3D Printer State Monitoring Mobile Application through a Deep Learning Approach

Devices used in additive manufacturing in fast prototyping frequently have faults and issues that device operators are unaware of. Failures that go undetected may damage the device and cause the manufactured product to be faulty, thus costing additional time and resources. The research objective is to design a web-based application that monitors 3D printing operations and predicts future printer temperature values. This paper compares various deep learning (DL) algorithms, including the multilayer perceptron (MLP), long short-term memory (LSTM), and convolutional neural network (CNN). There will also be a comparative analysis of the various DL algorithms for predicting future temperature values. The system will use a web-based application connected to an Internet of Things (IoT)-based system in charge of data collection from multiple sensors attached to the device. When the model is tested, forecasting measures such as the root mean square error (RMSE), mean average error (MAE), mean absolute percentage error (sMAPE), and r-square (R²) metrics will be examined. Based on the results of the various experiments conducted, the use of LSTM is observed to perform the best, with an RSME of 0.4062, MAE of 0.2176, sMAPE of 0.1037, and $\mathrm{R}^{2}$ of 0.7095.