Industrial Internet of Things-Based Fault Mitigation for Smart Additive Manufacturing Using Multi-Flow BiLSTM
Gabriel Avelino Sampedro, Made Adi Paramartha Putra, Jae‐Min Lee, Dong‐Seong Kim
Abstract
The advancement of Industry 4.0 has necessitated the development of reliable predictive maintenance systems in production and manufacturing processes. This need is particularly pronounced in facilities that employ multiple 3D printers concurrently. To address this, the present study proposes the establishment of an Industrial Internet of Things (IIoT)-based industrial setup, incorporating a robust machine-to-machine network. Within this framework, the paper introduces an additive manufacturing plant equipped with IIoT capabilities, focusing on a predictive maintenance technique tailored for Fused Deposition Modeling (FDM) 3D printers. The authors have devised a failure prediction algorithm that leverages a data-driven methodology and a Deep Learning (DL) algorithm called Multi-Flow BiLSTM. The proposed Multi-Flow BiLSTM employs a multi-learning flow process augmented by a residual connection, facilitating a more comprehensive understanding of historical data patterns. Notably, the findings demonstrate that the proposed Multi-Flow BiLSTM achieves an impressive mean absolute error of 2.95, the lowest error rate compared to alternative methods while exhibiting a respectable <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">R</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> value of 0.9121.By employing the proposed predictive maintenance technique, efficient and uninterrupted operation of 3D printers can be ensured, thereby enhancing the overall productivity of the manufacturing plant. Moreover, the study’s results offer valuable insights that can inform further research in developing predictive maintenance systems for industrial applications.