Hybrid model- and signal-based chatter detection in the milling process

Liu, Meng Kun; Tran, Minh-Quang; Chung, Chunhui; Qui, Yi Wen

doi:10.1007/s12206-019-1201-5

Cited by 22 publications

(3 citation statements)

References 22 publications

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“…Hence, the use of hybrid chatter detection approaches and novel AI algorithms may lead to promising solutions. For instance, Hanachi et al [410] fused data-driven and physics-based results to enhance tool wear prognosis and RUL estimation in milling, while Rahimi et al [149] and Liu et al [140] have proposed hybrid chatter detection systems which combine data-driven and physics-based methods. Oleaga et al [411] employed three different machine learning techniques to predict chatter frequency and critical depth in milling, and Postel et al [412] used DL with ensemble transfer learning to improve the prediction of the SLD using experimental data.…”

Section: Hybrid Approaches and Parameters Optimisationmentioning

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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Section: Hybrid Approaches and Parameters Optimisationmentioning

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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“…Many monitoring and diagnostic systems for the cutting process have been developed to deal with this problem, in which the cutting signals have been used for chatter identification, but most of them are offline systems [10][11][12]. Traditionally, the chatter monitoring process consists of data collection, processing of cutting signals, and applying statistical methods and model training for chatter detection [13,14]. Recently, machine learning approaches have been recognized to be an effective method for classification and recognition problems [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Reliable Deep Learning and IoT-Based Monitoring System for Secure Computer Numerical Control Machines Against Cyber-Attacks With Experimental Verification

et al. 2022

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“…In milling processes, the size of the cutting force is related to the previous cutting behavior. By extracting the cutting force, Liu et al [22] exploited a method of a dynamic cutting force model, in which the cutting force is only generated when there is contact between the tool and the workpiece. It forms an intermittent input with a time delay term in the cutting dynamics equation, which is a nonlinear system with progressive chaos characteristics (route-to-chaos).…”

Section: Introductionmentioning

confidence: 99%

Effective Optimization Based on Equilibrium Optimizer for Dynamic Cutting Force Coefficients of the End-Milling Process

Tran

Elsisi

et al. 2022

Mathematics

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This study aims to develop an accurate dynamic cutting force model in the milling process. In the proposed model, the estimated cutting force tackles the effect of the self-excited vibration that causes machining instability during the cutting process. In particular, the square root of the residual cutting force between the prediction and the actual cutting force is considered as an objective function for optimizing the cutting force coefficients using the equilibrium optimizer (EO) approach instead of the trial-and-error approach. The results confirm that the proposed model can provide higher prediction accuracy when the EO is applied. In addition, the proposed EO has a minimum integral square error (ISE) of around 1.12, while the genetic algorithm (GA) has an ISE of around 1.14 and the trial-and-error method has an ISE of around 2.4. Moreover, the proposed method can help to investigate the cutting stability and to suspend the chatter phenomenon by selecting an optimal set of cutting parameters.

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Hybrid model- and signal-based chatter detection in the milling process

Cited by 22 publications

References 22 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

Reliable Deep Learning and IoT-Based Monitoring System for Secure Computer Numerical Control Machines Against Cyber-Attacks With Experimental Verification

Effective Optimization Based on Equilibrium Optimizer for Dynamic Cutting Force Coefficients of the End-Milling Process

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