Intelligent G-code-based power prediction of ultra-precision CNC machine tools through 1DCNN-LSTM-Attention model

Xu, Zhicheng; Selvaraj, Vignesh; Min, Sangkee

doi:10.1007/s10845-023-02293-z

Cited by 6 publications

(1 citation statement)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Accuracy, Sensitivity, Precision, and F-score metrics used in the literature to measure the performance of classification problems were applied in estimating postoperative patient survival [20,21]. In order to calculate these performance measurement metrics, the confusion matrix of the classification process had first be determined [22,23].…”

Section: Evaluation Metricsmentioning

confidence: 99%

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Bülbül,

Işık

2024

Biomimetics

View full text Add to dashboard Cite

The prediction of patient survival is crucial for guiding the treatment process in healthcare. Healthcare professionals rely on analyzing patients’ clinical characteristics and findings to determine treatment plans, making accurate predictions essential for efficient resource utilization and optimal patient support during recovery. In this study, a hybrid architecture combining Stacked AutoEncoders, Particle Swarm Optimization, and the Softmax Classifier was developed for predicting patient survival. The architecture was evaluated using the Haberman’s Survival dataset and the Echocardiogram dataset from UCI. The results were compared with several Machine Learning methods, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Neural Networks, Gradient Boosting, and Gradient Bagging applied to the same datasets. The findings indicate that the proposed architecture outperforms other Machine Learning methods in predicting patient survival for both datasets and surpasses the results reported in the literature for the Haberman’s Survival dataset. In the light of the findings obtained, the models obtained with the proposed architecture can be used as a decision support system in determining patient care and applied methods.

show abstract

Section: Evaluation Metricsmentioning

confidence: 99%

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Bülbül,

Işık

2024

Biomimetics

View full text Add to dashboard Cite

show abstract

Deep learning based self-adaptive modeling of multimode continuous manufacturing processes and its application to rotary drying process

Wang,

Zheng,

et al. 2024

J Intell Manuf

View full text Add to dashboard Cite

3D printing for ultra-precision machining: current status, opportunities, and future perspectives

He,

Yip,

Yan

et al. 2024

Front. Mech. Eng.

View full text Add to dashboard Cite

Additive manufacturing, particularly 3D printing, has revolutionized the manufacturing industry by allowing the production of complex and intricate parts at a lower cost and with greater efficiency. However, 3D-printed parts frequently require post-processing or integration with other machining technologies to achieve the desired surface finish, accuracy, and mechanical properties. Ultra-precision machining (UPM) is a potential machining technology that addresses these challenges by enabling high surface quality, accuracy, and repeatability in 3D-printed components. This study provides an overview of the current state of UPM for 3D printing, including the current UPM and 3D printing stages, and the application of UPM to 3D printing. Following the presentation of current stage perspectives, this study presents a detailed discussion of the benefits of combining UPM with 3D printing and the opportunities for leveraging UPM on 3D printing or supporting each other. In particular, future opportunities focus on cutting tools manufactured via 3D printing for UPM, UPM of 3D-printed components for real-world applications, and post-machining of 3D-printed components. Finally, future prospects for integrating the two advanced manufacturing technologies into potential industries are discussed. This study concludes that UPM is a promising technology for 3D-printed components, exhibiting the potential to improve the functionality and performance of 3D-printed products in various applications. It also discusses how UPM and 3D printing can complement each other.

show abstract

Intelligent G-code-based power prediction of ultra-precision CNC machine tools through 1DCNN-LSTM-Attention model

Cited by 6 publications

References 46 publications

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Survival Prediction of Patients after Heart Attack and Breast Cancer Surgery with a Hybrid Model Built with Particle Swarm Optimization, Stacked AutoEncoders, and the Softmax Classifier

Deep learning based self-adaptive modeling of multimode continuous manufacturing processes and its application to rotary drying process

3D printing for ultra-precision machining: current status, opportunities, and future perspectives

Contact Info

Product

Resources

About