Chatter, process damping, and chip segmentation in turning: A signal processing approach

An increasing number of aerospace components are manufactured from titanium and nickel alloys that are difficult to machine due to their thermal and mechanical properties. This limits the metal removal rates that can be achieved from the production process. However, under these machining conditions the phenomenon of process damping can be exploited to help avoid self-excited vibrations known as regenerative chatter. This means that greater widths of cut can be taken so as to increase the metal removal rate, and hence offset the cutting speed restrictions that are imposed by the thermo-mechanical properties of the material. However, there is little or no consensus as to the underlying mechanisms that cause process damping. The present study investigates two process damping mechanisms that have previously been proposed in the machining literature: the tool flank/workpiece interference effect, and the short regenerative effect. A signal processing procedure is employed to identify flank/workpiece interference from experimental data. Meanwhile, the short regenerative model is solved using a new frequency domain approach that yields additional insight into its stabilising effect. However, analysis and signal processing of the experimentally obtained data reveals that neither of these models can fully explain the increases in stability that are observed in practice. Meanwhile, chip segmentation effects were observed in a number of measurements, and it is suggested that segmentation could play an important role in the process-damped chatter stability of these materials

An experimental investigation of chatter effects on tool life

Tool wear is one of the most important considerations in machining operations as it affects surface quality and integrity, productivity and cost. The most commonly used model for tool life analysis is the one proposed by F.W. Taylor about a century ago. Although the extended form of this equation includes the effects of important cutting conditions on tool wear, tool life studies are mostly performed under stable cutting conditions where the effect of chatter vibrations are not considered. This paper presents an empirical attempt to understand tool life under vibratory cutting conditions. Tool wear data are collected in turning and milling on different work materials under stable and chatter conditions. The effects of cutting conditions as well as severity of chatter on tool life are analyzed. The results indicate significant reduction in tool life due to chatter as expected. They also show that the severity of chatter, and thus the vibration amplitude, strongly reduces the life of cutting tools. These results can be useful in evaluating the real cost of chatter by including the reduced tool life. They can also be useful in justifying the cost of chatter suppression and more rigid machining systems

Remarks on Bootstrap Percolation in Metric Networks

We examine bootstrap percolation in d-dimensional, directed metric graphs in the context of recent measurements of firing dynamics in 2D neuronal cultures. There are two regimes, depending on the graph size N. Large metric graphs are ignited by the occurrence of critical nuclei, which initially occupy an infinitesimal fraction, f_* -> 0, of the graph and then explode throughout a finite fraction. Smaller metric graphs are effectively random in the sense that their ignition requires the initial ignition of a finite, unlocalized fraction of the graph, f_* >0. The crossover between the two regimes is at a size N_* which scales exponentially with the connectivity range \lambda like_* \sim \exp\lambda^d. The neuronal cultures are finite metric graphs of size N \simeq 10^5-10^6, which, for the parameters of the experiment, is effectively random since N<< N_*. This explains the seeming contradiction in the observed finite f_* in these cultures. Finally, we discuss the dynamics of the firing front

AI-predicted protein deformation encodes energy landscape perturbation

AI algorithms proved excellent predictors of protein structure, but whether their exceptional accuracy is merely due to megascale regression or these algorithms learn the underlying physics remains an open question. Here, we perform a stringent test for the existence of such learning in the Alphafold2 (AF) algorithm: We use AF to predict the subtle structural deformation induced by single mutations, quantified by strain, and compare with experimental datasets of corresponding perturbations in folding free energy $\Delta\Delta G$. Unexpectedly, we find that physical strain alone -- without any additional data or computation -- correlates almost as well with $\Delta \Delta G$ as state-of-the-art energy-based and machine-learning predictors.This indicates that the AF-predicted structures alone encode fine details about the energy landscape. In particular, the structures encode significant information on stability, enough to estimate (de-)stabilizing effects of mutations, thus paving the way for the development of novel, structure-based stability predictors for protein design and evolution

Percolation in living neural networks

We study living neural networks by measuring the neurons' response to a global electrical stimulation. Neural connectivity is lowered by reducing the synaptic strength, chemically blocking neurotransmitter receptors. We use a graph-theoretic approach to show that the connectivity undergoes a percolation transition. This occurs as the giant component disintegrates, characterized by a power law with critical exponent $\beta \simeq 0.65$ is independent of the balance between excitatory and inhibitory neurons and indicates that the degree distribution is gaussian rather than scale freeComment: PACS numbers: 87.18.Sn, 87.19.La, 64.60.Ak http://www.weizmann.ac.il/complex/tlusty/papers/PhysRevLett2006.pd

Host susceptibility hypothesis for shell disease in American lobsters

Author Posting. © American Fisheries Society, 2007. This article is posted here by permission of American Fisheries Society for personal use, not for redistribution. The definitive version was published in Journal of Aquatic Animal Health 19 (2007): 215-225, doi:10.1577/H06-014.1.Epizootic shell disease (ESD) in American lobsters Homarus americanus is the bacterial degradation of the carapace resulting in extensive irregular, deep erosions. The disease is having a major impact on the health and mortality of some American lobster populations, and its effects are being transferred to the economics of the fishery. While the onset and progression of ESD in American lobsters is undoubtedly multifactorial, there is little understanding of the direct causality of this disease. The host susceptibility hypothesis developed here states that although numerous environmental and pathological factors may vary around a lobster, it is eventually the lobster's internal state that is permissive to or shields it from the final onset of the diseased state. To support the host susceptibility hypothesis, we conceptualized a model of shell disease onset and severity to allow further research on shell disease to progress from a structured model. The model states that shell disease onset will occur when the net cuticle degradation (bacterial degradation, decrease of host immune response to bacteria, natural wear, and resorption) is greater than the net deposition (growth, maintenance, and inflammatory response) of the shell. Furthermore, lesion severity depends on the extent to which cuticle degradation exceeds deposition. This model is consistent with natural observations of shell disease in American lobster