Results 101 to 110 of about 1,892,423 (234)

Thermal Error Transfer Prediction Modeling of Machine Tool Spindle with Self-Attention Mechanism-Based Feature Fusion

Thermal errors affect machining accuracy in high-speed precision machining. The variability of machine tool operating conditions poses a challenge to the modeling of thermal errors.
Yue Zheng, Tao Wang, Caijiang Lu, Sen Mu, Guoqiang Fu, Xi Wang   +5 more
core   +1 more source

Digital twin driven thermal error control of linear axis for face gear grinding machine tool

The face gear transmission system offers advantages such as high interchangeability, strong load capacity, and effective power distribution, making it suitable for defense applications.
Totis G., Liu J., Li M., Ma C.
core   +1 more source

A Two-Stage Deep Learning Method for Non-Invasive Sow Body Temperature Prediction Fusing Thermal Imaging and Environmental Parameters. [PDF]

Animals (Basel)
Xu S, Qin Z, Huang Q, Tan C, Xu X, Li X.
europepmc   +1 more source

A novel meta-learning-based spatio-temporal modeling approach for thermal error prediction of multiple machine tools with few labeled data

International audienceThermal errors have a significant effect on the machining accuracy of high-precision machine tools. Developing a high precision and robust thermal error prediction model plays an important role in compensating thermal error and ...
Lin, Weicheng, Yin, Ling, Bassir, David, Zhang, Lijuan, Qiu, Chaochao, Liang, Qiongke   +5 more
core   +1 more source

Interpretable and extrapolation-stable model for predicting nanofluid thermal conductivity. [PDF]

Sci Rep
Zinhom E, Radwan SS, Elmasry A, Sharaf MAM, Nassar MM.   +4 more
europepmc   +1 more source

Multi-scale hybrid composites of erythritol/diatomite/GNP for enhanced thermal energy storage: experimental and machine learning optimization. [PDF]

Sci Rep
Nassar A, Nassar E, Rivilla I, Labidi J, Sarasini F.   +4 more
europepmc   +1 more source

An approach for estimating thermoelectric module equivalent properties by thermal resistance and numerical modeling. [PDF]

iScience
Pham MQ, Nguyen VL, Vo TQ, Hong TD.
europepmc   +1 more source

Development of Empirical Equations to Predict Sweating Skin Surface Temperature for Thermal Manikins in Warm Environments. [Elektronisk resurs]

, 2010
Clothing evaporative resistance is one of the most important parameters for clothing comfort. The clothing evaporation resistance can be measured on a sweating guarded hotplate, a sweating thermal manikin or a human subject.
Kuklane, Kalev,, Gao, Chuansi,, Wang, Faming,, Lund University., Holmér, Ingvar,   +4 more
core  

Machine learning potentials for modeling alloys across compositions. [PDF]

Sci Adv
Sheriff K, Xiao DZ, Cao Y, Owen LR, Freitas R.   +4 more
europepmc   +1 more source

Realistic 3D morphology reshapes insect heat budgets. [PDF]

Sci Rep
Ostwald MM, Johnson MG, Youngblood A, Childress A, Seltmann KC.   +4 more
europepmc   +1 more source