Chatter Classification in Turning using Machine Learning and Topological Data Analysis

Khasawneh, Firas A.; Munch, Elizabeth; Perea, José A.

doi:10.1016/j.ifacol.2018.07.222

Cited by 48 publications

(33 citation statements)

References 36 publications

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“…PH is helpful for characterizing the "shape" of data, and the myriad applications of it include studies of protein structure [24][25][26][27], DNA structure [28], neuronal morphologies [29], computer vision [30], diurnal cycles in hurricanes [31], chaotic dynamics in differential equations [32], spatial percolation problems [33], and many others. Additionally, combining machine-learning approaches with PH has also been very useful for several classification problems [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Spatial applications of topological data analysis: Cities, snowflakes, random structures, and spiders spinning under the influence

Feng

Porter

2020

Phys. Rev. Research

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Spatial networks are ubiquitous in social, geographical, physical, and biological applications. To understand the large-scale structure of networks, it is important to develop methods that allow one to directly probe the effects of space on structure and dynamics. Historically, algebraic topology has provided one framework for rigorously and quantitatively describing the global structure of a space, and recent advances in topological data analysis have given scholars a new lens for analyzing network data. In this paper, we study a variety of spatial networks-including both synthetic and natural ones-using topological methods that we developed recently for analyzing spatial systems. We demonstrate that our methods are able to capture meaningful quantities, with specifics that depend on context, in spatial networks and thereby provide useful insights into the structure of those networks. We illustrate these ideas with examples of synthetic networks and dynamics on them, street networks in cities, snowflakes, and webs that were spun by spiders under the influence of various psychotropic substances.

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Section: Introductionmentioning

confidence: 99%

Spatial applications of topological data analysis: Cities, snowflakes, random structures, and spiders spinning under the influence

Feng

Porter

2020

Phys. Rev. Research

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“…Moreover Filipic et al [21] used the same method to classify dielectric fluids in electrical discharge machining and for tool selection in an industrial grinding process, showing that the approach is beneficial in preventing poor process performance and improving product quality. Cherukuri [22] applied an artificial neural network (NN) to model stability in turning operations using analytical stability study to generate a dataset that trains the NN. Additionally, Khasawneh [23] had combined supervised machine learning with topological data analysis to obtain a descriptor of the process which can detect chatter in turning.…”

Section: Parameters Selection Based On Aimentioning

confidence: 99%

Roundness prediction in centreless grinding using physics-enhanced machine learning techniques

Safarzadeh

Leonesio

Bianchi

et al. 2020

Int J Adv Manuf Technol

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This work proposes a model for suggesting optimal process configuration in plunge centreless grinding operations. Seven different approaches were implemented and compared: first principles model, neural network model with one hidden layer, support vector regression model with polynomial kernel function, Gaussian process regression model and hybrid versions of those three models. The first approach is based on an enhancement of the well-known numerical process simulation of geometrical instability. The model takes into account raw workpiece profile and possible wheel-workpiece loss of contact, which introduces an inherent limitation on the resulting profile waviness. Physical models, because of epistemic errors due to neglected or oversimplified functional relationships, can be too approximated for being considered in industrial applications. Moreover, in deterministic models, uncertainties affecting the various parameters are not explicitly considered. Complexity in centreless grinding models arises from phenomena like contact length dependency on local compliance, contact force and grinding wheel roughness, unpredicted material properties of the grinding wheel and workpiece, precision of the manual setup done by the operator, wheel wear and nature of wheel wear. In order to improve the overall model prediction accuracy and allow automated continuous learning, several machine learning techniques have been investigated: a Bayesian regularized neural network, an SVR model and a GPR model. To exploit the a priori knowledge embedded in physical models, hybrid models are proposed, where neural network, SVR and GPR models are fed by the nominal process parameters enriched with the roundness predicted by the first principle model. Those hybrid models result in an improved prediction capability.

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“…From experimental tests in a precision lathe, the authors were capable of detecting chatter vibration under small number of computations. Later on, Khasawneh et al [91] combined supervised machine learning with Topological Data Analysis (TDA) to obtain an indicator of chatter imminent presence. Under this approach, deterministic and stochastic turning models (with varying cutting coefficients) work together.…”

Section: On-line Chatter Classification Detection and Monitoringmentioning

confidence: 99%

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

et al. 2019

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The general trend towards lightweight components and stronger but difficult to machine materials leads to a higher probability of vibrations in machining systems. Amongst them, chatter vibrations are an old enemy for machinists with the most dramatic cases resulting in machine-tool failure, accelerated tool wear and tool breakage or part rejection due to unacceptable surface finish. To avoid vibrations, process designers tend to command conservative parameters limiting productivity. Among the different machining processes, turning is responsible of a great amount of the chip volume removed worldwide. This paper reports some of the main efforts from the scientific literature to predict stability and to avoid chatter with special emphasis on turning systems. There are different techniques and approaches to reduce and to avoid chatter effects. The objective of the paper is to summarize the current state of research in this hot topic, particularly (1) the mechanistic, analytical, and numerical methods for stability prediction in turning; (2) the available techniques for chatter detection and control; (3) the main active and passive techniques.

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Chatter Classification in Turning using Machine Learning and Topological Data Analysis

Cited by 48 publications

References 36 publications

Spatial applications of topological data analysis: Cities, snowflakes, random structures, and spiders spinning under the influence

Spatial applications of topological data analysis: Cities, snowflakes, random structures, and spiders spinning under the influence

Roundness prediction in centreless grinding using physics-enhanced machine learning techniques

Prediction Methods and Experimental Techniques for Chatter Avoidance in Turning Systems: A Review

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