Biomaterial Database

Virtual product development process for reducing noise, vibration, and harshness of vehicle based on substructuring and artificial neural network. 2022

Yongdae Kim, and Un-Chang Jeong

Hyundai Motors Company, 165-24, Hyundaiyeonguso-ro, Namyang-eup, Hwaseong-si, Gyeonggi-do, Republic of Korea.

In this study, virtual product development method for reducing vibration and noise is proposed for designing at the concept development stage of a vehicle. To this end, the vibration characteristics of the system are predicted through the Lagrange-multiplier frequency-based substructuring technique. The concepts of contact, blocked and transmitted force, and force transmissibility were used for determining the improvement subsystem or combination of subsystems when using the modular platform. Moreover, after the subsystems to be improved were determined, Artificial Neural Network was used as a method of predicting vibration characteristics according to the change of design variables. To verify this, the prediction of the blocked force was performed by changing the young's modulus of the simplified substructure. Finally, the reduction in response was confirmed by applying the blocked force of the simplified subframe to the simplified structure, and a vehicle development process using a database at the concept setting stage is proposed.

UI	MeSH Term	Description	Entries
D009622	Noise	Any sound which is unwanted or interferes with HEARING other sounds.	Noise Pollution,Noises,Pollution, Noise
D014732	Vibration	A continuing periodic change in displacement with respect to a fixed reference. (McGraw-Hill Dictionary of Scientific and Technical Terms, 6th ed)	Vibrations
D016571	Neural Networks, Computer	A computer architecture, implementable in either hardware or software, modeled after biological neural networks. Like the biological system in which the processing capability is a result of the interconnection strengths between arrays of nonlinear processing nodes, computerized neural networks, often called perceptrons or multilayer connectionist models, consist of neuron-like units. A homogeneous group of units makes up a layer. These networks are good at pattern recognition. They are adaptive, performing tasks by example, and thus are better for decision-making than are linear learning machines or cluster analysis. They do not require explicit programming.	Computational Neural Networks,Connectionist Models,Models, Neural Network,Neural Network Models,Neural Networks (Computer),Perceptrons,Computational Neural Network,Computer Neural Network,Computer Neural Networks,Connectionist Model,Model, Connectionist,Model, Neural Network,Models, Connectionist,Network Model, Neural,Network Models, Neural,Network, Computational Neural,Network, Computer Neural,Network, Neural (Computer),Networks, Computational Neural,Networks, Computer Neural,Networks, Neural (Computer),Neural Network (Computer),Neural Network Model,Neural Network, Computational,Neural Network, Computer,Neural Networks, Computational,Perceptron
D055119	Elastic Modulus	Numerical expression indicating the measure of stiffness in a material. It is defined by the ratio of stress in a unit area of substance to the resulting deformation (strain). This allows the behavior of a material under load (such as bone) to be calculated.	Young Modulus,Modulus of Elasticity,Young's Modulus,Elasticity Modulus,Modulus, Elastic,Modulus, Young,Modulus, Young's,Youngs Modulus

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