Chip segmentation frequency based strategy for tool condition monitoring during turning of Ti-6Al-4V

González, Germán; Schwär, Daniel; Segebade, Eric; Heizmann, Michael; Zanger, Frederik

doi:10.1016/j.procir.2021.09.047

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…The chip segmentation frequency is the frequency over time, at which the individual segments that build up one chip, separate from the workpiece [10]. This frequency can be measured by using sensors for acoustic emission [13].…”

Section: Methodsmentioning

confidence: 99%

“…However, the geometry of the chips after the turning process is much more complex, due to the multidimensional angle of curvature. Signal processing of acoustic emission can be used to capture the chip segmentation frequency as strategy for process and tool condition monitoring, due to its high sensitivity and diverse information contained in the broad MHz frequency band [10].…”

Section: Introductionmentioning

confidence: 99%

“…S − t m+1 T n m+1 < 0,(10) and‖S − t m ‖ ≤ r max,m (11) S � m = R m S m − t m + d m . (12)R m = e m,1 , e m,2 , n m T , (13) e m,2 = ẽm,2 ‖ẽ m,2 ‖ with ẽm,2 = n m × e 1,m−1 (14) and e m,1 = e m,2 × n m , (15)…”

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confidence: 99%

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Enhanced chip analysis with computed tomography for estimation of chip segmentation frequency

Ocampo

Koch

González

et al. 2023

Prod. Eng. Res. Devel.

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This work presents an enhancement of a method for analyzing metallic chips produced during turning processes in order to estimate the chip segmentation frequency. Therefore, the resulting 3D image of a computer tomography scan of the chip can be transformed virtually into a straight chip by describing the course of the chip in three-dimensional space by a trajectory. Along this trajectory, the chip point cloud is sliced in thin layers which can be transformed to a straight chip. To estimate the segment rate, the height course describing the segmentation of the chip is extracted on the serrated side of the point cloud by means of nearest neighbor interpolation. The resulting signals are compared with the measurements of the chip surface using an optical microscope, whereby only two straight chips could be analyzed. The comparison shows a nearly perfect match between the two signals and is confirmed by a Pearson correlation coefficient of over $${86}\%$$ 86 % for both signals. Furthermore, a statistical comparison with the acoustic emissions of the turning process reveals satisfying similarities for the chip segmentation frequency with respect to the compression factor.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced chip analysis with computed tomography for estimation of chip segmentation frequency

Ocampo

Koch

González

et al. 2023

Prod. Eng. Res. Devel.

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“…Therefore, the focus of research activities was mainly on indirect approaches of tool and machining condition monitoring, which benefit from the fact that a variation in cutting tool condition changes certain variables such as cutting forces, vibration, and surface finish. In these methods, the machine data (such as the current, power, and so on) from the machine elements (such as spindle or axis motors) [5][6][7] or signals from the sensors integrated into the machine tool (such as piezosensor, accelerometer, strain gauge, thermocouple, acoustic emission sensor, and so on) [8][9][10][11][12][13][14][15][16] are analyzed to recognize the possible correlations with the cutting tool and machining condition. Olma et al [17] presented an efficient method for monitoring highspeed broaching for Inconel 718 using process vibrations recorded by accelerometers.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Machining Condition Using Time Series Imaging and Deep Learning in Slot Milling of Titanium Alloy

Hojati

Azarhoushang

Daneshi

et al. 2022

JMMP

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Low surface quality, undesired geometrical and dimensional tolerances, and product damage due to tool wear and tool breakage lead to a dramatic increase in production cost. In this regard, monitoring tool conditions and the machining process are crucial to prevent unwanted events during the process and guarantee cost-effective and high-quality production. This study aims to predict critical machining conditions concerning surface roughness and tool breakage in slot milling of titanium alloy. Using the Siemens SINUMERIK Edge Box integrated into a CNC machine tool, signals were recorded from main spindle and different axes. Instead of extraction of features from signals, the Gramian angular field (GAF) was used to encode the whole signal into an image with no loss of information. Afterwards, the images obtained from different machining conditions were used for training a convolutional neural network (CNN) as a suitable and frequently applied deep learning method for images. The combination of GAF and trained CNN model indicates good performance in predicting critical machining conditions, particularly in the case of an imbalanced dataset. The trained classification CNN model resulted in recall, precision, and accuracy with 75%, 88%, and 94% values, respectively, for the prediction of workpiece surface quality and tool breakage.

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Three-dimensional characterization of abrasive chips using micro-computed tomography

Fang,

Fell,

Frank

et al. 2024

Int J Adv Manuf Technol

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Chip formation is considered one important indicator to evaluate machining processes. In particular, geometric features of chips may provide important insights for the assessment of machining stability and productivity. In fixed-abrasive machining processes, such as grinding and honing, chips are simultaneously produced by many of the geometrically undefined cutting edges. Despite being “undefined,” geometric features of abrasive grains can still be statistically characterized or described. Accordingly, it can be assumed that, under stable machining conditions, the geometric features of abrasive chips may also conform to certain statistical patterns. However, statistical characterization of abrasive chips can be very challenging due to their large quantity, irregular shapes, minuscule size, and sometimes tangled condition. In this study, an analysis method combining metallographic preparation and micro-computed tomography (micro-CT) has been developed to characterize abrasive chips produced by a honing stone. The results regarding the geometric characteristics of the obtained massive abrasive chips, i.e., their sizes and shapes, were presented and statistically described. It was found most chips had a size around 50 µm and the shape being a slightly elongated and curved cone. Most of the geometric features could meet a positive skewness distribution.

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Chip segmentation frequency based strategy for tool condition monitoring during turning of Ti-6Al-4V

Cited by 5 publications

References 6 publications

Enhanced chip analysis with computed tomography for estimation of chip segmentation frequency

Enhanced chip analysis with computed tomography for estimation of chip segmentation frequency

Prediction of Machining Condition Using Time Series Imaging and Deep Learning in Slot Milling of Titanium Alloy

Three-dimensional characterization of abrasive chips using micro-computed tomography

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