scholarly journals PREDICTION OF THERMAL ERRORS IN MACHINE TOOLS THROUGH DECOUPLED SIMULATIONS USING GENETIC ALGORITHM AND ARTIFICIAL NEURAL NETWORKS

2021 ◽  
Vol 2021 (3) ◽  
pp. 4683-4691
Author(s):  
T. Suresh Kumar ◽  
◽  
J. Glaenzel ◽  
M. Bergmann ◽  
M. Putz ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Machine Tool ◽  
Machine Tools ◽  
Waste Heat ◽  
Internal Heat ◽  
Thermal Error ◽  
Heat Sources ◽  
Thermal Errors ◽  
Production Processes ◽  
Internal Heat Sources

Thermal errors are one of the major contributors towards positioning discrepancies in machine tools in precision machining. Along with friction and waste heat generated from production processes and internal heat sources, environmental influences around the machine tool create considerable thermal gradients followed by non-linear structural deformations. Efficient quantification of these three contributing sources of thermal errors are required in order to formulate a reliable thermal-error compensation system. The creation of all possible thermal configurations, which a machine tool could be subjected to, is experimentally infeasible and requires complex and time-consuming coupled flow and thermo-structural simulations. This paper presents a new approach in thermal error prediction by using CFD and finite element (FE) simulations to train a three-level interconnected neural network system. The first level essentially decouples flow simulations from thermo-structural simulations using optimal FE node points found using a Genetic Algorithm (GA), which significantly reduces the required training data. The boundary convection data obtained from this level is used in the second level to predict possible thermal configurations of the machine tool, after careful consideration of parameters related to internal heat sources and production processes. The third level maps these thermal configurations onto displacements on the machine tool.

Thermal Error Modeling of Machine Tools Based on M-RAN

2011 ◽  
Vol 121-126 ◽  
pp. 529-533
Author(s):  
Jian Han ◽  
Li Ping Wang ◽  
Ning Bo Cheng ◽  
Xu Wang
Keyword(s):  
Machine Tool ◽  
Eddy Current ◽  
Machine Tools ◽  
Thermal Error ◽  
Error Modeling ◽  
Test Results ◽  
Thermal Errors ◽  
Machining Center ◽  
Machining Errors ◽  
Minimal Resource

Thermal error in machine tools is one of the most significant causes of machining errors. This paper presents a new modeling method for machine tool error. Minimal-resource allocating networks (M-RAN) are used to establish the relationships between the temperature variables and thermal errors. Pt-100 thermal resistances and eddy current sensors are used to measure the temperature variables and the thermal errors respectively. A machining center is used to experiment. The test results show that method with minimal-resource allocating networks can predict the thermal errors of the machine accurately.

scholarly journals Internal Heat Gains in a Lunar Base—A Contemporary Case Study

Energies ◽  
2020 ◽  
Vol 13 (12) ◽  
pp. 3213
Author(s):  
Marcin Kaczmarzyk ◽  
Aleksander Starakiewicz ◽  
Aleksander Waśniowski
Keyword(s):  
Energy Storage ◽  
Alternative Energy ◽  
Heat Dissipation ◽  
Life Support ◽  
Waste Heat ◽  
Internal Heat ◽  
Lithium Ion ◽  
Heat Sources ◽  
Internal Heat Sources ◽  
Storage Technologies

The Moon’s environmental conditions present limited opportunities for waste heat dissipation, so internal heat gains (IHG) are a key component of thermal balance in a lunar building. Despite the significant development in energy saving and energy storage technologies of the last thirty years, the issue of IHG in lunar buildings has not been readdressed since the early 1990s. This study is based on an inspection of internal heat sources conducted aboard LUNARES, the first European extraterrestrial analogue habitat. The equipment absent on LUNARES, but indispensable for an actual lunar base, was identified and accounted for, along with additional laboratory and maintenance equipment. Three main groups of internal heat sources were identified and studied in detail. Waste heat generated by electric devices was accounted for, along with occupational heat loads adjusted for lunar partial gravity conditions. Assuming a photovoltaic power source for the studied building, two alternative energy storage systems (ESS) were analysed as another source of waste heat. Depending on the time of lunar day and applied ESS, the nominal IHG were between 73 and 133 W/m2. The most significant internal heat sources in a lunar base are life support systems and potentially, regenerative fuel cells; thus, lithium–ion batteries were recommended for ESS. Within assumed parameter range, parametric study exhibited differences in IHG between 41.5 and 163 W/m2.

Study on the Thermal Errors of CNC Machine Tools

2012 ◽  
Vol 472-475 ◽  
pp. 2918-2921
Author(s):  
Hong Qi Luo ◽  
Yue Hua Lai
Keyword(s):  
Machine Tools ◽  
Thermal Deformation ◽  
Thermal Error ◽  
External Heat ◽  
Cnc Machining ◽  
Cnc Machine Tools ◽  
Heat Sources ◽  
Cnc Machine ◽  
Thermal Errors ◽  
Machining Errors

Thermal deformation is produced by heat sources in CNC machine tools. Thermal error is one of the main parts in CNC machining errors. The internal and external heat sources were introduced. The research status about thermal errors was analyzed, including identification of thermal sensitive points, precaution against thermal errors and error compensation. Finally, thermal error models were summarized and discussed.

scholarly journals Application of the digital model thermal errors of machine tools in automated production

2021 ◽  
Vol 2094 (4) ◽  
pp. 042022
Author(s):  
V V Pozevalkin ◽  
A N Polyakov
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Machine Tools ◽  
Polynomial Regression ◽  
Block Diagram ◽  
Thermal Error ◽  
External Input ◽  
Machine Model ◽  
Automated Production ◽  
Thermal Errors

Abstract The article presents a predicting method for a machine tool thermal error based on a nonlinear autoregressive neural network with an external input, as well as methods for smoothing experimental data obtained from measuring devices by approximation using polynomial regression and the gray systems theory. The development of accurate and robust thermal models is a critical step in achieving high productivity in thermal deformation reduction techniques on machine tools. Because thermal deformations of the machine structure caused by temperature increase often lead to thermal errors and reduce the accuracy of machining parts. The use of neural networks is a promising direction in solving forecasting problems. The authors propose a block diagram of a thermal process digital twin based on a neural network, which can be used in automated production. The results of the experiment carried out for the machine model 400V are obtained in the form of an assessment of approximation quality and accuracy of the forecasting model. The results show that the use of the proposed smoothing methods and a model for predicting a machine tool thermal error based on a neural network can improve the forecast accuracy.

Application of Ubiquitous Sensor Network in Collection and Analysis of CNC Machine Tool Processing Status Information

2010 ◽  
Vol 455 ◽  
pp. 621-624
Author(s):  
X. Li ◽  
Y.Y. Yu
Keyword(s):  
Machine Tool ◽  
Sensor Network ◽  
Machine Tools ◽  
Thermal Error ◽  
Development Trend ◽  
Cnc Machine Tools ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Status Information ◽  
The Development Trend

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.

Sign in / Sign up

Export Citation Format

Share Document