Results 41 to 50 of about 1,892,423 (234)

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

Dynamic modeling for spindle thermal error in machine tools under time-varying rotational speeds and cooling states

Case Studies in Thermal Engineering
To accurately predict and compensate for spindle thermal error under time-varying rotational speeds and cooling states, this paper proposes a novel dynamic thermal error modeling method characterized by a dual-attention-enhanced parallel branch ...
Ji Peng, Ke Yang, Zhirong Zhang, Yebing Mao, Feng Tan, Xiaobing Hu, Ming Yin, Zhoujie Zhao, Longjiang Li   +8 more
doaj   +1 more source

Thermal error modeling method of truss robot based on GA-LSTM

Industrial robot
Purpose The selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability ...
Long Li, Binyang Chen, Jiangli Yu
semanticscholar   +1 more source

An efficient offline method for determining the thermally sensitive points of a machine tool structure

, 2012
Whether from internal sources or arising from environmental sources, thermal error in most machine tools is inexorable. Out of several thermal error control methods, electronic compensation can be an easy-to-implement and cost effective solution. However,
Longstaff, Andrew P., Mian, Naeem, Mian, Naeem S., Fletcher, Simon, Longstaff, Andrew, Myers, Alan   +5 more
core   +1 more source

Hybrid-Mechanism Deep Learning Modeling for Machine Tool Thermal Error: Robust Prediction via Few-Sample Learning

Machines
To address spindle thermal error in precision machining, this study proposes a hybrid modeling method. It combines a physical model for linear deformation with a GAT-LSTM network.
Hongru Chen, Yubin Huang, Chaochao Qiu, Xueyan Ning, Pingjiang Wang, Ke Yang   +5 more
doaj   +1 more source

Heat Transfer and Structural Characteristics of Dissimilar Joints Joining Ti-64 and NiTi via Laser Welding

Energies, 2022
This study investigates the thermal-stress characteristics of a bi-metallic Ti-6Al-4V-Nitinol butt joints manufactured via laser welding. Particularly, the thermal profile along the weld interface and the deformation profile of the finished welded ...
Aspen Glaspell, Jose Angel Diosdado De la Pena, Saroj Dahal, Sandesh Neupane, Jae Joong Ryu, Kyosung Choo   +5 more
doaj   +1 more source

Thermal Error Modelling of the Spindle Using Neurofuzzy Systems [PDF]

Mathematical Problems in Engineering, 2016
This paper proposes a new combined model to predict the spindle deformation, which combines the grey models and the ANFIS (adaptive neurofuzzy inference system) model. The grey models are used to preprocess the original data, and the ANFIS model is used to adjust the combined model.
Jingan Feng, Xiaoqi Tang, Yanlei Li, Bao Song   +3 more
openaire   +1 more source

Efficient estimation by FEA of machine tool distortion due to environmental temperature perturbations

, 2013
Machine tools are susceptible to exogenous influences, which mainly derive from varying environmental conditions such as the day and night or seasonal transitions during which large temperature swings can occur.
Mian, Naeem, Fletcher, Simon, S. Fletcher, Longstaff, A.P., Naeem S. Mian, Fletcher, S., Myers, Alan, Longstaff, Andrew P., Myers, A., Mian, Naeem S., A.P. Longstaff, Longstaff, Andrew, A. Myers   +12 more
core   +1 more source

Application of Machine Tool Thermal Error Compensation in Digital Twin-based System

Journal of Harbin University of Science and Technology
Thermal error significantly affects machining accuracy, demanding careful control. A robust system integrating a highly accurate thermal error model is the key to this control. This can be achieved through deep learning-based models.
MA Chi, LI Minging, LIU Jialan, HE Jialong, HUA Chunlei, WANG Liang   +5 more
doaj   +1 more source

Determination of Core Losses Using an Inverse Modeling Technique

IEEE Access, 2022
This paper presents an inverse thermal modeling technique to determine the core losses from the temperature rise inside the transformer core. For this purpose, initially, a customized printed circuit board (PCB) with thermal sensors is used to measure ...
Osaruyi Osemwinyen, Ahmed Hemeida, Floran Martin, Ismet Tuna Gurbuz, Payam Shams Ghahfarokhi, Anouar Belahcen   +5 more
doaj   +1 more source