Machine learning-driven power prediction in continuous extrusion of pure titanium for enhanced structural resilience under extreme loading

Discover Materials, Год журнала: 2025, Номер 5(1)

Опубликована: Янв. 11, 2025

Abstract The increasing demand for advanced materials capable of withstanding extreme loading conditions, such as those encountered during impact or blast events, underscores the need innovative approaches in material processing. This study focuses on leveraging machine learning (ML) to enhance predictive accuracy continuous extrusion CP-Titanium Grade 2, a vital structural resilience critical applications. Specifically, an Artificial Neural Network (ANN) model optimized using Stochastic Gradient Descent (SGD) was introduced forecast power requirements with high precision. analysis utilized published dataset that comprises theoretical, numerical, and experimental calculations robust foundation validation comparison. A visualization highlighted influence process parameters, feedstock temperature wheel velocity, performance align thematic focus resilient design. ANN-SGD achieved RMSE 0.9954 CVRMSE 11.53% which demonstrated significant improvements prediction compared traditional approaches. By achieving superior alignment results, validated its efficacy reliable efficient tool understanding optimizing complex manufacturing processes. research emphasizes potential ML revolutionize processing conditions contribute broader goals sustainable manufacturing.

Язык: Английский

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Geographical perspectives on home-based enterprises in urban nigeria: economic growth and environmental impacts

GeoJournal, Год журнала: 2025, Номер 90(1)

Опубликована: Янв. 29, 2025

Язык: Английский

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Stuck Pipe Detection in Oil and Gas Drilling Operations Using Deep Learning Autoencoder for Anomaly Diagnosis

Applied Sciences, Год журнала: 2025, Номер 15(9), С. 5042 - 5042

Опубликована: Май 1, 2025

Stuck pipe events remain a critical challenge in oil and gas drilling operations, leading to increased non-productive time substantial financial losses. Traditional detection methods rely on manual monitoring expert judgment, which are prone delays human error. This study proposes deep learning autoencoder-based anomaly diagnosis approach enhance the of stuck incidents. Using high-resolution series data from Volve field, autoencoder model was trained exclusively normal conditions learn operational patterns detect deviations indicative events. The proposed leverages reconstruction error as an metric, effectively distinguishing between cases. results demonstrate that achieves accuracy 99.06%, with area under receiver operating characteristic curve (AUC) 0.958. Additionally, attained precision 97.12%, recall 91.34%, F1-score 94.15%, significantly reducing false positives negatives. findings highlight potential learning-based approaches improving real-time detection, offering scalable cost-effective solution for mitigating disruptions. research contributes advancing intelligent systems industry, risks, enhancing efficiency.

Язык: Английский

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A Biomechanical Pilot Case Study on the Effect of Spinal Fusion Surgery on Gait Loading Asymmetry in Scoliosis Patients

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105431 - 105431

Опубликована: Май 1, 2025

Язык: Английский

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Adsorptive Removal of Zinc and Lead Ions from Industrial Wastewater Using Al-Fe Pillared Silty Clay: Kinetic and Isothermal Analysis

Surfaces and Interfaces, Год журнала: 2025, Номер unknown, С. 106824 - 106824

Опубликована: Май 1, 2025

Язык: Английский

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