# Time-Series Prediction of Failures in an Industrial Assembly Line Using Artificial Learning
## 5 Conclusions  
This study focuses on the modelling and prediction of operational failures within aerospace assembly lines to enable proactive defect mitigation. Accurate prediction of  
Figure 16: Training performance of the NAR network: (**a**) gradient, (**b**) \(mu\), and (**c**) validation status. failure patterns is critical for pre-emptive intervention, as it facilitates the avoidance of costly disruptions in manufacturing processes. The experimental dataset consists of 45,654 time-stamped records from a mounting process variable. To analyse these, supervised machine and deep learning methodologies are implemented, including a multilayer perceptron (MLP), nonlinear autoregressive exogenous network (NARX), adaptive neuro-fuzzy inference system (ANFIS), long short-term memory network (LSTM), gated recurrent unit (GRU), and a hybrid architecture integrating convolutional neural networks (CNNs) with recurrent neural networks (RNNs). These techniques are selected for their capacity to model nonlinear temporal dependencies and sequential behaviours inherent in industrial failure dynamics. The comparative analysis reveals that NAR and MLP are the most effective techniques in terms of both predictive accuracy and computational efficiency. MLP achieved the lowest MAE (2.122), RMSE (1.418), and ME (1.145). NAR closely followed, with a notably high coefficient of determination (\(R^{2}=0.9867\)) and the shortest computation time (4.847 s), indicating strong real-time applicability. ANFIS also demonstrates a competitive accuracy (MAE = 2.805, RMSE = 1.409) and \(R^{2}\) value (0.9875), but at the cost of significantly higher computational overhead due to its iterative clustering mechanism (1000 iterations). GRU and CNN-RNN, although generally effective in modelling temporal dependencies, have relatively higher error rates and training durations (e.g., GRU: MAE = 4.124, time = 1724 s; CNN-RNN: MAE = 5.063, time = 1004 s), which may limit their deployment in latency-sensitive environments. LSTM, while widely recognised for sequential learning, shows suboptimal performance in this case, with the highest MSE (17.297) and longest training time (1826 s). The study demonstrates that simpler architectures such as NAR and MLP can outperform more complex models in specific industrial contexts, particularly when low latency and interpretability are prioritised. It contributes significantly to predictive maintenance strategies. It validates the potential of artificial learning models to mitigate late-stage failures and suggests a path forward for more generalised applications. As a future study potential, the research can be extended by adding exogenous inputs including multi-modal data and domain-specific factors (e.g., human error, environmental effects). Expanding these dimensions could facilitate the broader adoption of AI-driven predictive maintenance in complex manufacturing ecosystems beyond aerospace. Conceptualisation, M.A. and M.C.S. ; methodology, M.A. and M.C.S. ; software, M.A. and M.C.S. ; validation, M.A. and M.C.S. ; formal analysis, M.A. ; investigation, M.A. ; resources, M.A. ; data curation, M.A. ; writing--original draft preparation, M.A. and M.C.S. ; writing--review and editing, M.C.S. ; visualisation, M.A. and M.C.S. ; supervision, M.A. and M.C.S. ; project administration, M.A. and M.C.S. ; funding acquisition, M.A. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author(s). The authors would like to thank [PERSON], Karadeniz Technical University, for providing the opportunity to use Matlab version 9.13.0 (R2022b) for educational purposes. This study has been produced from the Master's Thesis of [PERSON], accepted by Nigde Omer Halisdemir University, Graduate School of Natural and Applied Sciences. The authors appreciate the valuable comments from the reviewers. The authors declare no conflicts of interest.