Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images

Bhat, Nagaraj; Dutta, Samik; Pal, Surjya K.; Pal, Srikanta

doi:10.1016/j.measurement.2016.05.022

Cited by 51 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Bhat et al . (2016) developed a hidden Markov model-based tool condition monitoring methodology ensuring the economical usages of cutting tools. Liao et al .…”

Section: Case Studymentioning

confidence: 99%

“…(c) Simulated surface profile. (d) Return map of (c).Artificial Intelligence for Engineering Design,Analysis and Manufacturing et al (2016) described the hidden Markov model-based methodology for recognizing the machining states ensuring safe operations Bhat et al (2016). developed a hidden Markov model-based tool condition monitoring methodology ensuring the economical usages of cutting tools Liao et al (2006).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Hidden Markov model-based digital twin construction for futuristic manufacturing systems

Ghosh

Ullah

Kubo

2019

AIEDAM

View full text Add to dashboard Cite

This paper addresses the construction of digital twins (exact mirror images of real-world in cyberspace) using hidden Markov models for the futuristic manufacturing systems known as Industry 4.0. The proposed digital twin consists of two components namely model component and simulation component. The model component forms a Markov chain that encapsulates the dynamics underlying the phenomenon by using some discrete states and their transition probabilities. The simulation component recreates the phenomenon using a Monte Carlo simulation process. The efficacy of the proposed digital twin construction methodology is shown by a case study, where the digital twin of the surface roughness of a surface created by successive grinding operations is described. The developers of the cyber-physical systems will be benefitted from the outcomes of this study because these systems need the computable virtual abstractions of the manufacturing phenomena to address the issues related to the maturity index of futuristic manufacturing systems (i.e., understand, predict, decide, and adopt).

show abstract

“…Bhat et al . (2016) developed a hidden Markov model-based tool condition monitoring methodology ensuring the economical usages of cutting tools. Liao et al .…”

Section: Case Studymentioning

confidence: 99%

mentioning

confidence: 99%

Hidden Markov model-based digital twin construction for futuristic manufacturing systems

Ghosh

Ullah

Kubo

2019

AIEDAM

View full text Add to dashboard Cite

show abstract

“…Haralick texture features, computed from the GLCM, are widely used due to their simplicity and intuitive interpretations, and have successfully been applied in e.g . in the analysis of skin texture [5], in land-use and forest-type classification [6], in automatic pollen detection [7], in fabric defect detection [8], in plant leaf classification [9], in cutting tool condition monitoring [10], and electrophoresis analysis [11]. In recent years there has been a rapid increase in the application of Haralick features in medical image analysis, e.g .…”

Section: Introductionmentioning

confidence: 99%

Gray-level invariant Haralick texture features

et al. 2019

View full text Add to dashboard Cite

Haralick texture features are common texture descriptors in image analysis. To compute the Haralick features, the image gray-levels are reduced, a process called quantization. The resulting features depend heavily on the quantization step, so Haralick features are not reproducible unless the same quantization is performed. The aim of this work was to develop Haralick features that are invariant to the number of quantization gray-levels. By redefining the gray-level co-occurrence matrix (GLCM) as a discretized probability density function, it becomes asymptotically invariant to the quantization. The invariant and original features were compared using logistic regression classification to separate two classes based on the texture features. Classifiers trained on the invariant features showed higher accuracies, and had similar performance when training and test images had very different quantizations. In conclusion, using the invariant Haralick features, an image pattern will give the same texture feature values independent of image quantization.

show abstract

“…Although condition monitoring has been extensively studied, some limitations persist concerning the performance of monitoring systems under realistic machining conditions [ 10 ]. Condition monitoring systems have been proposed using many different approaches, some with different success rates and limitations, such as neural networks [ 3 , 7 , 11 , 12 , 13 , 14 ], fuzzy logic [ 15 , 16 ], Markov chains [ 17 , 18 , 19 ], expert systems [ 20 ] and many others [ 10 ]. Neural networks present some of the most attractive features, such as the capability of abstraction of hardly accessible knowledge and generalization from distorted sensor signals when applied to sensor fusion and classification in tool wear monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks

Silva

Araújo

2020

Sensors

View full text Add to dashboard Cite

Condition monitoring is a fundamental part of machining, as well as other manufacturing processes where, generally, there are parts that wear out and have to be replaced. Devising proper condition monitoring has been a concern of many researchers, but there is still a lack of robustness and efficiency, most often hindered by the system’s complexity or otherwise limited by the inherent noisy signals, a characteristic of industrial processes. The vast majority of condition monitoring approaches do not take into account the temporal sequence when modelling and hence lose an intrinsic part of the context of an actual time-dependent process, fundamental to processes such as cutting. The proposed system uses a multisensory approach to gather information from the cutting process, which is then modelled by a recurrent neural network, capturing the evolutive pattern of wear over time. The system was tested with realistic cutting conditions, and the results show great effectiveness and accuracy with just a few cutting tests. The use of recurrent neural networks demonstrates the potential of such an approach for other time-dependent industrial processes under noisy conditions.

show abstract

Tool condition classification in turning process using hidden Markov model based on texture analysis of machined surface images

Cited by 51 publications

References 19 publications

Hidden Markov model-based digital twin construction for futuristic manufacturing systems

Hidden Markov model-based digital twin construction for futuristic manufacturing systems

Gray-level invariant Haralick texture features

A Novel Approach to Condition Monitoring of the Cutting Process Using Recurrent Neural Networks

Contact Info

Product

Resources

About