Air-Bearing Applications to Machine Tools and Measuring Instruments

1968 ◽  
Vol 90 (4) ◽  
pp. 680-686 ◽  
Author(s):  
H. L. Wunsch

This paper attempts to illustrate the advantages and limitations of air bearings in the machine-tool and measuring instrument field by reference to actual industrial applications. It also indicates likely future developments.

Author(s):  
E Baumeister ◽  
S Klaeger ◽  
A Kaldos

The ability to recycle mechanical and structural materials at the end of their useful lifetime is of high importance. The use of non-conventional materials in these applications enables such recycling and provides a number of other advantages, including increased quality, better economics, protection of the environment, and reduction of energy consumption. New lightweight materials, which have similar properties to their more traditional alternatives, are therefore highly significant for some industrial applications. In machine-tool design, for example, it is important to reduce the mass of the moving parts to obtain better dynamic characteristics. The current use of the term ‘lightweight materials’ refers to the lighter metallic materials such as aluminium, titanium, and magnesium. However, in recent years, the application of alternative, recyclable materials has substantially increased, for example the use of polymer concrete for supports, casing, or tables in machine tools with lighter weight and improved thermodynamic properties. An advantage of hollow-sphere-composites (HSC), which mostly consist of hollow spheres with different particle proportions and a reactive resin system, is that they can be recycled with ease. The aim of this article is to evaluate and characterize the mechanical and thermal properties of HSC as necessary input parameters for the design of various machine-tool and robotic system elements. The versatility of HSCs for machine tools and jig design is also demonstrated.


2018 ◽  
pp. 76-84
Author(s):  
K. V. Sorokin ◽  
E. A. Sunarchina

Improvement of orbits precision is one of the most important tasks of space surveillance catalogue maintenance. The solution of this problem is directly related to an adequate consideration of the errors of the coordinate information from the measuring instruments. The article consideresd a new method for estimating the precision of measuring instruments on the catalog orbits. To carry out such analysis, in PJSC «VIMPEL» special technological program was created. Main results of a study of radar errors with orbits of space surveillance catalogue was presented. Also, the results were compared with data of measuring instrument's calibration software complex. This software complex provides determination of satellite's position with errors less than 10 m. A new dynamic model of measuring instrument errors is proposed.


2021 ◽  
Vol 3 (4) ◽  
Author(s):  
Jianlei Zhang ◽  
Yukun Zeng ◽  
Binil Starly

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.


2010 ◽  
Vol 455 ◽  
pp. 621-624
Author(s):  
X. Li ◽  
Y.Y. Yu

Because of the practical requirement of real-time collection and analysis of CNC machine tool processing status information, we discuss the necessity and feasibility of applying ubiquitous sensor network(USN) in CNC machine tools by analyzing the characteristics of ubiquitous sensor network and the development trend of CNC machine tools, and application of machine tool thermal error compensation based on USN is presented.


2012 ◽  
Vol 531-532 ◽  
pp. 751-754
Author(s):  
Ying Xue Yao ◽  
Hong Bo Wang ◽  
Liang Zhou

A low-speed spindle running on air bearings is presented, it is used on rotary viscometer based on velocity attenuation of rotating cylinder. Principle of spindle is introduced, it is composed of a low speed motor and an air bearing. The low speed motor is a coupling of two motors. Design of the spindle shows the structure of it. Materials of the spindle are selected. The spindle is machined and operation process of it shows it is suitable for driving part of rotary viscometer based on velocity attenuation of rotating cylinder.


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