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Dynamic Model of the Machining Process on the Basis of Neural Networks: From Simulation to Real Time Application.

, , , , and . International Conference on Computational Science (3), volume 2331 of Lecture Notes in Computer Science, page 574-583. Springer, (2002)

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Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System., , , and . Sensors, 17 (9): 2109 (2017)Embedded fuzzy-control system for machining processes: Results of a case study., , , , and . Comput. Ind., 50 (3): 353-366 (2003)Embedded Fuzzy Control System in an Open Computerized Numerical Control; A Technology Transfer Case-Study., , , , and . IFSA, volume 2715 of Lecture Notes in Computer Science, page 442-449. Springer, (2003)A two-step machine learning approach for dynamic model selection: A case study on a micro milling process., , , , , and . Comput. Ind., (2022)A Distributed Intelligent Monitoring System Applied to a Micro-scale Turning Process., , and . IWANN (2), volume 5518 of Lecture Notes in Computer Science, page 338-345. Springer, (2009)Optimal Fuzzy Control for a Time-Delay System Using Simulated Annealing: An Application to High-Performance Drilling., , and . ICNC (5), page 639-643. IEEE Computer Society, (2007)Transductive-Weighted Neuro-Fuzzy Inference System for Tool Wear Prediction in a Turning Process., , , and . HAIS, volume 5572 of Lecture Notes in Computer Science, page 113-120. Springer, (2009)Conductance sensor for micromachining. A case study on monitoring tool-workpiece contact., , , and . ICIT, page 1422-1426. IEEE, (2015)Dynamic Model of the Machining Process on the Basis of Neural Networks: From Simulation to Real Time Application., , , , and . International Conference on Computational Science (3), volume 2331 of Lecture Notes in Computer Science, page 574-583. Springer, (2002)Artificial intelligence-based modelling and optimization of microdrilling processes., , , , and . CIES, page 49-53. IEEE, (2014)