Dynamic Model of Spindle-Holder Taper Joint of BT40 Holder

The dynamic characteristics of spindle-holder taper joint greatly influencethe performance of machine tools. This paper proposed a 24-DOFs spring-damper model to deal with the dynamic model of spindle-holder taper joint. To identify the stiffness of springs and damping coefficient of damper, a parameter identification method was developed based on the inverse relationship of dynamic matrix and frequency response function matrix. Using modal test and the proposed dynamic model, the taper joint of BT40 holder was studied. At the end of this paper, a case was presented to validate the dynamic model. Because the radial, tangential and axial effects of the taper joint were all taken into considerations, the proposed model provided a higher accurate model of spindle-holder taper joint.

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Locating Al in the DABCO catalyst before and after Al extraction

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100174461 ◽

1990 ◽

Vol 48 (4) ◽

pp. 265-267

Author(s):

Kathleen B. Reuter

Keyword(s):

Active Site ◽

Inverse Relationship ◽

Transverse Direction ◽

Reaction Rate ◽

Acid Phosphate ◽

Fracture Surfaces ◽

Al Content ◽

Before And After ◽

Relationship Of

The reaction rate and efficiency of piperazine to 1,4-diazabicyclo-octane (DABCO) depends on the Si/Al ratio of the MFI topology catalysts. The Al was shown to be the active site, however, in the Si/Al range of 30-200 the reaction rate increases as the Si/Al ratio increases. The objective of this work was to determine the location and concentration of Al to explain this inverse relationship of Al content with reaction rate.Two silicalite catalysts in the form of 1/16 inch SiO2/Al2O3 bonded extrudates were examined: catalyst A with a Si/Al of 83; and catalyst B, the acid/phosphate Al extracted form of catalyst A, with a Si/Al of 175. Five extrudates from each catalyst were fractured in the transverse direction and particles were obtained from the fracture surfaces near the center of the extrudate diameter. Particles were also obtained from the outside surfaces of five extrudates.

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Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

SN Applied Sciences ◽

10.1007/s42452-021-04427-5 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Jianlei Zhang ◽

Yukun Zeng ◽

Binil Starly

Keyword(s):

Neural Network ◽

Tool Wear ◽

Machine Tool ◽

Recurrent Neural Network ◽

Machine Tools ◽

Prediction Performance ◽

Sequential Data ◽

Diagnosis And Prognosis ◽

Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

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The Constitutive Model for High Temperature Flow Behavior of SiC/6168Al Composite

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.1089.37 ◽

2015 ◽

Vol 1089 ◽

pp. 37-41

Author(s):

Jiang Wang ◽

Sheng Li Guo ◽

Sheng Pu Liu ◽

Cheng Liu ◽

Qi Fei Zheng

Keyword(s):

Constitutive Model ◽

Hot Deformation ◽

Flow Behavior ◽

Type Equation ◽

Strain Rate Range ◽

Compression Tests ◽

Stress Strain ◽

Hollomon Parameter ◽

Proposed Model ◽

Relationship Of

The hot deformation behavior of SiC/6168Al composite was studied by means of hot compression tests in the temperature range of 300-450 °C and strain rate range of 0.01-10 s-1. The constitutive model was developed to predict the stress-strain curves of this composite during hot deformation. This model was established by considering the effect of the strain on material constants calculated by using the Zenter-Hollomon parameter in the hyperbolic Arrhenius-type equation. It was found that the relationship of n, α, Q, lnA and ε could be expressed by a five-order polynomial. The stress-strain curves obtained by this model showed a good agreement with experimental results. The proposed model can accurately describe the hot flow behavior of SiC/6168Al composite, and can be used to numerically analyze the hot forming processes.

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Inverse Relationship of Glucose-6-Phosphate Levels and Glucose Utilization in 6C3HED Lymphoma

Nature ◽

10.1038/207478a0 ◽

1965 ◽

Vol 207 (4996) ◽

pp. 478-480 ◽

Cited By ~ 5

Author(s):

CHARLES E. WENNER

Keyword(s):

Inverse Relationship ◽

Glucose Utilization ◽

Relationship Of

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The Relationship of Undifferentiated Mesenchymal Cells to TMJ Articular Tissue Thickness

Journal of Dental Research ◽

10.1177/00220345920710111001 ◽

1992 ◽

Vol 71 (11) ◽

pp. 1816-1821 ◽

Cited By ~ 22

Author(s):

C.A. Bibb ◽

A.G. Pullinger ◽

F. Baldioceda

Keyword(s):

Soft Tissue ◽

Inverse Relationship ◽

Biological Processes ◽

Spearman Correlation ◽

Articular Eminence ◽

Tissue Thickness ◽

Cartilage Layer ◽

Soft Tissue Thickness ◽

Relationship Of ◽

The Relationship

Undifferentiated mesenchymal (UM) cells, the progenitor cells of the cartilage layer, have been assigned a significant role in TMJ articular tissue maintenance. This was based on reports of UM cell reduction with increased soft-tissue thickness for the condyle and temporal component. However, the strength of this inverse relationship was not presented and remained unclear. The purpose of the present study was to assess the strength of the correlation between UM cell presence and soft-tissue thickness in young adult TMJs at autopsy. Sagittal histological sections from the central thirds of 50 joints were evaluated with respect to articular soft-tissue thickness, histological character, and UM cell presence in the condyle and temporal component. The superior sector ofthe condyle and the articular eminence showed the greatest variability in soft-tissue thickness and were the only areas to show localized UM cell absence. The eminence was the only location to show an inverse relationship between soft-tissue thickness and UM cell presence, and this was consistent in both an ANOVA (p = 0.0016) and a Spearman correlation analysis. However, the strength of this correlation was only moderate (rho = -0.52), and no such relationship was observed in any other location. This study suggests that the relationship between UM cell presence and soft-tissue thickness is more complex than previously hypothesized and that the contribution of UM cells to articular tissue maintenance has been overstated, while other biological processes were overlooked.

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