Additive manufacturing processes have considerable potential for the production of sustainable parts and products. This is because these processes enable the fabrication of lightweight components reducing the amount of material needed for part production. However, additive manufacturing processes such as the material extrusion process are often energy-intensive. This suggests that there is significant potential to reduce the carbon footprint of the corresponding parts by lowering the emissions attributed to energy consumption. The composition of the electricity mix and the resulting carbon emissions can undergo substantial fluctuations throughout the day, depending on the availability of energy obtained from sustainable low emission sources. This paper proposes a novel methodology that incorporates the predicted energy mix into the production process planning for the material extrusion process. This methodology enables users to select an optimal timeslot for the production of their parts, thereby reducing the resulting carbon footprint by leveraging the electricity mix. The method is augmented by a production management platform that guides users through the process and facilitates the selection of a suitable production machine and timeslot based on availability and the predicted energy mix. The proposed method is validated by the production of a sample part under different decision scenarios, demonstrating achievable emission savings as well as the limitations of the energy mix prediction. The novel method contributes to a more sustainable approach to production planning for the material extrusion process, thereby advancing a more holistic approach to sustainable production of parts manufactured by the material extrusion process.
Osterod et al. (Thu,) studied this question.