Bifurcation and chaotic vibration of frictional chatter in turning process

Wang, An; Jin, Wuyin; Chen, Wenke; Feng, Ruoqiang; Xu, Chunjing

doi:10.1177/1687814018771262

Cited by 8 publications

(3 citation statements)

References 20 publications

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“…However, the computational tasks involved in performing the weighted sum or multiplication and accumulation functions are technically intensive. Therefore, efforts are made to achieve low power consumption in these computational tasks [33]. Two MCDM (Multiple Criteria Decision Making) methods were employed: the weighted sum method, chosen for its simplicity despite containing inaccurate and vague information, and the fuzzy logic method, used for performance assessment.…”

Section: Wight Sum Methodsmentioning

confidence: 99%

Assessment of Turning process using the WSM Method

Arivudainambi¹,

Muthusamy²,

Suresh³

2022

JEMM

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Metal turning is a form of machining. It is used to create circular areas by cutting objects. The turning process requires a machine or lathe, a working tool, a fixture, and a cutting tool. A preformed piece of metal is secured to a device. A common process for turning a part includes rotating it while moving a single-point cutting tool parallel to the axis of rotation. This cutting tool works on both the outer and inner surfaces of the part. Factors such as cutting speed, cutting tool, depth of cut, and work-piece are considered in the turning process. In the assessment, the options are Cu, Si, Mn, Mg, and Zn. The results show that Zn ranks first, while Si has the lowest rank. The dataset's value for the Range of Turning process in the WSM (Weighted Sum Model) Method indicates that zinc (Zn) achieves the top ranking.

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Section: Wight Sum Methodsmentioning

confidence: 99%

Assessment of Turning process using the WSM Method

Arivudainambi¹,

Muthusamy²,

Suresh³

2022

JEMM

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“…Hence, it is meaningful to study the tool wear effect on chatter occurrence and detection. Tool wear has been studied and modelled for frictional chatter, and some researchers reported how this issue affects the dynamics greatly in metal cutting [380][381][382][383], while the coexistence of regenerative and frictional chatter has been studied and recently modelled by Yan et al [384][385][386] and Weremczuk et al [387], and the no co-occurrence of mode coupling and regenerative chatter is reported by Celikag et al [388]. Similarly, Matsumoto et al [389] and Stavropoulos et al [390] concluded that chatter reduces TCM accuracy.…”

Section: Multi-condition Monitoring and Identificationmentioning

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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“…In frictional chatter, friction interaction between cutter and workpiece surfaces leads to a sudden instantaneous separation of the cutting edges from the workpiece, which in turn produces a variation in their relative speed, thus altering the chip thickness. Under certain conditions, these effects can lead to chaotic movements and vibrations [22,23].…”

Section: Basic Principles Of Chatter Dynamicsmentioning

confidence: 99%

In-Process Chatter Detection Using Signal Analysis in Frequency and Time-Frequency Domain

et al. 2021

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All machining processes involve vibrations generated by structural sources such as a machine’s moving parts or by the interaction between cutting tools and work-pieces. Relative vibrations between the work-pieces and the cutting tool are the most relevant from the point of view of the regenerative chatter phenomenon. In fact, these vibrations can lead to a chip yregeneration effect, which results in unwanted consequences, rapidly degenerating towards a very poor quality of surface finishing or, in case of severe chatter conditions, to machine-tool or work-piece damage. In the past decades, two different approaches for chatter avoidance were proposed by the scientific community, and they are commonly referred to as Out-of-Process (OuP) and in-Process (iP) solutions. The OuP solutions are off-line approaches, which allow to properly set the working parameters before machining starts. Ip solutions are on-line techniques, which allow to dynamically change the working parameters during machining by using single or multiple sensors. By monitoring the machining process, iP algorithms try to keep the machining process in stable working conditions while keeping high productivity levels. This study dealt with a novel iP chatter-detection strategy based on the Power Spectral Density (PSD) analysis and on the Wavelet Packet Decomposition (WPD) of different sensor signals. The preliminary results demonstrate the stability and feasibility of proposed indicators for chatter detection in industrial application.

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Bifurcation and chaotic vibration of frictional chatter in turning process

Cited by 8 publications

References 20 publications

Assessment of Turning process using the WSM Method

Assessment of Turning process using the WSM Method

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

In-Process Chatter Detection Using Signal Analysis in Frequency and Time-Frequency Domain

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