Cutting signals analysis in milling titanium alloy thin-part components and non-thin-wall components

Huang, Panling; Li, J. F.; Sun, Jie; Jia, Xiujie

doi:10.1007/s00170-015-7837-0

Cited by 26 publications

(8 citation statements)

References 18 publications

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“…Similarly, Srinivasan et al [216], Tansel et al [204] and Lamraoui et al [95] reported that it is possible by using a single monitoring system to acquire signals from indirect sensors for simultaneous chatter detection and tool wear diagnosis, but the correlation between the two phenomena has not been widely revealed. In addition, diverse studies [264,[397][398][399][400][401][402] provided experimental results of the tool wear effect on the signal waveform and the energy distribution of the frequency bands, during the machining of different materials, as shown in Fig. 18.…”

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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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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“…To further evaluate the de-noising effect of noise reduction, we conducted the frequency spectrum analysis to signals. The energy of vibration signal should be distributed at the integral multiples of the tooth passing frequency (TPF) [35].…”

Section: ) Vibrationmentioning

confidence: 99%

Singularity Analysis of Cutting Force and Vibration for Tool Condition Monitoring in Milling

et al. 2019

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Tool wear is inevitable in manufacturing and affects the surface quality and geometric tolerance significantly. A robust and efficient tool condition monitoring (TCM) system is needed to maximize tool life, ensure work-piece quality, and benefit the cost control of manufacturers. This paper presents a systematic singularity analysis approach of cutting force and vibrations for feature extraction of TCM in milling. The singularity of sensory signals is estimated by Holder Exponents (HE), which are determined by wavelet transform modulus maxima (WTMM). A comprehensive wavelet basis selection approach is proposed to choose the appropriate wavelet basis for different sensory signals. A de-noising algorithm based on WTMMs' estimation was used as a pre-processing technique to improve noise reduction and preserve the singularities. The mutual information method was employed to rank HE features. The effectiveness of the singularity analysis approach is validated through the Support Vector Machine (SVM) models trained by these ranked features. The estimating results of case studies confirm the efficacy and efficiency of the proposed approach.

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“…Zhou et al [10] studied the nonlinear dynamic behavior of cutting force during the machining process of thin plate parts, and the results indicate that the cutting force can develop complicated dynamic behavior due to the time-delay effect. Huang et al [11] used spectrum analysis and wavelet analysis to analyze and compare the cutting force and spindle acceleration signal between thin-walled and non-thin-walled titanium alloy parts to determine the influence of tool wear on cutting parts. Liu et al [12] proposed a finite-element-based milling error prediction method.…”

Section: Optimization Of Cutting Force and Error Compensationmentioning

confidence: 99%

A non-uniform allowance allocation method based on interim state stiffness of machining features for NC programming of structural parts

Jiang

Liu

2018

Vis. Comput. Ind. Biomed. Art

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For thin-walled parts, uniform allowance to each machining surface is allocated by the traditional machining method. Considering the sequence of the adjacent machining features, it may cause poor stiffness for some side walls due to a minor wall thickness, which may cause the deformation of the final formed parts to be large, or deduce machining efficiency for some machining features due to too thick remains. In order to address this issue, a non-uniform allowance allocation method based on interim state stiffness of machining features for the finishing of thin-walled structural parts is proposed in this paper. In this method, the interim state model of machining features is constructed according to the machining sequence of the parts, and the stiffness of the side wall is taken as the evaluation index to allocate reasonable allowance value to the corresponding machining surface to ensure the stiffness requirement of the parts in the machining process. According to the finite element simulation results, the non-uniform allowance allocation method proposed in this paper can effectively improve the stiffness of the parts and reduce the deformation of the parts, when compared with the traditional uniform allowance machining method.

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Cutting signals analysis in milling titanium alloy thin-part components and non-thin-wall components

Cited by 26 publications

References 18 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

Singularity Analysis of Cutting Force and Vibration for Tool Condition Monitoring in Milling

A non-uniform allowance allocation method based on interim state stiffness of machining features for NC programming of structural parts

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