# Energy Consumption Prediction for Fused Deposition Modelling 3D Printing Using Machine Learning
## 2 Literature Review  
A significant number of research studies investigated the estimation and the optimization of energy use in AM Processes by following various modeling approaches, such as mathematical and mechanical modeling, based on the process's physics and artificial intelligence modeling. A prediction model of energy consumption and printing time based on the working states of machine components and process parameters was proposed [17].The proposed model has certain limitations in the printing phase; the prediction model did not take into account the time needed for the empty travel, acceleration, and deceleration of the nozzle movement. A data fusion method was suggested based on the convolutional neural network and long short-term memory CNN-LSTM model for AM energy consumption prediction [18]. For validating the proposed model, a case study was carried out using an SLS system. The CNN-LSTM-developed model does not predict accurately; this may be due to a significant loss of information during the convolution feature extraction process because the sliced images are less informative. An energy consumption prediction model for AM systems was developed that incorporates clustering techniques and deep learning to integrate the multi-source data collected using the Internet of Things [19]. For validating the approach, a case study is conducted on the basis of actual data collected on the SLS process, which demonstrated the merits of the proposed approach. In [20], authors presented the implementation of a tool to predict the energy flows occurring in direct metal laser sintering. A model for assessing energy and material consumption for binder jetting was developed [21]. A comparative study on energy consumption made at the process level for conventional and additive manufacturing processes was presented [22]. In [23], authors developed an approach based on the NC codes to estimate the material usage, time, and energy consumption of 3D-printed models. They validated their approach with two experiments, both of which present deviations of around 9% and 11% from the theoretical values. A deep learning algorithm (ANN) was applied to predict the part mass, support material mass, and build time of voxelized objects for an AM process [24]. The results of the model were compared to results generated afterwards. This prediction model had low accuracy, with determination coefficients of 46.8% for part mass, 30.1% for support material mass, and 22.5% for build time. A model and an energy profiler was developed to simulate the energy cost of the printing process [11]. From the obtained results, they suggested a cross-layer energy optimization solution, called 3 DGates; it was assessed over 338 benchmarks on a 3D printer and realized a 25% reduction in energy consumption. In [25], researchers built an energy consumption model and calculated the amount of energy used in wire-based additive-subtractive hybrid manufacturing and powder-based hybrid additive-subtractive manufacturing. They observed that the wire-based and powder-based methods used similar amounts of energy. Potential environmental implications of AM were described in [26], such as energy use, waste, lifecycle impact, and occupational health, noting that AM technologies use more energy than conventional manufacturing technologies. A mathematical model was developed for estimating the energy consumption of stereolithography processes [2].The obtained results were validated experimentally, the effects of different parameters on the total energy consumption was studied, and an optimization method was proposed to minimize energy consumption based on an optimal combination of parameters. In [27], the operating procedure was analyzed and the impact of printing parameters on the energy consumption and particulates emissions was studied. The power profile analysis illustrated that print bed heating and temperature maintenance consume significant amount of energy during the FDM process.