Results 41 to 50 of about 12,237 (275)

Prediction, detection, and suppression of regenerative chatter in milling

open access: yesAdvances in Mechanical Engineering, 2022
In metal cutting processing, especially in the processing of low-rigidity workpieces, chatter is a key factor affecting many aspects such as surface quality, processing efficiency and tool life.
Bo Liu   +4 more
doaj   +1 more source

Chatter detection on thin wall machining of alluminum alloy under MQL condition [PDF]

open access: yes, 2016
The purpose of this project was to analyze and study the chatter detection on thin wall machining of aluminum alloy under mql condition. Initially, 3 difference type of thin wall design are selected which is L shape, T shape and Pocket shape, then the ...
Amirul Syafiq, Ibrahim
core  

Hopfield Neural Networks for Online Constrained Parameter Estimation With Time‐Varying Dynamics and Disturbances

open access: yesInternational Journal of Adaptive Control and Signal Processing, EarlyView.
This paper proposes two projector‐based Hopfield neural network (HNN) estimators for online, constrained parameter estimation under time‐varying data, additive disturbances, and slowly drifting physical parameters. The first is a constraint‐aware HNN that enforces linear equalities and inequalities (via slack neurons) and continuously tracks the ...
Miguel Pedro Silva
wiley   +1 more source

Suppression of period doubling chetter in high-speed milling by spindle speed variation [PDF]

open access: yes, 2011
Spindle speed variation is a well known technique to suppress regenerative machine tool vibra- tions, but it is usually considered to be effective only for low spindle speeds.
Arnaud, Lionel   +4 more
core   +3 more sources

Hard‐Magnetic Soft Millirobots in Underactuated Systems

open access: yesAdvanced Robotics Research, EarlyView.
This review provides a comprehensive overview of hard‐magnetic soft millirobots in underactuated systems. It examines key advances in structural design, physics‐informed modeling, and control strategies, while highlighting the interplay among these domains.
Qiong Wang   +4 more
wiley   +1 more source

Deep Neural Network Tool Chatter Model for Aluminum Surface Milling Using Acoustic Emmision Sensor

open access: yesMATEC Web of Conferences, 2018
Chatter is a self-excited vibration in any machining processes which contributes to the system instability due to resonance and resulting in an inaccuracy in machining product.
Abul Hasan M.   +3 more
doaj   +1 more source

Multi-station deep learning on geodetic time series detects slow slip events in Cascadia

open access: yesCommunications Earth & Environment, 2023
Slow slip events (SSEs) originate from a slow slippage on faults that lasts from a few days to years. A systematic and complete mapping of SSEs is key to characterizing the slip spectrum and understanding its link with coeval seismological signals.
Giuseppe Costantino   +5 more
doaj   +1 more source

Application of the stability lobes theory to milling of thin workpieces, experimental approach [PDF]

open access: yes, 2003
The optimisation of cutting conditions in High Speed Machining (HSM) requires the use of a vibratory approach in order to avoid a fast deterioration of the tool and of the spindle, as well as a loss of quality of the surface rough- ness.
Arnaud, Lionel, Dessein, Gilles
core  

Nonlinear dynamics of a regenerative cutting process [PDF]

open access: yes, 2012
We examine the regenerative cutting process by using a single degree of freedom non-smooth model with a friction component and a time delay term. Instead of the standard Lyapunov exponent calculations, we propose a statistical 0-1 test analysis for chaos
Litak, Grzegorz   +2 more
core   +2 more sources

Robust Reinforcement Learning Control Framework for a Quadrotor Unmanned Aerial Vehicle Using Critic Neural Network

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
Quadrotor unmanned aerial vehicle control is critical to maintain flight safety and efficiency, especially when facing external disturbances and model uncertainties. This article presents a robust reinforcement learning control scheme to deal with these challenges.
Yu Cai   +3 more
wiley   +1 more source

Home - About - Disclaimer - Privacy