Milling chatter monitoring under variable cutting conditions based on time series features

Chen, Kunhong; Zhang, Xing; Zhao, Zhao; Yin, Jie; Zhao, Wanhua

doi:10.1007/s00170-021-06746-8

Cited by 10 publications

(4 citation statements)

References 44 publications

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“…While most studies aim to extract statistical or other conventional features that represent the signal characteristics in the time domain, as detailed in Section 4.1, some have proposed various dimensionless chatter indicators based on statistical parameters, as done, for instance, in [5,74,182]. Others have treated the signals as time series and utilized different mathematical approaches for data analysis, as presented in [144,147,170]. All methodologies aim to identify the chatter onset as early as possible by extracting diverse chatter features.…”

Section: Time Domain Analysismentioning

confidence: 99%

“…As to feature dimension reduction, principal component analysis (PCA) is a commonly utilized technique, as shown in [175,186,196]. Fu et al [132], Chen et al [144] and Dun et al [185] employed it as a reference to show the advantages of their methods. Jo et al [253] suggested that the use of the modified independent component analysis (MICA) method outperforms PCA, while Liu et al [175] illustrated the contribution of PCA with different signal processing and classification methods.…”

Section: Feature Selectionmentioning

confidence: 99%

See 1 more Smart Citation

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: Time Domain Analysismentioning

confidence: 99%

Section: Feature Selectionmentioning

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

“…The approach incorporates signal processing in a time-frequency domain called Wavelet Transform Modulus Maxima (WTMM). Chen et al [5] proposed a method for monitoring chatter under different cutting conditions in milling. It incorporates time series analysis (using Recurrence Quantitative Analysis or RQA) of cutting force signals acquired at a sampling frequency of 8 kHz.…”

Section: Literature Reviewmentioning

confidence: 99%

“…When a machining operation continues in a given smart manufacturing environment, as seen in Figure 1, sensors collect signals. The signals are generally processed in the time [5], frequency [6][7][8], and time-frequency [5,6,9] domains to extract the underlying features. In some cases, alternative processing methods, such as fractal-based [10], Approximate Entropy (ApEn)-based, and Sampling Entropy (SampEn)-based [11] methods, are used.…”

Section: Introductionmentioning

confidence: 99%

Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing

Ura

Ghosh

2021

Sensors

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Smart manufacturing employs embedded systems such as CNC machine tools, programable logic controllers, automated guided vehicles, robots, digital measuring instruments, cyber-physical systems, and digital twins. These systems collectively perform high-level cognitive tasks (monitoring, understanding, deciding, and adapting) by making sense of sensor signals. When sensor signals are exchanged through the abovementioned embedded systems, a phenomenon called time latency or delay occurs. As a result, the signal at its origin (e.g., machine tools) and signal received at the receiver end (e.g., digital twin) differ. The time and frequency domain-based conventional signal processing cannot adequately address the delay-centric issues. Instead, these issues can be addressed by the delay domain, as suggested in the literature. Based on this consideration, this study first processes arbitrary signals in time, frequency, and delay domains and elucidates the significance of delay domain over time and frequency domains. Afterward, real-life signals collected while machining different materials are analyzed using frequency and delay domains to reconfirm its (the delay domain’s) significance in real-life settings. In both cases, it is found that the delay domain is more informative and reliable than the time and frequency domains when the delay is unavoidable. Moreover, the delay domain can act as a signature of a machining situation, distinguishing it (the situation) from others. Therefore, computational arrangements enabling delay domain-based signal processing must be enacted to effectively functionalize the smart manufacturing-centric embedded systems.

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Online chatter detection in robotic machining based on adaptive variational mode decomposition

Chen

Zhang

et al. 2021

Int J Adv Manuf Technol

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Chatter is the main problem that limits the application of industrial robots in the field of machining process. It is critical important to establish an adaptive chatter detection solution for robot machining process and realize the online detection of chatter. However, different from machine tool chatter, the chatter in robotic machining process is more complex to be detected due to the variable stiffness characteristics and weaker stiffness of normal industrial robot, and the existing literature has less research on this problem. This paper presents a comprehensive solution for online chatter detection in robotic machining process. Firstly, in order to detect the chatter in robotic machining process and avoid mode mixing problem in variational mode decomposition (VMD) process, an adaptive variational mode decomposition (AVMD) method based on kurtosis and instantaneous frequency is proposed, which realizes the adaptive selection of the decomposition parameter. Secondly, optimal decomposition parameters are calculated by using genetic algorithm. By optimizing the discrete step length of decomposition parameter, it greatly reduces the optimization time. Last but not least, approximate entropy, energy entropy and proposed entropy drift coefficient are extracted to distinguish chatter and stable machining state. Simulation and experimental results show that the proposed method can meet the real-time requirements of online detection and detect the occurrence of chatter effectively.

show abstract

Milling chatter monitoring under variable cutting conditions based on time series features

Cited by 10 publications

References 44 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

Time Latency-Centric Signal Processing: A Perspective of Smart Manufacturing

Online chatter detection in robotic machining based on adaptive variational mode decomposition

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