Measurement of spindle thermal errors in machine tool using hemispherical ball bar test

Yang, Seung-Han; Kim, Ki-Hoon; Park, Yong Kuk

doi:10.1016/j.ijmachtools.2003.08.010

Cited by 46 publications

(16 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similar to the volumetric calibration, there are several measurement systems that can be used for the measurement of displacement vectors, like geometrical standards, 3D artefacts, a touch-trigger probe, or a double ball bar [210].…”

Section: Measuring Local Displacement Vectorsmentioning

confidence: 99%

Thermal issues in machine tools

et al. 2012

View full text Add to dashboard Cite

Section: Measuring Local Displacement Vectorsmentioning

confidence: 99%

Thermal issues in machine tools

et al. 2012

View full text Add to dashboard Cite

“…Another indirect measurement is that of contour measurement, with multiple axes moving along a predefined path simultaneously [32]. Circular paths with two linear axes moving simultaneously [33], circular paths with two linear axes and one rotary axis moving simultaneously [34] and three-dimensional helix paths with three linear axes moving simultaneously [35,36] are all possible trajectories for indirect measurements. Commercially available measuring equipment capable of contour measurement includes 3D ball plates [37], laser tracers [38], the R-test system and the DBB [39] etc.…”

Section: Case Study Identified Geometric Errorsmentioning

confidence: 99%

Volumetric Error Compensation in Five-Axis CNC Machining Center Through Kinematics Modeling of Geometric Error

Pashaki

Pouya²

2016

Adv. Sci. Technol. Res. J.

View full text Add to dashboard Cite

Accuracy of a five-axis CNC machine tool is affected by a vast number of error sources. This paper investigates volumetric error modeling and its compensation to the basis for creation of new tool path for improvement of work pieces accuracy. The volumetric error model of a five-axis machine tool with the configuration RTTTR (tilting head B-axis and rotary table in work piece side A΄) was set up taking into consideration rigid body kinematics and homogeneous transformation matrix, in which 37 error components are included. Volumetric error comprises 37 error components that can separately reduce geometrical and dimensional accuracy of work pieces. The machining accuracy of work piece is guaranteed due to the position of the cutting tool center point (TCP) relative to the work piece. The cutting tool is deviated from its ideal position relative to the work piece and machining error is experienced. For compensation process detection of the present tool path and analysis of the RTTTR fiveaxis CNC machine tools geometrical error, translating current position of component to compensated positions using the Kinematics error model, converting newly created component to new tool paths using the compensation algorithms and finally editing old G-codes using G-code generator algorithm have been employed.

show abstract

“…It has the basic ability to learn, memorize, and generalize. (11)(12)(13)(14)(15)(16) Among the many types of neural network, the back propagation neural network is the core of all the others and has the following four advantages: (1) function approximation: train the input vector and correspondent output vector to a model and establish function approximation; (2) model identification: link an undetermined output vector with an input vector, thereby achieving the effect of discrimination; (3) classification: define and classify the type of input vector; and (4) data compression: reduce the dimension of the input vector for convenient transfer and storage.…”

Section: Back Propagation Neural Network For Mtdsmentioning

confidence: 99%

Development of a Machine Tool Diagnostic System Using Micro-Electromechanical System Sensors: A Case Study

2015

Sensors and Materials

View full text Add to dashboard Cite

Key words: MEMS sensor, back propagation neural network, machine tool diagnostic systemIn this study, micro-electromechanicals (MEMS) system sensors were used to extract physical signals from a machine tool. A performance assessment was carried out using fuzzy logic theory. This, in turn, was used to judge the ownership of various signals. It then defined the level that each ownership group belonged to in order to determine whether the performance was broken or not. If normal, the system determined specified rules for such normality. During the process of extracting the vibration and noise signals, the system used Fourier transform to analyze any changes made to each signal in the frequency field, and then principal component analysis was used to decrease the data dimensions. We then evaluated the work status of the machine tool on the basis of the signal features. Furthermore, we built a feature math model from the recorded signals using a back propagation neural network and further determined the abnormal items using an error function. Finally, we obtained a diagnostic feature for the performance of the machine tool using physical signals through diagnostic reports from a humanmachine interface. The machine tool diagnostic system is able to provide maintenance personnel with a proper way of responding quickly to any reduction in the output from the machine tool so as to avoid further damage.

show abstract

Measurement of spindle thermal errors in machine tool using hemispherical ball bar test

Cited by 46 publications

References 16 publications

Thermal issues in machine tools

Thermal issues in machine tools

Volumetric Error Compensation in Five-Axis CNC Machining Center Through Kinematics Modeling of Geometric Error

Development of a Machine Tool Diagnostic System Using Micro-Electromechanical System Sensors: A Case Study

Contact Info

Product

Resources

About