Abstract
A preventive maintenance embedded for the fused deposition modeling (FDM) printing technique is proposed. A monitoring and control integrated system is developed to reduce the risk of having thermal degradation on the fabricated products and prevent printing failure; nozzle clogging. As for the monitoring program, the proposed temporal neural network with a two-stage sliding window strategy (TCN-TS-SW) is utilized to accurately provide the predicted thermal values of the nozzle tip. These estimated thermal values are utilized to be the stimulus of the control system that performs countermeasures to prevent the anomaly that is bound to happen. The performance of the proposed TCN-TS-SW is presented in three case studies. The first scenario is when the proposed system outperforms the other existing machine learning algorithms namely multi-look back LSTM, GRU, LSTM, and the generic TCN architecture in terms of obtaining the highest training accuracy and lowest training loss. TCN-TS-SW also outperformed the mentioned algorithms in terms of prediction accuracy measured by the performance metrics like RMSE, MAE, and R2 scores. In the second case, the effect of varying the window length and the changing length of the forecasting horizon. This experiment reveals the optimized parameters for the network to produce an accurate nozzle thermal estimation.
Highlights
The innovative way of production method operated by additive manufacturing (AM)or 3-D printing takes the manufacturing process in a whole new perspective [1]
The performance of the proposed temporal convolutional network (TCN)-TS-SW in terms of training loss value as compared to other machine learning algorithms namely multi look-back LSTM (MLBLSTM) [32], gated recurrent unit (GRU), and LSTM is presented on the Figure 4a
The results showed that TCN-TS-SW reaches the lowest training loss value over
Summary
3-D printing takes the manufacturing process in a whole new perspective [1]. With this emerging technology, engineers are up to challenge in transforming their concept on designing a product and at the same time, it is a chance to showcase their creativity in solving modern engineering problems. A different approach is done in [26] where the thermal state of the nozzle has been estimated using a multiphysics simulation software These techniques provide precise nozzle temperature predictions the mentioned procedures are relatively slow for real-time applications. On [28] a cyber-physical system (CPS) can provide an online data user interface where the printing parameters are displayed and according to the authors, they used SVM to extract the data features and a PID control is responsible to stabilize the nozzle temperature They presented a promising technology; previous studies have succeeded controlling the nozzle temperature by using PID control itself [29] and from the mentioned papers the existing monitoring and quality control program was able to detect the anomaly during or moments before it occurs making it difficult to prevent the fault from occurring.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
