A study on formulation of objective functions for determining material models

Cao, Jian; Lin, Jianguo

doi:10.1016/j.ijmecsci.2007.07.003

Cited by 109 publications

(44 citation statements)

References 17 publications

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“…Therefore, it is convenient to normalize these distances. The concept of calculation of the correspondent computed-experimental point is called weighted length, in opposition to the weighted distance proposed in [1]. This concept takes into account negative values and curve length equidistant values.…”

Section: Novel Criterionmentioning

confidence: 99%

“…Recently, the mentioned criteria were satisfied by Lin et al [1,2,3] with the introduction of new objective functions formulations and automatic weighting factors. Similar to the true strain definition, the concept of true (logarithm) error definition was also used in order to increase the function sensitivity to the parameters.…”

Section: Introductionmentioning

confidence: 99%

“…This tries to ensure that all the experimental and computed data are involved in the optimisation and the predicted data will be pushed towards the corresponding experimental data. The objective function formulation suggested by Lin and coworkers [1,2] is…”

Section: Introductionmentioning

confidence: 99%

“…The weighting factor for the ith experimental data point of the jth curve developed by Cao and Lin [1] is expressed as…”

Section: Introductionmentioning

confidence: 99%

“…However, the success of this task is dependent of the specification of the cost/objective function, defined as the difference between the experimental and the numerical results. Recently, various objective functions have been formulated to assess the errors between the experimental and computed data Cao and Lin, 2008; among others). The objective functions should be able to efficiently lead the optimisation process.…”

mentioning

confidence: 99%

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A novel criterion for determination of material model parameters

Andrade‐Campos¹,

de-Carvalho

Valente³

2011

AIP Conference Proceedings

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Metal-organic frameworks as promising candidates for future ultralow-k dielectrics Appl. Phys. Lett. 97, 251905 (2010) Synthesis and evaluation of magnetic active charcoals for removal of environmental endocrine disrupter and heavy metal ion J. Appl. Phys. 101, 09J114 (2007) Aging and free surface flow of a thixotropic fluid Phys. Fluids 17, 033101 (2005) Optical properties of heterodiamond B2CN using first-principles calculations Appl. Phys. Lett. 84, 4544 (2004) Optimal design of manufacturable three-dimensional composites with multifunctional characteristics J. Appl. Phys. 94, 5748 (2003) Additional information on AIP Conf. Proc. Santiago, Portugal, Tel: +351.234.370830; Fax: +351.234.370933; Abstract. Parameter identification problems have emerged due to the increasing demanding of precision in the numerical results obtained by Finite Element Method (FEM) software. High result precision can only be obtained with confident input data and robust numerical techniques. The determination of parameters should always be performed confronting numerical and experimental results leading to the minimum difference between them. However, the success of this task is dependent of the specification of the cost/objective function, defined as the difference between the experimental and the numerical results. Recently, various objective functions have been formulated to assess the errors between the experimental and computed data Cao and Lin, 2008; among others). The objective functions should be able to efficiently lead the optimisation process. An ideal objective function should have the following properties: (i) all the experimental data points on the curve and all experimental curves should have equal opportunity to be optimised; and (ii) different units and/or the number of curves in each sub-objective should not affect the overall performance of the fitting. These two criteria should be achieved without manually choosing the weighting factors. However, for some non-analytical specific problems, this is very difficult in practice. Null values of experimental or numerical values also turns the task difficult. In this work, a novel objective function for constitutive model parameter identification is presented. It is a generalization of the work of Cao and Lin and it is suitable for all kinds of constitutive models and mechanical tests, including cyclic tests and Baushinger tests with null values.

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Section: Novel Criterionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…The weighting factor for the ith experimental data point of the jth curve developed by Cao and Lin [1] is expressed as…”

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%