Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

Kosaraju, Satyanarayana; Anne, Venu Gopal; Popuri, Bangaru Babu

doi:10.1007/s00170-012-4621-2

Cited by 22 publications

(15 citation statements)

References 11 publications

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“…Specifically, when employing these technologies, the cutting tools are extracted from the manufacturing machine and placed on the measuring instruments to inspect the cutting tool conditions directly. Indirect monitoring technologies measure sensing signals corresponding to the insert condition during the machining process, including information, such as cutting force [ 5 , 6 , 7 , 8 , 9 ], vibration [ 10 , 11 , 12 ], acoustic emissions [ 13 , 14 , 15 ], temperature [ 16 ], and sound [ 17 ], and subsequently analyze the signals for tool condition classification. Indirect monitoring technologies have lower tool condition classification accuracy than do direct types; however, they are applicable for online tool condition inspection.…”

Section: Introductionmentioning

confidence: 99%

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

Huang

Yeh

2020

Sensors

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Insert conditions significantly influence the product quality and manufacturing efficiency of lathe machining. This study used the power spectral density distribution of the vibration signals of a lathe machining accelerometer to design an insert condition classification system applicable to different machining conditions. For four common lathe machining insert conditions (i.e., built-up edge, flank wear, normal, and fracture), herein, the insert condition classification system was established with two stages—insert condition modeling and machining model fusion. In the insert condition modeling stage, the magnitude features of the segmented frequencies were captured according to the power spectral density distributions of the accelerometer vibration signals. Principal component analysis and backpropagation neural networks were used to develop insert condition models for different machining conditions. In the machining model fusion stage, a backpropagation neural network was employed to establish the weight function between the machining conditions and insert condition models. Subsequently, the insert conditions were classified based on the calculated weight values of all the insert condition models. Cutting tests were performed on a computer numerical control (CNC) lathe and utilized to validate the feasibility of the designed insert condition classification system. The results of the cutting tests showed that the designed system could perform insert condition classification under different machining conditions, with a classification rate exceeding 80%. Using a triaxial accelerometer, the designed insert condition classification system could perform identification and classification online for four common insert conditions under different machining conditions, ensuring that CNC lathes could further improve manufacturing quality and efficiency in practice.

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Section: Introductionmentioning

confidence: 99%

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

Huang

Yeh

2020

Sensors

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“…In the literature, a number of research works have been developed with the aim to monitor the tool conditions based on relevant sensor signals acquired during the machining process. Kosaraju et al [ 12 ] presented a procedure based on the acquisition of acoustic emission (AE) signals for tool wear prediction in the turning of titanium alloy. In Reference [ 13 ], Jemielniak discussed the main challenges related to the employment of acoustic emission sensors in tool condition monitoring, which requires an effective pre-processing of the high-frequency signals in order to reduce background noise, e.g., through the use of appropriate filters in the pre-amplifiers.…”

Section: Introductionmentioning

confidence: 99%

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

Caggiano

2018

Sensors

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Machining of titanium alloys is characterised by extremely rapid tool wear due to the high cutting temperature and the strong adhesion at the tool-chip and tool-workpiece interface, caused by the low thermal conductivity and high chemical reactivity of Ti alloys. With the aim to monitor the tool conditions during dry turning of Ti-6Al-4V alloy, a machine learning procedure based on the acquisition and processing of cutting force, acoustic emission and vibration sensor signals during turning is implemented. A number of sensorial features are extracted from the acquired sensor signals in order to feed machine learning paradigms based on artificial neural networks. To reduce the large dimensionality of the sensorial features, an advanced feature extraction methodology based on Principal Component Analysis (PCA) is proposed. PCA allowed to identify a smaller number of features (k = 2 features), the principal component scores, obtained through linear projection of the original d features into a new space with reduced dimensionality k = 2, sufficient to describe the variance of the data. By feeding artificial neural networks with the PCA features, an accurate diagnosis of tool flank wear (VBmax) was achieved, with predicted values very close to the measured tool wear values.

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“…Another work has focused on estimation of tool’s flank wear using AE signals. 10 The authors have used the root mean square value of AE recorded at the chip-tool contact to detect the progression of flank wear in turning of a titanium alloy with coated carbide tool. Similarly, Bhaskaran et al 11 have used the skew and kurtosis parameters of the root mean square values of AE signal to estimate tool wear in a hard turning process.…”

Section: Introductionmentioning

confidence: 99%

Investigating the impact of tool inertia on machinability of a β-titanium alloy using tool deflection and acoustic emission

Iqbal

Biermann

Ali

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part B:

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Finding sustainable ways of machining exotic materials is gaining more and more importance in the manufacturing industry. Application of advanced measuring instruments for quantifying performance measures is a crucial requirement for making machining processes viable. The presented work aims to ameliorate machining of a high-strength β-titanium alloy using information from measurements of key responses, such as cutting energy consumption, tool deflection, and tool damage. Acoustic emission data and tool’s acceleration data are utilized to work out the magnitudes of energy consumed and deflection undergone by the tool, respectively. The article focuses on quantifying the effects of tool’s inertia, strength of work material, and two cutting parameters on the aforementioned responses. A total of 54 continuous cutting experiments are performed in which a fixed volume of material per experimental run is removed. Tool deflection method helped to determine the significant effects of varying tool inertia, work material strength, and cutting speed on the machining process. Likewise, acoustic emission method highlighted the strong effects of material strength and cutting speed caused on the cutting energy consumption. The effect of feed rate is found to be significant regarding tool wear only. Finally, the tool wear data are tested for correlation against the corresponding data sets of the other two responses. It is found that both tool deflection and cutting energy possess strong uphill relationships with tool wear.

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Online tool condition monitoring in turning titanium (grade 5) using acoustic emission: modeling

Cited by 22 publications

References 11 publications

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

Development of Insert Condition Classification System for CNC Lathes Using Power Spectral Density Distribution of Accelerometer Vibration Signals

Tool Wear Prediction in Ti-6Al-4V Machining through Multiple Sensor Monitoring and PCA Features Pattern Recognition

Investigating the impact of tool inertia on machinability of a β-titanium alloy using tool deflection and acoustic emission

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