Results 21 to 30 of about 443,795 (288)

Dielectronic Recombination in Photoionized Gas. II. Laboratory Measurements for Fe XVIII and Fe XIX [PDF]

, 1999
In photoionized gases with cosmic abundances, dielectronic recombination (DR) proceeds primarily via nlj --> nl'j' core excitations (Dn=0 DR). We have measured the resonance strengths and energies for Fe XVIII to Fe XVII and Fe XIX to Fe XVIII Dn=0 DR ...
Badnell, N. R., Bartsch, T., Brandau, C., Chen, M. H., Grieser, M., Hoffknecht, A., Kahn, S. M., Linkemann, J., Mueller, A., Repnow, R., Saghiri, A. A., Savin, D. W., Schippers, S., Schmitt, M., Schwalm, D., Wolf, A.   +15 more
core   +3 more sources

Modeling batch annealing process using data mining techniques for cold rolled steel sheets [PDF]

, 2011
The annealing process is one of the important operations in production of cold rolled steel sheets, which significantly influences the final product quality of cold rolling mills.
Bagheri, A, Moghimi, M, Saraee, MH
core   +1 more source

Thermal Error Analysis of Five-Axis Machine Tools Based on Five-Point Test Method

Lubricants, 2022
The accuracy of five-axis machine tools is a key performance indicator. Among the various error sources of high precision five-axis machine tools, thermal and geometric errors occupy the majority.
Yu Li, Hongchuan Tian, Difei Liu, Quanbo Lu   +3 more
doaj   +1 more source

Modeling Extragalactic Foregrounds and Secondaries for Unbiased Estimation of Cosmological Parameters From Primary CMB Anisotropy [PDF]

, 2011
Using the latest physical modeling and constrained by the most recent data, we develop a phenomenological parameterized model of the contributions to intensity and polarization maps at millimeter wavelengths from external galaxies and Sunyaev-Zeldovich ...
Amblard, Battaglia, Cardoso, Dunkley, Fendt, Fixsen, G. Holder, Giavalisco, Giodini, Gold, Gruzinov, Haiman, Hall, Hodge, Hu, Iliev, J. Dudley, Keisler, Knox, Knox, Komatsu, Komatsu, L. Knox, L. Shaw, Lagache, Lau, Lewis, Lueker, M. Millea, McQuinn, Navarro, O. Doré, O. Zahn, Peacock, Planck Collaboration., Prunet, Puget, Santos, Scott, Sehgal, Shaw, Shaw, Shirokoff, Tegmark, Tegmark, Tinker, Trac, Vieira, Viero, White, Wright, Y.-S. Song, Zahn, Zahn, Zhang   +54 more
core   +2 more sources

An Unsupervised Regularization and Dropout based Deep Neural Network and Its Application for Thermal Error Prediction

Applied Sciences, 2020
Due to the large size of the heavy duty machine tool-foundation systems, space temperature difference is high related to thermal error, which affects to system’s accuracy greatly. The recent highly focused deep learning technology could be an alternative
Yang Tian, Guangyuan Pan
doaj   +1 more source

The Thermal Error Estimation of the Machine Tool Spindle Based on Machine Learning

Machines, 2021
Thermal error is one of the main sources of machining error of machine tools. Being a key component of the machine tool, the spindle will generate a lot of heat in the machining process and thereby result in a thermal error of itself.
Yu-Cheng Chiu, Po-Hsun Wang, Yuh-Chung Hu   +2 more
doaj   +1 more source

A novel approach for ANFIS modelling based on Grey system theory for thermal error compensation [PDF]

, 2014
The fast and accurate modelling of thermal errors in machining is an important aspect for the implementation of thermal error compensation. This paper presents a novel modelling approach for thermal error compensation on CNC machine tools.
Abdulshahed, Ali, Fletcher, Simon, Longstaff, Andrew P   +2 more
core   +1 more source

The application of ANFIS prediction models for thermal error compensation on CNC machine tools [PDF]

, 2014
Thermal errors can have significant effects on CNC machine tool accuracy. The errors come from thermal deformations of the machine elements caused by heat sources within the machine structure or from ambient temperature change.
Abdulshahed, Abdulshahed, Ali M. Abdulshahed, Andrew P. Longstaff, Arosha Senanayake, Bezdek, Bryan, Chen, Deng, Dunn, Guillaume, Han, Han, Jain, Jang, Kar, Karahoca, Lee, Li, Liu, Mayr, Melin, Mian, Park, Postlethwaite, Ramesh, Rizal, Simon Fletcher, Soto, Soto, Tan, Velmurugan, Wang, Wei, Yan, Özkan   +35 more
core   +1 more source

Numerical and Experimental Investigation of the Temperature Rise of Cutting Tools Caused by the Tool Deflection Energy

Machines, 2021
Tool temperature variation in flank milling usually causes excessive tool wear, shortens tool life, and reduces machining accuracy. The heat source is the primary factor of the machine thermal error in the process of cutting components.
Yunxia Guo, Wenhua Ye, Xiang Xu
doaj   +1 more source

A Review of Machine Learning-Based Thermal Error Modeling Methods for CNC Machine Tools

Machines
Heat source-induced thermal error is a primary element influencing the precision of CNC machine tools. A practical and economical approach to mitigating thermal errors is through thermal error compensation.
Sen Mu, Chunping Yu, Kunlong Lin, Caijiang Lu, Xi Wang, Tao Wang, Guoqiang Fu   +6 more
doaj   +1 more source