Modelizado y optimización de problemas mecánicos no lineales mediante el Método de Elementos Finitos, RSM y ML

Elastomer materials have a non-linear mechanical behaviour, and can also admit large deformations. In order to correctly model these materials, it is not enough to obtain an elastic modulus and their elastic limit and rupture stresses. These materials require a more complex characterization, which must be carried out through a series of tests followed by a process of adjustment of different strain energy functions. The biggest drawback in this type of procedure is the impossibility of explicitly expressing force values in the energy functions. Therefore, the only possible way to carry out the characterization is the use of extensometers to collect the strain values in order to determine the stress-strain curves. The subsequent adjustment method consists of an iterative procedure that tries to reduce the existing error between the curves obtained in the tests and those generated by means of certain input parameters. The difficulty that this implies, added to the cost of the necessary equipment, causes the characterization process to increase, even more, the complexity in its execution. This thesis presents an elastomer characterization methodology based on the Finite Element Method (FEM), Machine Learning (ML) and advanced optimization techniques. By means of this procedure it is possible to characterize elastomeric materials in a practically immediate way by means of their force-displacement curves. These curves are easily obtainable by means of a universal machine without the need to add any extensometry equipment. Ten elastomeric materials of the NBR (Acrylonitrile Butadiene Rubber), SBR (Styrene-Butadiene Rubber), EVA (Ethylene-Vinyl-Acetate) and PU (Polyurethane) types have been tested to validate the proposed methodology. Each of the tests carried out on the materials was replicated using a finite element model. These models were used as a source of information from which a force-displacement behavior database was extracted. Different input parameters were used in four different energy functions (Mooney-Rivlin, Arruda-Boyce, Gent and Ogden). The last step consisted in the application of optimization techniques such as the Multiple Response Surface Method (MSR) and the Genetic Algorithm (GA) method to obtain the optimal parameters in each of the cases. The proposed methodology was subsequently validated through a sixth test, known as the bulge test. In this case, a new NBR-type material was characterized and tested, replicating the process of the test carried out using FEM. The obtained results revealed the validity of the developed methodology, having managed to obtain a good approximation compared to the real test. Alternatively, after the review of these results, the use of the bulging test was proposed as a substitute method for the equibiaxiality or planar stress tests in characterization tests set. Once established and validated, the methodology was applied to three non-linear problems present in the footwear sector. The first problem was the simulation and the determination of the critical break zones of a forming membrane used in production machinery. These types of membranes are used to press footwear components against a last, in a cyclical inflation process, which ends up triggering the component breakage. The simulation of the problem is based on the characterization of the materials by means of the previously defined method and later the development of an adequate model that allows determining the affected areas. The second problem consists on the application of the methodology to 3D printed elastomers, with the final objective of its use in the manufacture of personalized orthoses. Three types of material with variable Shore hardness were used, which were also 3D printed with different fill pattern configurations. Assuming that the deformations are controlled, the adjustment methodology allows both the characterization of a totally solid material, as well as that of a material that presents a concrete internal structure. This fact allows the FEM models used in these cases to be simplified, making it possible to use them, as in this case, in the simulation of plantar orthoses. The combination of the adjustment methodology together with anatomical models personalized by patient obtained from Computed Axial Tomography (CT) is proposed. This tool can help both to significantly improve podiatric diagnoses (it is possible to study the stresses inside the body) and to improve the design of corrective solutions, by being able to validate the way in which they influence the foot without the presence of the patient. Finally, the third problem consists of the study of the safety footwear soles slip test. This test is one of the biggest problems to overcome in the design process of a new sole. Several times, the manufactured sole molds must be discarded as the components do not pass this test. In order to avoid this problem, the adjustment methodology was applied to the characterization of the materials used in a sole. In addition to that, the slippage of the material was studied against different load states. A global FEM sole model that includes all this information is capable of accurately determine the coefficient of friction that the real component will have. By doing this, there is no need of obtaining a physical prototype, greatly serving to speed up design times and avoid unnecessary costs in the process.

Grantor dc:publisher

Universidad de La Rioja (España)

Year dc:date

2023

Author dc:creator

• Íñiguez Macedo, Saúl

Contributors dc:contributor

• González Marcos, Ana (Universidad de La Rioja)
• Lostado Lorza, Rubén (Universidad de La Rioja)

• Elastómeros
• caracterización
• método de los elementos finitos
• machine learning
• algoritmos genéticos
• método superficie de respuesta
• Elastomers
• characterization
• finite element method
• genetic algorithms
• response surface method

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Language dc:language

spa

Repository record dc:identifier

https://dialnet.unirioja.es/servlet/oaites?codigo=319833

OAI identifier oai:identifier

oai:dialnet.unirioja.es:TES0000023106