the DfAM stage are observed.

Automated summary

To evaluate and confirm the environmental benefits of AM, it is important to consider environmental issues during the design stage. Improving energy performance in AM has been mostly focused on process planning or the process chain/network design, with product design for AM (DfAM) rarely being considered. To address this issue, a framework called the multi-player competition algorithm (MPCA) is proposed, where the energy performance improvement in DfAM is modeled as a game of multiple players picking design variants to find the highest energy performance. MPCA has shown energy demand reduction rates of up to 3.9% and 5.8% in two use cases during the DfAM stage.