no code implementations • 23 Mar 2018 • Firas A. Khasawneh, Elizabeth Munch, Jose A. Perea
The features gleaned from the deterministic model are then utilized for characterization of chatter in a stochastic turning model where there are very limited analysis methods.
no code implementations • 15 Feb 2019 • Joshua R. Tempelman, Firas A. Khasawneh
Bi-variate density estimates of the randomly projected time-series in the $p$-$q$ plane described in Gottwald and Melbourne's approach for 0/1 detection are used to generate a gray-scale image.
Chaotic Dynamics
1 code implementation • 19 Feb 2019 • Jose A. Perea, Elizabeth Munch, Firas A. Khasawneh
Specifically, we begin by characterizing relative compactness with respect to the bottleneck distance, and then provide explicit theoretical methods for constructing compact-open dense subsets of continuous functions on persistence diagrams.
no code implementations • 16 Apr 2019 • Audun Myers, Elizabeth Munch, Firas A. Khasawneh
Specifically, we show how persistent homology, a tool from TDA, can be used to yield a compressed, multi-scale representation of the graph that can distinguish between dynamic states such as periodic and chaotic behavior.
Chaotic Dynamics Computational Geometry Information Theory Information Theory Data Analysis, Statistics and Probability
1 code implementation • 3 May 2019 • Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto
The increasing availability of sensor data at machine tools makes automatic chatter detection algorithms a trending topic in metal cutting.
no code implementations • 21 May 2019 • Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto
We present the results for several choices of the topological feature vectors, and we compare our results to the WPT and EEMD methods using experimental turning data.
no code implementations • 5 Aug 2019 • Melih C. Yesilli, Firas A. Khasawneh, Andreas Otto
In this paper, we present an alternative approach for chatter detection based on K-Nearest Neighbor (kNN) algorithm for classification and the Dynamic Time Warping (DTW) as a time series similarity measure.
no code implementations • 18 Oct 2019 • Sarah Tymochko, Elizabeth Munch, Firas A. Khasawneh
As the field of Topological Data Analysis continues to show success in theory and in applications, there has been increasing interest in using tools from this field with methods for machine learning.
no code implementations • 27 Oct 2019 • Melih C. Yesilli, Sarah Tymochko, Firas A. Khasawneh, Elizabeth Munch
In this study, we use topological features of data simulating cutting tool vibrations, combined with four supervised machine learning algorithms to diagnose chatter in the milling process.
no code implementations • 28 Aug 2020 • Melih C. Yesilli, Firas A. Khasawneh
In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment.
no code implementations • 19 Oct 2021 • Melih C. Yesilli, Firas A. Khasawneh
Therefore, fast and automatic determination of the roughness level is essential to avoid costs resulting from surfaces with unacceptable finish, and user-intensive analysis.
no code implementations • 11 Apr 2022 • Melih C. Yesilli, Firas A. Khasawneh, Brian Mann
Three challenges can be identified in applying machine learning for chatter detection at large in industry: an insufficient understanding of the universality of chatter features across different processes, the need for automating feature extraction, and the existence of limited data for each specific workpiece-machine tool combination.
no code implementations • 12 Apr 2022 • Melih C. Yesilli, Jisheng Chen, Firas A. Khasawneh, Yang Guo
Comparing our results with the heuristic threshold selection approach shows good agreement with mean accuracies as high as 95\%.
no code implementations • 27 Apr 2022 • Audun Myers, Firas A. Khasawneh, Elizabeth Munch
For this task, we turn to the field of topological data analysis (TDA), which encodes information about the shape and structure of data.
no code implementations • 20 May 2022 • Audun D. Myers, Max M. Chumley, Firas A. Khasawneh, Elizabeth Munch
We contrast dynamic state detection from time series using a coarse-grained state-space network (CGSSN) and topological data analysis (TDA) to two state of the art approaches: ordinal partition networks (OPNs) combined with TDA and the standard application of persistent homology to the time-delay embedding of the signal.
no code implementations • 29 Jan 2024 • Sunia Tanweer, Firas A. Khasawneh
The current state of the art for detecting these bifurcations requires reliable kernel density estimates computed from an ensemble of system realizations.