Intelligent monitoring and detection of chatter in ball-end milling process on CNC machining center

Pongsathornwiwat, Narongsak; Tangjitsitcharoen, Somkiat

doi:10.1109/iccie.2010.5668378

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…The axial force (F z ) is typically much smaller than F x (cutting feed force) and F y (radial cutting force), so it does not contribute to the chatter (Sutherland, 1987). Pongsathornwiwat and Tangjitsitcharoen (2010) found that the force along the cutting feed (F x ) is larger than the cutting force along the radial depth of cut direction (F y ), which is experimentally validated by authors. Due to larger cutting force along the cutting feed, the amplitude of vibration along cutting feed is considered for analysis.…”

Section: Methodsmentioning

confidence: 57%

Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

Koohestani

Yang

2014

Machining Science and Technology

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Significant research effort has been carried out in the detection of chatter, which is one of the main barriers against titanium milling. State-of-the-art techniques are unable to satisfy requirements of industry in terms of in-process chatter detection. The present study reports the use of sensor-signal driven reconstructed phase space attractors combined with image correlation as a solution of chatter prediction during milling of titanium in industry. The method uses Poincaré sections of reconstructed phase space attractor as patterns to identify the onset of chatter in the apparently random behavior of vibrations in the milling process. Image correlation of Poincaré sections indicates the onset of chatter in the milling process.

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Section: Methodsmentioning

confidence: 57%

Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

Koohestani

Yang

2014

Machining Science and Technology

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“…The results showed that at chatter, the maximum value of cutting forces increased by at least 60% and its standard variation by about 80%. Wei et al [114] utilized only the signal in the vertical direction (Z), while Tangjitsitcharoen and Pongsathornwiwat [74,226] registered the three axes and calculated the average variance of each axis and the normalized ratio of these variances (X/Y, X/Z and Y/Z) in a 1-s frame. In chatter condition, these ratios increased for two different cutter types, flat and ball nose end mills.…”

Section: Dynamometer-cutting Forcesmentioning

confidence: 99%

Chatter detection in milling processes—a review on signal processing and condition classification

Navarro-Devia

Chen

Dao

et al. 2023

Int J Adv Manuf Technol

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Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing.

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“…[34] that the cutting force approaches a Gaussian distribution when chatter occurs is questionable. Furthermore, most time series analysis methods [28,29,31,32,[34][35][36][37]39,40] cannot identify the chatter frequency. [35] is computationally very efficient but sensitive to the measurement errors and various transient events in the cutting process.…”

Section: Introductionmentioning

confidence: 99%

A Model-Based Computationally Efficient Method for On-Line Detection of Chatter in Milling

Ma¹,

Melkote

Castle

2013

Journal of Manufacturing Science and Engineering

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This paper presents a model-based computationally efficient method for detecting milling chatter in its incipient stages and for chatter frequency estimation by monitoring the cut-ting force signals. Based on a complex exponentials model for the dynamic chip thick-ness, the chip regeneration effect is amplified and isolated from the cutting force signal for early chatter detection. The proposed method is independent of the cutting conditions. With the aid of a one tap adaptive filter, the method is shown to be capable of distinguish-ing between chatter and the dynamic transients in the cutting forces arising from sudden changes in workpiece geometry and tool entry/exit. To facilitate chatter suppression once the onset of chatter is detected, a time domain algorithm is proposed so that the dominant chatter frequency can be accurately determined without using computationally expensive frequency domain transforms such as the Fourier transform. The proposed method is experimentally validated. [DOI: 10.1115/1.4023716]

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Intelligent monitoring and detection of chatter in ball-end milling process on CNC machining center

Cited by 6 publications

References 6 publications

Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

Stability Prediction of Titanium Milling With Data Driven Reconstruction of Phase-Space

Chatter detection in milling processes—a review on signal processing and condition classification

A Model-Based Computationally Efficient Method for On-Line Detection of Chatter in Milling

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