Recent progress of chatter prediction, detection and suppression in milling

Zhu, Lida; Liu, Changfu

doi:10.1016/j.ymssp.2020.106840

Cited by 239 publications

(63 citation statements)

References 345 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Surface roughness has an important influence on the service life, fatigue life and work efficiency of a machined workpiece and is influenced itself by the workpiece’s material properties, chatter, machining parameters, etc., [ 30 , 31 , 32 , 33 ]. The effects of dry micro cutting parameters on three-dimensional surface roughness parameters, including S a and S q , were investigated.…”

Section: Resultsmentioning

confidence: 99%

Experimental Investigation on Machinability of Aluminum Alloy during Dry Micro Cutting Process Using Helical Micro End Mills with Micro Textures

et al. 2020

View full text Add to dashboard Cite

Aluminum alloy material is widely used in the electronics, weapons, aviation and aerospace industries, due to its medium strength, good corrosion resistance, good toughness and excellent oxidation properties. With the trend of product miniaturization, micro cutting has become the mainstream technique for fabricating micro parts and components, so it is very meaningful and vital to work on removing the cutting fluid from the micro cutting process and make it totally sustainable and eco-friendly. In this work, an attempt has been made to fabricate micro textures onto the rear surface of helical micro end mills with diameters of less than 1 mm. Micro textures in the form of grooves were fabricated using a noncontact low speed wire electrical discharge turning technique. Dry micro cutting experiments were carried out on an aluminum alloy material using helical micro end mills with micro textures and the dry micro cutting surface quality and tool wear have been investigated. The influence of dry micro cutting parameters on the surface roughness parameters were also investigated. Experimental results showed that the Sa and Sq can be reduced to be about 1.56 μm and 2.08 μm, respectively. Contrasting results indicate that the implantation of micro textures does not deteriorate the dry micro cutting surface but improves the machined surface consistency of an aluminum alloy workpiece. The tool wear on helical micro end mills with micro textures involved in the dry micro cutting process of Al 6061 mainly include rear frictional wear, oxidation wear and diffusion wear.

show abstract

Section: Resultsmentioning

confidence: 99%

Experimental Investigation on Machinability of Aluminum Alloy during Dry Micro Cutting Process Using Helical Micro End Mills with Micro Textures

et al. 2020

View full text Add to dashboard Cite

show abstract

“…In the cutting of thin-walled parts, chatter occurs easily due to the weak rigidity of the workpiece, which will greatly affect the machined surface quality, reduce production efficiency [ 1 ], accelerate tool wear [ 2 ], and even affect the fatigue life of the workpiece [ 3 , 4 , 5 , 6 ]. Therefore, effective methods to predict, control, and monitor chatter have been a focus of research in the past 20 years.…”

Section: Introductionmentioning

confidence: 99%

A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts

Wang

Song

Wang

et al. 2021

Sensors

View full text Add to dashboard Cite

Data-driven chatter detection techniques avoid complex physical modeling and provide the basis for industrial applications of cutting process monitoring. Among them, feature extraction is the key step of chatter detection, which can compensate for the accuracy disadvantage of machine learning algorithms to some extent if the extracted features are highly correlated with the milling condition. However, the classification accuracy of the current feature extraction methods is not satisfactory, and a combination of multiple features is required to identify the chatter. This limits the development of unsupervised machine learning algorithms for chattering detection, which further affects the application in practical processing. In this paper, the fractal feature of the signal is extracted by structure function method (SFM) for the first time, which solves the problem that the features are easily affected by process parameters. Milling chatter is identified based on k-means algorithm, which avoids the complex process of training model, and the judgment method of milling chatter is also discussed. The proposed method can achieve 94.4% identification accuracy by using only one single signal feature, which is better than other feature extraction methods, and even better than some supervised machine learning algorithms. Moreover, experiments show that chatter will affect the distribution of cutting bending moment, and it is not reliable to monitor tool wear through the polar plot of the bending moment. This provides a theoretical basis for the application of unsupervised machine learning algorithms in chatter detection.

show abstract

“…The appearance of the chatter reduces productivity, accelerates wear of the cutting tool, and leads to poor machining quality. The early chatter should be detected as soon as possible to avoid the damage of the work-piece and save time in the chatter control loop [2].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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.

show abstract

Recent progress of chatter prediction, detection and suppression in milling

Cited by 239 publications

References 345 publications

Experimental Investigation on Machinability of Aluminum Alloy during Dry Micro Cutting Process Using Helical Micro End Mills with Micro Textures

Experimental Investigation on Machinability of Aluminum Alloy during Dry Micro Cutting Process Using Helical Micro End Mills with Micro Textures

A Novel Unsupervised Machine Learning-Based Method for Chatter Detection in the Milling of Thin-Walled Parts

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

Contact Info

Product

Resources

About