Experimental correlation between acoustic emission and stability in micromilling of different grain-sized materials

Ribeiro, Kandice Suane Barros; Venter, Giuliana Sardi; Rodrigues, Alessandro Roger

doi:10.1007/s00170-020-05711-1

Cited by 10 publications

(2 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4, the AE detection system included AE R6ɑ sensor, 2/4/6 preamplifier and AE win TM software in the robotic milling process. It is well known that acoustic emission is defined as a physical phenomenon in which instantaneous elastic waves are generated within the material due to the rapid release of energy [26]. In the cutting process, the major sources for AE waves include the shear deformation of workpiece in the first deformation zone (shear zone), the plastic deformation of workpiece and the sliding friction between tool and chip in the second deformation zone (chip-tool interface), extrusion and friction between flank face and newly formed surface of workpiece in the third deformation zone (tool flank-workpiece interface), the plastic deformation and breakage in the chip, collisions between chip and tool, tool vibration, and chipping and fracture of the tool [26][27][28], as the details shown in Fig.…”

Section: Mechanisms Of Using Acoustic Emission To Detect Robotic Milling Chattermentioning

confidence: 99%

Chatter stability prediction and detection during high-speed robotic milling process based on acoustic emission technique

Huang

Yang

2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Chatter as a common and thorny problem occurs easily during robotic milling process, leading to the instability, severe tool wear and poor surface finish. In this work, an acoustic emission technique was employed to analyze a chatter phenomenon using root mean square (RMS) value and fast Fourier transform method during high-speed robotic milling of aluminum alloys (with cutting speed up to 678 m/min). A stability lobe diagram was proposed to predict the occurrence of chatter with various spindle speeds, which was considered as the most effective tool for chatter analysis. The underline mechanism and theoretical analysis were also presented to provide physical understanding of chatter stability. The cutting force model and robot structure model were firstly established to study chatter mechanism. The stability of a robotic milling system was then analyzed using a zero-order approximation method. Results showed that fast Fourier transform and the time-domain root mean square (RMS) value of acoustic emission signals could be effectively used for detection and verification of chatter in the robotic milling process. The stable cutting zone in the stability lobe diagram was in agreement with experimental results, which can help for the selection of reasonable cutting parameters to avoid chatter and improve efficiency during the high-speed robotic milling process.

show abstract

Section: Mechanisms Of Using Acoustic Emission To Detect Robotic Milling Chattermentioning

confidence: 99%

Chatter stability prediction and detection during high-speed robotic milling process based on acoustic emission technique

Huang

Yang

2021

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…Beyond their conventional uses in traditional machining, the continuous research on AE allowed it to have newfound popularity in more subtle applications, such as roughness analysis [20] and the study of micromachining processes [21]. The main advantages of using AE sensors rely on their low invasiveness and the possibility to assess different sources of interest during the machining process.…”

Section: Introductionmentioning

confidence: 99%

Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission

Sio-Sever

Leal-Muñoz

Lopez-Navarro

et al. 2020

Sensors

View full text Add to dashboard Cite

This work presents a non-invasive and low-cost alternative to traditional methods for measuring the performance of machining processes directly on existing machine tools. A prototype measuring system has been developed based on non-contact microphones, a custom designed signal conditioning board and signal processing techniques that take advantage of the underlying physics of the machining process. Experiments have been conducted to estimate the depth of cut during end-milling process by means of the measurement of the acoustic emission energy generated during operation. Moreover, the predicted values have been compared with well established methods based on cutting forces measured by dynamometers.

show abstract

In-process chatter detection in micro-milling using acoustic emission via machine learning classifiers

Sestito

Venter

Ribeiro

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Experimental correlation between acoustic emission and stability in micromilling of different grain-sized materials

Cited by 10 publications

References 45 publications

Chatter stability prediction and detection during high-speed robotic milling process based on acoustic emission technique

Chatter stability prediction and detection during high-speed robotic milling process based on acoustic emission technique

Non-Invasive Estimation of Machining Parameters during End-Milling Operations Based on Acoustic Emission

In-process chatter detection in micro-milling using acoustic emission via machine learning classifiers

Contact Info

Product

Resources

About