Suchergebnisse - "Rahman, Achmad Zaki"

A feature extraction method for intelligent chatter detection in the milling process.

Autoren: Jauhari, Khairul Rahman, Achmad Zaki Al Huda, Mahfudz et al.

Quelle: Journal of Intelligent Manufacturing; Oct2025, Vol. 36 Issue 7, p5113-5139, 27p

Schlagwörter: FEATURE extraction, MILLING machinery, MACHINE learning, MATHEMATICAL optimization, VIBRATION (Mechanics), UNCERTAINTY (Information theory), SIGNAL processing

Building digital-twin virtual machining for milling chatter detection based on VMD, synchro-squeeze wavelet, and pre-trained network CNNs with vibration signals.

Autoren: Jauhari, Khairul Rahman, Achmad Zaki Al Huda, Mahfudz et al.

Quelle: Journal of Intelligent Manufacturing; Oct2024, Vol. 35 Issue 7, p3083-3114, 32p

Schlagwörter: CONVOLUTIONAL neural networks, ARTIFICIAL intelligence, COMPUTERS, VIRTUAL machine systems, SIGNAL processing

Flank wear detection with signal variational mode decomposition and support vector machine algorithm.

Autoren: Rahman, Achmad Zaki Jauhari, Khairul Al Huda, Mahfudz et al.

Quelle: AIP Conference Proceedings; 2024, Vol. 3069 Issue 1, p1-12, 12p

Schlagwörter: MACHINE learning, STANDARD deviations, SUPPORT vector machines, SIGNAL detection, SURFACE finishing, CUTTING tools

Static and dynamic analysis of computer numerical control (CNC) mother machine structure.

Autoren: Krisbudiman, Arif Widodo, Barep Luhur Suryaningrat, Albertus Rianto et al.

Quelle: AIP Conference Proceedings; 2024, Vol. 3069 Issue 1, p1-12, 12p

Schlagwörter: MACHINE tool industry, NUMERICAL control of machine tools, MODE shapes, FREQUENCIES of oscillating systems, SAFETY factor in engineering, DEAD loads (Mechanics), DYNAMIC loads, MACHINE tools, MILLING-machines

Vibration response analysis in Turn-Milling of SUS 410 using raster and vortex strategy.

Autoren: Majid, Nur Cholis Azka, Muizuddin Aziz, Amiruddin et al.

Quelle: AIP Conference Proceedings; 2024, Vol. 3069 Issue 1, p1-11, 11p

Schlagwörter: TURBINE blades, NUMERICAL control of machine tools, MACHINING, STAINLESS steel, RAW materials

Innovative tool condition classification: utilizing time–frequency moments as inputs for BiLSTM networks in milling processes.

Autoren: Rahman, Achmad Zaki Jauhari, Khairul Huda, Mahfudz Al et al.

Quelle: Journal of the Brazilian Society of Mechanical Sciences & Engineering; Sep2024, Vol. 46 Issue 9, p1-19, 19p

Correlation Analysis of Vibration Signal Frequency with Tool Wear During the Milling Process on Martensitic Stainless Steel Material.

Autoren: Rahman, Achmad Zaki Jauhari, Khairul Al Huda, Mahfudz et al.

Quelle: Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ); Aug2024, Vol. 49 Issue 8, p10573-10586, 14p

Schlagwörter: MARTENSITIC stainless steel, FREQUENCIES of oscillating systems, STATISTICAL correlation, MACHINABILITY of metals, PEARSON correlation (Statistics), THERMAL conductivity

Enhancing Turbine Blade Manufacturing through MEMS-Based Milling Monitoring.

Autoren: Rahman, Achmad Zaki Jauhari, Khairul Huda, Mahfudz Al et al.

Quelle: IOP Conference Series: Earth & Environmental Science; 2024, Vol. 1344 Issue 1, p1-14, 14p

An intelligent milling chatter detection method based on VMD-synchro-squeeze wavelet and transfer learning via deep CNN with vibration signals.

Autoren: Jauhari, Khairul Rahman, Achmad Zaki Al Huda, Mahfudz et al.

Quelle: International Journal of Advanced Manufacturing Technology; Nov2023, Vol. 129 Issue 1/2, p629-657, 29p

Schlagwörter: CONVOLUTIONAL neural networks, OPTIMIZATION algorithms, ARTIFICIAL intelligence, FEATURE extraction, DEEP learning, DIGITAL technology