Accurate Energy Consumption Prediction for Cleaner Fused Deposition Modeling (FDM)

Additive Manufacturing (AM) has revolutionized rapid prototyping and mechanical production in a range of different fields, including e-mobility and aerospace, as well as personalized medical devices. In an era dominated by increasing environmental awareness, requirements to reduce CO2 emissions and climate change associated with manufacturing energy consumption are paramount, further to the development of clean energy systems. With the aim of achieving cleaner production, accurate prediction of energy consumption during additive manufacturing processes is the first step in obtaining tangible reductions in manufacturing-related emissions. Energy optimization not only promotes environmental sustainability, but also gives an additional competitive advantage to additive manufacturing processes in today's market, bringing considerable benefits to these technologies. The present work provides an in-depth study into energy consumption during additive manufacturing, applying a novel and highly accurate methodology for evaluating electrical energy usage during Fused Deposition Modeling (FDM) as a test case for applying this approach to a wider range of additive manufacturing technologies such as Laser Powder Bed Fusion (LPBF), Selective Laser Sintering (SLS), Stereolithography (SLA) and others. FDM is renowned for its popularity in multiple sectors due to its versatility in achieving customized results at low cost with relative ease and safety. To date, the vast majority of scientific literature relating to additive manufacturing has focused on the mechanical properties of printed objects, topology optimization, support structures and energy consumption. Investigations into the energy consumption during additive manufacturing have mainly focused on the measurement and estimation of total energy consumption over the entire printing process based on general considerations and, in some cases, machine learning algorithms. The present study distinguishes itself from previous works by providing precise and detailed analysis of each individual step in the printing process. Experimental evaluation of each machine action was performed, allowing translation of the machine instructions (G-code) employed for printing an object into distinct energy contributions for each action during the process. This approach makes it possible to estimate the total energy consumption very accurately as the sum of all energy contributions, allowing an additional energy optimization parameter to be introduced. Such a detailed analysis allows the main drivers of energy consumption during FDM to be identified, leading to relatively simple solutions to dramatically reduce energy consumption during the process. The presented approach was developed as a test bed for detailed evaluation of energy consumption during additive manufacturing, with potential for application over a wide range of different technologies with similar approaches.