Background: Conventional engineering systems rely on reactive or scheduled maintenance and rule-based decision-making, limiting responsiveness in complex, data-rich environments. The integration of artificial intelligence (AI), machine learning (ML), and deep learning (DL) with Internet of Things (IoT), digital twins, and edge computing is enabling a transition toward smart engineering.
Objective: This article proposes and evaluates a multi-layer AI-powered smart engineering framework that progresses from predictive analytics to autonomous decision-making, addressing limitations of traditional approaches through real-time sensing, intelligent modeling, and closed-loop control.
Methods: A conceptual framework is defined across data acquisition, processing, AI intelligence, digital-twin, decision-support, and autonomous layers. Simulated evaluation employs representative industrial time-series and sensor datasets processed with classical ML (Random Forest, XGBoost), recurrent and convolutional architectures (LSTM, GRU, CNN), and hybrid models. Performance is assessed via accuracy, F1-score, RMSE, MAE, R², inference latency, and failure-detection rate under cross-validation. Explainability is examined using SHAP-based feature attribution. Results are identified as simulated/hypothetical.
Results: Simulated comparisons indicate that AI-powered approaches outperform rule-based and statistical baselines in predictive maintenance accuracy and anomaly detection, while digital-twin synchronization reduces decision latency. Edge deployment yields acceptable real-time performance with moderate computational overhead. Limitations appear in model generalization across heterogeneous assets and in explainability under high-dimensional inputs.
Conclusion: The framework demonstrates a coherent pathway from raw sensor data to autonomous engineering actions. Remaining challenges in reliability, cybersecurity, computational scalability, and trustworthiness must be resolved to realize fully autonomous, Industry 5.0-aligned engineering ecosystems.