Flank wear detection of cutting tool inserts in turning operation: application of nonlinear time series analysis

Rajesh, V.; Namboothiri, V. N. N.

doi:10.1007/s00500-009-0466-5

Cited by 14 publications

(6 citation statements)

References 15 publications

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“…Finally we consider the t4.8k data set from the Chameleon data sets 3 . Chameleon is a hierarchical clustering algorithm developped by Karypis et al [70].…”

Section: Clustering Chameleon Data Setsmentioning

confidence: 99%

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: Dynamic clustering for tracking evolving environments

Roa

Travé-Massuyès

Grisales

2019

Pattern Recognition

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Evolving environments challenge researchers with non stationary data flows where the concepts-or states-being tracked can change over time. This requires tracking algorithms suited to represent concept evolution and in some cases, e.g. real industrial environments, also suited to represent time dependent features. This paper proposes a unified approach to track evolving environments that uses a two-stages distance-based and density-based clustering algorithm. In this approach data samples are fed as input to the distance based clustering stage in an incremental, online fashion, and they are then clustered to form µ-clusters. The density-based algorithm analyses the micro-clusters to provide the final clusters: thanks to a forgetting process, clusters may emerge, drift, merge, split or disappear, hence following the evolution of the environment. This algorithm has proved to be able to detect high overlapping clusters even in multi-density distributions, making no assumption about cluster convexity. It shows fast response to data streams and good outlier rejection properties.

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“…Finally we consider the t4.8k data set from the Chameleon data sets 3 . Chameleon is a hierarchical clustering algorithm developped by Karypis et al [70].…”

Section: Clustering Chameleon Data Setsmentioning

confidence: 99%

“…[2]. These environments cause component health state to change and the process behavior to evolve, as has been widely reported in the cases of tool wear [3,4] and pipe clogging or leaking [5,6]. Even though aging acts on the process components, "normal" conditions may be maintained regardless of some evolution.…”

Section: Introductionmentioning

confidence: 99%

: Dynamic clustering for tracking evolving environments

Roa

Travé-Massuyès

Grisales

2019

Pattern Recognition

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“…Teti et al [17] also described various vibration sensing techniques [18,19] to monitor flank wear in their full comprehensive survey conducted on machine monitoring techniques. Rajesh and Narayanan Namboothiri [20] conducted nonlinear time series analysis by examining the vibration signals which are occurred all through the cutting process. Ding and He [21] also researched on tool condition monitoring information attained from cutting tool vibration signals, Acoustic Emission signals, servo motor current signals, spindle and microscopy technique, based on which they developed a model among tool wear states and reliability.…”

Section: Vibration Signaturementioning

confidence: 99%

Comparision of Dominant Features Identification for Tool Wear in Hard Turning of Inconel 718 by Using Vibration Analysis

Rao

Srinivas²

2019

Strojnícky Časopis - Journal of Mechanical Engineering

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In various machining processes, the vibration signals are studied for tool condition monitoring often referred as wear monitoring. It is essential to overcome unpredicted machining trouble and to improvise the efficiency of the machine. Tool wear is a vital problem in materials such as nickel based alloys as they have high hardness ranges. Though they have high hardness, a nickel based alloy Inconel 718 with varying HRC (51, 53, and 55), is opted as work material for hard turning process in this work. Uncoated carbide, coated carbide and ceramic tools are employed as cutting tools. Taguchi’s L9 orthogonal array is considered by taking hardness, speed, feed and depth of cut as four input parameters, the number of experiments and the combinations of parameters for every run is obtained. The vibration signals are recorded at various stages of cutting, till the tool failure is observed. Taking this vibration signal data as input to ANOVA and Grey relation analysis (GRA) which categorizes the optimal and utmost dominant features such as Root Mean Square (RMS), Crest Factor (CF), Skewness (Sk), Kurtosis (Ku), Absolute Deviation (AD), Mean, Standard Deviation (SD), Variance, peak, Frequency and Time in the tool wear process.

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“…Their experiments on cutting forces demonstrated that the predicted results closely matched the measured data. Rajesh and Narayanan [13] monitored vibration signals produced during the cutting process and conducted nonlinear time series analysis. Their study revealed that both phase space reconstruction and correlation dimension calculations of the vibration signals increased with tool wear.…”

Section: Introductionmentioning

confidence: 99%

Semi-supervised Prediction of Milling Cutter Wear Based on an Empirical Formula for Cutting Force and Wear

Yu,

Zhan,

et al. 2024

Preprint

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Accurately predicting tool wear is essential for maintaining high machining quality. Currently, deep learning models are extensively utilized in predicting tool wear. However, deep learning models are prone to local optima, and limited tool wear sample data and multi-sensor feature fusion limit the model's ability to generalize and extract valid information. To address the above problems, this paper proposes a semi-supervised milling cutter wear prediction method based on an empirical formula for cutting force wear, which is modelled (IRM-CFAM) by the Inception-ResNet Module (IRM) and the Channel Feature Adaptive Module (CFAM). By introducing an empirical formula for cutting force wear, the cutting forces in the unlabeled samples are input to estimate wear values and construct wear curves. Based on the constructed wear curves, a monotonic loss function guided by an empirical curve of milling cutter wear is proposed. Meanwhile, the estimated wear values are combined with unlabeled samples to form a second data source, which is then merged with the labeled samples to expand the dataset. The constructed CFAM consists of channel attention and cross-attention mechanisms, which achieves adaptive fusion of weights. Following validation on the PHM2010 milling cutter dataset, the proposed method attained average RMSE and MAE values of 6.693 and 5.136, respectively, across three test sets, showing a significant advantage over other methods. This research facilitates accurate prediction of milling cutter wear and introduces a novel approach for expanding sample data.

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Flank wear detection of cutting tool inserts in turning operation: application of nonlinear time series analysis

Cited by 14 publications

References 15 publications

: Dynamic clustering for tracking evolving environments

: Dynamic clustering for tracking evolving environments

Comparision of Dominant Features Identification for Tool Wear in Hard Turning of Inconel 718 by Using Vibration Analysis

Semi-supervised Prediction of Milling Cutter Wear Based on an Empirical Formula for Cutting Force and Wear

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