# Assessing Energy Efficiency in Desktop-Size FFF 3D Printers
## 1 Introduction  
Manufacturing cost is a crucial indicator for any production method. In the context of products manufactured via the Fused Filament Fabrication (FFF) technique, whether in domestic or industrial environments, this cost is primarily composed of three factors: machine and operator time, raw material expenses (filament and auxiliary substances), and the cost of electricity required for production [1, 2]. While the first two factors are relatively straightforward to estimate--given that CAM software (commonly known as slicers) can often predict these values--estimating the energy needed for producing a specific part presents challenges, even for approximate calculations [3]. Energy consumption in production is essential not only for calculating manufacturing costs but also in industrial settings, where energy management is increasingly prioritized [3, 4]. As companies adopt energy management systems under ISO 50001 [5], the need for accurate energy tracking and optimization across all processes becomes evident. Compliance with this standard necessitates either comprehensive measurement or reliable reference data [6, 7]. In the Fused Filament Fabrication (FFF) domain of additive manufacturing, these requirements introduce distinct challenges. FFF's energy consumption varies widely due to parameters such as layer height, print speed, and infill density, each impacting energy efficiency differently [7]. For example, primary energy is expended on material extrusion, while secondary energy supports processes like heating and cooling. Studies indicate that optimizing parameters, such as reducing infill density by over 50% without compromising structural integrity and insulating the print head, can reduce energy use by up to 30% [8, 9]. Energy mapping techniques are critical for visualizing energy flows in additive manufacturing. Through tools like Sankey diagrams, this research methodology identifies potential energy-saving areas, allowing companies to make data-driven adjustments to reduce costs and enhance process efficiency. With industry growth, such methodologies are essential for establishing reliable, compliant, and sustainable energy practices [6]. Energy consumption in the FDM manufacturing process also carries significant environmental implications [8]. Currently, energy generation is a major contributor to air pollution, associated with emissions of gases like carbon dioxide, nitrogen oxides, sulfur oxides, and particulate matter (PM10, PM2.5). These pollutants not only worsen air quality but also exacerbate global warming, with each kWh consumed in manufacturing releasing approximately 0.836 kg of CO2 [10]. Emissions from fossil fuel power plants, such as carbon dioxide, sulfur dioxide, nitrogen oxides, and particulate matter, pose health risks, including respiratory and cardiovascular diseases, and contribute to environmental issues like acid rain and climate change. Reducing electricity consumption can diminish the demand for fossil-fuel-based power generation, thereby mitigating these emissions. Enhancing energy efficiency, particularly through energy-conscious 3D printer selection, is one effective strategy. Selecting energy-efficient 3D printers can substantially reduce electricity usage during manufacturing, resulting in fewer emissions. Companies and individuals should consider energy requirements when choosing 3D printers for various applications, a practice that supports cleaner air and promotes sustainable operations. For most household appliances in the European Union, product labeling mandates include basic energy efficiency information, such as energy class and energy consumption per operational cycle or over extended periods. However, there are no such guidelines for FFF devices, which means manufacturers are not required to provide this information, making it challenging to compare devices at the point of purchase. Researchers nonetheless anticipate that the adoption of 3D printing could contribute to overall energy savings [11].