Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

Zhang, Wei; Liu, Xurong; Huang, Zhiwen; Ji, Zhu

doi:10.1177/1687814021992181

Cited by 7 publications

(2 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The number of neurons in a hidden layer of a DNN model is determined according to equation (3), where, N H means the number of neurons in the H th hidden layer, α is a random number between 1 and 10. 40 After repeated adjustment, the number of neurons in each hidden layer is set to 11.…”

Section: The Digital Twin Dynamic Model Of the Ball Screw Feed Systemmentioning

confidence: 99%

Dynamic parameters identification of rolling joints based on the digital twin dynamic model of an assembled ball screw feed system

Zhang

Zhu

Huang

et al. 2022

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

Currently, due to the rare consideration on the coupling of the various rolling joints and their directional dynamic parameters, and the constraints of the traditional modeling methods, the dynamic modeling precision of the ball screw feed system and the dynamic parameters identification accuracy of the rolling joints are difficult to be further improved. In this paper, a novel method to identify the dynamic parameters of rolling joints based on the digital twin dynamic model of the assembled ball screw feed system is proposed. Firstly, synchronizing information of the physical entity, the geometric model is constructed. Then the finite element analysis (FEA) model is constructed which can simultaneously consider multiple rolling joints and their dynamic parameters at multiple directions. Based on the FEA modal data, the deep neural network (DNN) model is constructed to reflect the mapping between the dynamic parameters and the natural frequencies. Thus, the digital twin dynamic model can be established by fusion of these sub-models. Combining the digital twin-driven and experimental natural frequencies, the optimization model is built, and the dynamic parameters can be identified by particle swarm optimization (PSO) algorithm. Finally, the relative error of dynamic parameters identification is less than 3%, which indicates that the proposed method is feasible, effective, and has greater accuracy.

show abstract

Section: The Digital Twin Dynamic Model Of the Ball Screw Feed Systemmentioning

confidence: 99%

Dynamic parameters identification of rolling joints based on the digital twin dynamic model of an assembled ball screw feed system

Zhang

Zhu

Huang

et al. 2022

Advances in Mechanical Engineering

Self Cite

View full text Add to dashboard Cite

show abstract

“…37,38 Zhu et al 39 used DNN to establish the equivalent dynamic model of the critical joints of the ball screw feeding system, which greatly improved the identification accuracy of the dynamic characteristic parameters. Zhang et al 40 used DNN to obtain the dynamic model of sliding joints, with stiffness and damping in X , Y , and Z directions as input and natural frequencies as output and achieved a smaller identification error of <3% than previous studies. From the above literature, DNN is excellent at handling complicated data problems, which makes it suitable for constructing the dynamic model with high-dimensional parameters.…”

Section: Introductionmentioning

confidence: 99%

Dynamic modeling of sliding joints based on transversely isotropic virtual material and deep neural network

Fan,

Zhang,

et al. 2023

Advances in Mechanical Engineering

View full text Add to dashboard Cite

Aiming at the problem that the current isotropic virtual material-based modeling method for dynamic modeling of sliding joints can hardly reflect the difference between normal and tangential mechanical properties, which restricts the modeling quality, a transversely isotropic material model is introduced to comprehensively describe the mechanical properties of sliding joints. Firstly, a dynamic model based on transversely isotropic virtual material and Deep Neural Network (DNN) is constructed to reflect the relationship between the dynamic parameters of transversely isotropic virtual material [Formula: see text] and the natural frequencies. Then, using the cuckoo search algorithm, the transversely isotropic virtual material parameters are determined. Subsequently, as an application case, the flat and V-guide joints of the M7120D/H surface grinder are employed to validate the proposed modeling method. Finally, compared to the experimental modal test results, the error of natural frequencies is less than 1%, which achieves high accuracy. Additionally, the quantitative comparison based on the same application case shows that the proposed modeling method is superior to isotropic virtual material and spring damping method.

show abstract

Advancements in accuracy decline mechanisms and accuracy retention approaches of CNC machine tools: a review

Liu

Zhang

Lin

et al. 2022

Int J Adv Manuf Technol

View full text Add to dashboard Cite

Dynamic parameters identification for sliding joints of surface grinder based on deep neural network modeling

Cited by 7 publications

References 40 publications

Dynamic parameters identification of rolling joints based on the digital twin dynamic model of an assembled ball screw feed system

Dynamic parameters identification of rolling joints based on the digital twin dynamic model of an assembled ball screw feed system

Dynamic modeling of sliding joints based on transversely isotropic virtual material and deep neural network

Advancements in accuracy decline mechanisms and accuracy retention approaches of CNC machine tools: a review

Contact Info

Product

Resources

About