Chatter Diagnosis in Milling Using Supervised Learning and Topological Features Vector

Chatter detection has become a prominent subject of interest due to its effect on cutting tool life, surface finish and spindle of machine tool. Most of the existing methods in chatter detection literature are based on signal processing and signal decomposition. 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. Persistence diagrams, a method of representing topological features, are not easily used in the context of machine learning, so they must be transformed into a form that is more amenable. Specifically, we will focus on two different methods for featurizing persistence diagrams, Carlsson coordinates and template functions. In this paper, we provide classification results for simulated data from various cutting configurations, including upmilling and downmilling, in addition to the same data with some added noise. Our results show that Carlsson Coordinates and Template Functions yield accuracies as high as 96% and 95%, respectively. We also provide evidence that these topological methods are noise robust descriptors for chatter detection

“Lombard Effect” and Voice Changes in Adductor Laryngeal Dystonia: A Pilot Study.

Objectives: The aim was to describe the acoustic, auditory-perceptive, and subjective voice changes under the Lombardeffect (LE) in adductor laryngeal dystonia (AdLD) patients.Methods: Subjective perception of vocal effort (OMNI Vocal Effort Scale OMNI-VES), Maximum Phonation Time (MPT),and the perceptual severity of dysphonia (GRBAS scale) were assessed in condition of stillness and under LE in 10 AdLDpatients and in 10 patients with typical voice. Speakers were asked to produce the sustained vowel /a/ and to read a phoneti-cally balanced text aloud. Using the PRAAT software, the following acoustic parameters were analyzed: Mean Pitch (Hz), Mini-mum and Maximum Intensity (dB), the Fraction of Locally Unvoiced Frames, the Number of Voice Breaks, the Degree of VoiceBreaks (%), the Cepstral Peak Prominence-Smoothed (CPPS) (dB).Results: Under LE, the AdLD group showed a decrease of both G and S parameters of GRBAS and subjective effort, meanMPT increased significantly; in the controls there were no significant changes. In both groups under LE, pitch and intensity ofthe sustained vowel /a/ significantly increased consistently with LE. In the AdLD group the mean gain of OMNI-VES score andthe mean gain of each parameter of the speech analysis were significantly greater than the controls’ ones.Conclusion: Auditory feedback deprivation obtained under LE improves subjective, perceptual-auditory, and acousticsparameters of AdLD patients. These findings encourage further research to provide new knowledge into the role of the audi-tory system in the pathogenesis of AdLD and to develop new therapeutic strategies.Key Words: acoustic analysis, adductor laryngeal dystonia, audio-vocal feedback control, Lombard effect.Level of Evidence: 4Laryngoscope, 134:3754–3760, 202

Differential expression of HSPA1 and HSPA2 proteins in human tissues; tissue microarray-based immunohistochemical study

In the present study we determined the expression pattern of HSPA1 and HSPA2 proteins in various normal human tissues by tissue-microarray based immunohistochemical analysis. Both proteins belong to the HSPA (HSP70) family of heat shock proteins. The HSPA2 is encoded by the gene originally defined as testis-specific, while HSPA1 is encoded by the stress-inducible genes (HSPA1A and HSPA1B). Our study revealed that both proteins are expressed only in some tissues from the 24 ones examined. HSPA2 was detected in adrenal gland, bronchus, cerebellum, cerebrum, colon, esophagus, kidney, skin, small intestine, stomach and testis, but not in adipose tissue, bladder, breast, cardiac muscle, diaphragm, liver, lung, lymph node, pancreas, prostate, skeletal muscle, spleen, thyroid. Expression of HSPA1 was detected in adrenal gland, bladder, breast, bronchus, cardiac muscle, esophagus, kidney, prostate, skin, but not in other tissues examined. Moreover, HSPA2 and HSPA1 proteins were found to be expressed in a cell-type-specific manner. The most pronounced cell-type expression pattern was found for HSPA2 protein. In the case of stratified squamous epithelia of the skin and esophagus, as well as in ciliated pseudostratified columnar epithelium lining respiratory tract, the HSPA2 positive cells were located in the basal layer. In the colon, small intestine and bronchus epithelia HSPA2 was detected in goblet cells. In adrenal gland cortex HSPA2 expression was limited to cells of zona reticularis. The presented results clearly show that certain human tissues constitutively express varying levels of HSPA1 and HSPA2 proteins in a highly differentiated way. Thus, our study can help designing experimental models suitable for cell- and tissue-type-specific functional differences between HSPA2 and HSPA1 proteins in human tissues

Topological data analysis and machine learning framework for studying time series and image data

The recent advancements in signal acquisition and data mining have revealed the importance of data-driven tools for analyzing signals and images. The availability of large and complex data has also highlighted the need for investigative tools that provide autonomy, noise-robustness, and efficiently utilize data collected from different settings but pertaining to the same phenomenon. State-of-the-art approaches include using tools such as Fourier analysis, wavelets, and Empirical Mode Decomposition for extracting informative features from the data. These features can then be combined with machine learning for clustering, classification, and inference. However, these tools typically require human intervention for feature extraction, and they are sensitive to the input parameters that the user chooses during the laborious but often necessary manual data pre-processing. Therefore, this dissertation was motivated by the need for automatic, adaptive, and noise-robust methods for efficiently leveraging machine learning for studying images as well as time series of dynamical systems. Specifically, this work investigates three application areas: chatter detection in manufacturing processes, image analysis of manufactured surfaces, and tool wear detection during titanium alloys machining. This work's novel investigations are enabled by combining machine learning with methods from Topological Data Analysis (TDA), a relatively recent field of applied topology that encompasses a variety of mature tools for quantifying the shape of data. First, this study experimentally shows for the first time that persistent homology (or persistence) from TDA can be used for chatter classification with accuracies that rival existing detection methods. Further, the efficient use of chatter data sets from different sources is formulated and studied as a transfer learning problem using experimental turning and milling vibration signals. Classification results are shown using comparisons between the TDA pipeline developed in this dissertation and prominent methods for chatter detection. Second, this work describes how to utilize TDA tools for extracting descriptive features from simulated samples generated using different Hurst roughness exponents. The efficiency of the feature extraction is tested by classifying the surfaces according to their roughness level. The resulting accuracies show that TDA can outperform several traditional feature extraction approaches in surface texture analysis. Further, as part of this work, adaptive threshold selection algorithms are developed for Discrete Cosine Transform, and Discrete Wavelet Transform to bypass the need for subjective operator input during surface roughness analysis. Both experimental and synthetic data sets are used to test the effectiveness of these two algorithms. This study also discusses a TDA-based framework that can potentially provide a feasible approach for building an automatic surface finish monitoring system. Finally, this work shows that persistence can be used for tool condition monitoring during titanium alloys machining. Since, in these processes, the cutting tools typically fracture catastrophically before the gradual tool wear reaches the maximum tool life criteria, the industry uses very conservative criteria for replacing the tools. An extensive experiment is described for relating wear markers in various sensor signals to the tool condition at different stages of the tool life. This work shows how, in this setting, TDA provides significant advantages in terms of robustness to noise and alleviating the need for an expert user to extract the informative features. The obtained TDA-based features are compared to existing state-of-the-art featurization tools using feature-level data fusion. The temporal location of the most representative tool condition features is also studied in the signals by considering a variety of window lengths preceding tool wear milestones.Thesis (Ph. D.)--Michigan State University. Mechanical Engineering, 2022Includes bibliographical references (pages 263-287