This study presents a method for predicting carbon emissions from 3D printing based on the G-code and compares the emissions between FDM and injection molding (IM) processes. Traditional manufacturing methods for (polylactic acid) PLA plastic products involve IM, which require extensive equipment and metal molds to be manufactured first. This process involves repetitive heating and machining, low material utilization, high energy consumption, and high pollution. 3D printing using a gradual accumulation of materials to manufacture parts has transformed traditional manufacturing methods. However, concerns regarding the environmental impact of 3D printing have been raised in previous studies. To achieve sustainability in 3D printing, it is important to make effective adjustments by predicting the carbon emissions before printing. In this study, we propose a method for predicting carbon emissions from 3D printing by integrating processing characteristics with G-code instructions. The deviation of the model was confirmed to be between 3.86% and 5.82% through a case study of FDM 3D printing. The optimal process parameters were determined from carbon emissions predictions, resulting in carbon emissions reductions of up to 43.77%. Carbon emissions were evaluated using both the traditional IM method and FDM 3D printing for manufacturing PLA plastic products. The results showed that the carbon emissions from FDM were significantly lower than those from traditional IM in small batches or customized production. In the traditional IM method, mold manufacturing produces high carbon emissions, accounting for more than 99% of the entire process. The findings of this study offer valuable guidance to manufacturers in selecting appropriate manufacturing methods and formulating production strategies. By predicting carbon emissions in the FDM process and adjusting the parameters based on product quality requirements, significant reductions in carbon emissions can be achieved during the production of small batches or customized PLA plastic products. The carbon emission prediction method proposed in this study presents a more sustainable solution for 3D printing technology, which is crucial for advancing sustainable production and environmental protection in this field.
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