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

Ma, Lei; Melkote, Shreyes N.; Castle, James

doi:10.1115/1.4023716

Cited by 31 publications

(4 citation statements)

References 54 publications

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“…Frequency domain methods are also utilized to obtain decomposition parameters, estimate frequency bands, detect chatter occurrence or validate a new chatter model, as seen in [73,88,119,162,218,332,333]. For instance, Afazov and Scrimieri [334] proposed a Digital Twin model for chatter in milling and utilized the FFT of measured signals for model validation.…”

Section: Frequency Domain Analysismentioning

confidence: 99%

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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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Section: Frequency Domain Analysismentioning

confidence: 99%

“…* Two or more signal processing methods combined[91,136,152,159,165,179,192,196,220,348] Other methods[72,73,84,95,101,135,137,147,148,150,155,157,161,164,168,173,190,227,258,259] …”

mentioning

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

View full text Add to dashboard Cite

show abstract

“…Thus, the chatter feature based on the filtered chatter signal is independent of machining parameters. However, it is a challenge to determine the number of spaced notches of the comb filter [10]. This disadvantage limits the application of comb filter-based methods in early chatter detection.…”

Section: Introductionmentioning

confidence: 99%

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

et al. 2021

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Real-time detection of early chatter is a vital strategy to improve machining quality and material removal rate in the high-speed milling processes. This paper proposes a maximum entropy (MaxEnt) feature-based reliability model method for real-time detection of early chatter based on multiple sampling per revolution (MSPR) technique and second-order reliability method (SORM). To enhance the detection reliability, the MSPR is used to acquire multiple sets of once-per-revolution sampled data (i.e., MSPR data) and to overcome the shortcoming of the once-per-revolution sampling. The proposed MaxEnt feature-based reliability model method solves the issue of the real-time detection of early chatter while ensuring its reliability. The failure hazard function (FHF) is estimated as a chatter indicator by using the SORM with the MaxEnt feature. The proposed method consists of five steps. First, set the prior parameters. Then collect data by using the MSPR technique. Next, calculate a set of the standard deviation of the data collected as a chatter feature and estimate the chatter indicator FHF by applying the SORM with the MaxEnt feature. Finally, implement the real-time detection of early chatter based on the estimated chatter indicator FHF and the threshold FHF0. The proposed method is applied to the high-speed milling process. Two examples prove that the proposed method can detect two kinds of early chatter: the early-stage of a severe chatter and the slightly intolerable chatter.

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“…JAP acknowledges the support of the NSF under grant DMS-1622301 and DARPA under grant HR0011-16-2-003. modeling difficulties by investigating the cutting signal itself. Currently available in-process methods for studying chatter typically rely on comparing the characteristics of the acoustic, vibration, or force signals against certain predefined features indicative of chatter (Tlusty and Andrews (1983); Delio et al (1992); Gradisek et al (1998); Schmitz et al (2002); Choi and Shin (2003); Bediaga et al (2009); Sims (2009) ;Nair et al (2010); van Dijk et al (2010); Tsai et al (2010); Kakinuma et al (2011); Ma et al (2013)). Often a metric or an index and a threshold are defined on the time series and chatter is detected if this threshold is exceeded.…”

Section: Introductionmentioning

confidence: 99%

Chatter Classification in Turning using Machine Learning and Topological Data Analysis

2018

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Chatter identification and detection in machining processes has been an active area of research in the past two decades. Part of the challenge in studying chatter is that machining equations that describe its occurrence are often nonlinear delay differential equations. The majority of the available tools for chatter identification rely on defining a metric that captures the characteristics of chatter, and a threshold that signals its occurrence. The difficulty in choosing these parameters can be somewhat alleviated by utilizing machine learning techniques. However, even with a successful classification algorithm, the transferability of typical machine learning methods from one data set to another remains very limited. In this paper we combine supervised machine learning with Topological Data Analysis (TDA) to obtain a descriptor of the process which can detect chatter. The features we use are derived from the persistence diagram of an attractor reconstructed from the time series via Takens embedding. We test the approach using deterministic and stochastic turning models, where the stochasticity is introduced via the cutting coefficient term. Our results show a 97% successful classification rate on the deterministic model labeled by the stability diagram obtained using the spectral element method. The features gleaned from the deterministic model are then utilized for characterization of chatter in a stochastic turning model where there are very limited analysis methods.

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

Cited by 31 publications

References 54 publications

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

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

MaxEnt feature-based reliability model method for real-time detection of early chatter in high-speed milling

Chatter Classification in Turning using Machine Learning and Topological Data Analysis

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