An integrated IoT and CNN system detects cracks in additive manufacturing with 99.54% accuracy and supports predictive maintenance via digital twins.
International Journal of Advanced Manufacturing Technology 116:1763–1782
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XGBoost outperforms DNN, AdaBoost, and ElasticNet on PMEDM datasets with powder and vibration features; MOEAs including NSGA-II optimize the resulting Pareto frontier.
citing papers explorer
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IoT-Enhanced CNN-Based Labelled Crack Detection for Additive Manufacturing Image Annotation in Industry 4.0
An integrated IoT and CNN system detects cracks in additive manufacturing with 99.54% accuracy and supports predictive maintenance via digital twins.
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Multi-objective Evolutionary Algorithms (MOEAs) in PMEDM -- A Comparative Study in Pareto Frontier
XGBoost outperforms DNN, AdaBoost, and ElasticNet on PMEDM datasets with powder and vibration features; MOEAs including NSGA-II optimize the resulting Pareto frontier.