Andrej Škrlec and Jernej Klemenc
In conditions where a product is subjected to extreme mechanical loading in a very short time, a strain rate has a significant influence on the behaviour of the product’s…
Abstract
Purpose
In conditions where a product is subjected to extreme mechanical loading in a very short time, a strain rate has a significant influence on the behaviour of the product’s material. To accurately simulate the behaviour of the material during these loading conditions, the strain rate parameters of the selected material model should be appropriately used. This paper aims to present a fast method with which the proper strain-rate-dependent parameter values of the selected material model can be easily determined.
Design/methodology/approach
In the paper, an experiment was designed to study the behaviour of thin, flat, metal sheets during an impact. The results from this experiment were the basis for the determination of the strain-rate-dependent parameter values of the Cowper–Symonds material model. Optimisation processes with different numbers of required parameters of the selected material model were performed. The optimisation process consists of the method for design of experiment, modelling a response surface and a genetic algorithm.
Findings
The paper provides comparison of two optimisation processes with different methods for design of experiment. The performances of the presented method are compared and the engineering applicability of the results is discussed.
Originality/value
This paper presents a new fast approach for the identification of the parameter values of the Cowper–Symonds material model, if these cannot be easily determined directly from experimental data.
Details
Keywords
Jernej Klemenc and Matija Fajdiga
One of the biggest problems in an R&D process is the acquisition of information about the structure dynamic loads, which are needed to reliably prove the structure's durability…
Abstract
Purpose
One of the biggest problems in an R&D process is the acquisition of information about the structure dynamic loads, which are needed to reliably prove the structure's durability. This paper aims to present an innovative method for simulating stationary Gaussian random processes, which is based on the conditional probability density function (PDF) approach.
Design/methodology/approach
The basic information on the structure dynamic loads is first obtained by short‐duration measurements on prototypes or the structure itself. These data are then used to simulate the expected structure load states during operations. A theoretical background is presented first, which is followed by the application of the method.
Findings
The results show that the spectral characteristics of the original and simulated Gaussian random processes are very similar, if the influential range of the conditional PDF is properly chosen.
Practical implications
The method can be applied for simulating random loads of structures, and excitations of dynamic systems, for example.
Originality/value
The innovative simulation approach could be helpful to engineers in the early phases of the new product development process.
Details
Keywords
Andrej Škrlec, Jernej Klemenc and Matija Fajdiga
In the event of a crash involving a car, its seats, together with their backrests and head supports, ensure the safety of the passengers. The filling material used for such a car…
Abstract
Purpose
In the event of a crash involving a car, its seats, together with their backrests and head supports, ensure the safety of the passengers. The filling material used for such a car seat is normally made of polyurethane foam. To simulate the behaviour of the seat assembly during a crash, the material characteristics of the seat-filling foam should be appropriately modelled. The purpose of this paper is to present a method, with which the proper parameter values of the selected material model for the seat-filling foam can be easily determined.
Design/methodology/approach
In the study, an experiment with the specimen from seat-filling foam was carried out. The results from this experiment were the basis for the determination of the parameter values of the low-density-foam material model, which is often used in crash-test simulations. Two different numerical optimisation algorithms – a genetic algorithm and a gradient-descent algorithm – were coupled with LS-DYNA explicit simulations to identify the material parameters.
Findings
The paper provides comparison of two optimisation algorithms and discusses the engineering applicability of the results.
Originality/value
This paper presents an approach for the identification of the missing parameter values of the highly non-linear material model, if these cannot be easily determined directly from experimental data.