Modelizado y optimización de problemas biomecánicos mediante la combinación del método de los elementos finitos (mef) y técnicas avanzadas de optimización
Usually, biomechanical problems present non-linear behaviours produced by mechanical contacts, large deformations, large displacements, hyperelasticity, etc. This type of nonlinear behaviour is difficult to model and optimise by widely used methods such as the Finite Element Method (FEM). First, the...
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Universidad de La Rioja (España)
2020
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Biomechanics finite element method machine learning response surface method Biomecánica elementos finitos machine learning método superficie de respuesta |
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Biomechanics finite element method machine learning response surface method Biomecánica elementos finitos machine learning método superficie de respuesta Somovilla Gómez, Fátima Modelizado y optimización de problemas biomecánicos mediante la combinación del método de los elementos finitos (mef) y técnicas avanzadas de optimización |
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Usually, biomechanical problems present non-linear behaviours produced by mechanical contacts, large deformations, large displacements, hyperelasticity, etc. This type of nonlinear behaviour is difficult to model and optimise by widely used methods such as the Finite Element Method (FEM). First, there is a very high computational cost when only FEM is applied to model and optimise biomechanical problems. In addition, solving this type of biomechanical problems experimentally (through trial-error tests) is nowadays unfeasible for ethical reasons.
This thesis presents a methodology that combines FEM with advanced data analysis techniques, such as the Multiple Response Surface Method (MRS) or Machine Learning (ML) algorithms, to model and optimise the biomechanical problems that are present in humans and animals. The application of the proposed methodology in this thesis is carried out in three main phases. The first phase focuses on the creation of FE models, while the second phase is devoted to the generation of prediction models by means of regression techniques, decision trees, neural networks, etc. Finally, in the third phase, an optimisation is performed using either the MRS or genetic algorithms.
The proposed methodology can be applied to any biomechanical problem. In this thesis, it has been validated through its implementation in four actual problems that are found in humans and animals.
In animals, it is applied to model the biomechanical behaviour of a canine pelvis with two different types of fixation plates (ventral and DPO), used for the treatment of canine pelvic osteotomy. In this case, the FEM is solely applied to analyse and compare the stiffness difference between ventral and DPO fixation plates. In this manner, the experimental cost is reduced and the ethical problem is avoided.
In the case of human problems, the methodology that combines the FEM and the MRS with desirability functions is applied for the modelling and optimisation of the biomechanical behaviour of intervertebral discs (DIV) in lumbar functional spinal units (FSU), with the aim of obtaining the most appropriate parameters that define the biomechanical behaviour of the FE models. The advantage of the combined use of FEM and MRS, as proposed in this thesis, is that it allows adjusting and optimising the parameters that define the biomechanical behavior of FE models of complex structures in a more efficient way. Thus, avoiding the arduous adjustment of the parameters to obtain the optimal FE model through the trial-error method.
Finally, the proposed methodology is applied to the design of an artificial disc or lumbar prosthesis by combining FEM and ML techniques. In this case, the regression models generated are based on neural networks and regression trees, while the optimisation of the geometry of the artificial disc is carried out through the application of genetic algorithms. This way, it is also possible to obtain, the parameters that best define the geometry proposed for the artificial lumbar disc for different patient’s weights and statures in an efficient manner. Therefore, this methodology is considered to provide an important tool for the design and optimisation of artificial disc prostheses (custom-made prostheses).
In short, it has been proven that the methodology proposed in this thesis, which combines several techniques (FEM, MSR MRS and ML) to generate mathematical models or metamodels, is very useful to efficiently model and optimise complex biomechanical problems. The main advantages of the methodology are the following: • It significantly reduces the experimental cost and eliminates the ethical problem associated with the use of cadavers.
• It allows to obtain prediction models that are accurate enough, easy to interpret and much more computationally efficient than the models obtained through the FEM to model biomechanical problems.
• It allows to optimise complex biomechanical problems, in a more efficient way. That is, it substantially reduces the computational cost as compared to the solely use of the FEM by applying the trial-error method. |
| author2 |
González Marcos, Ana (null) |
| author_facet |
González Marcos, Ana (null) Somovilla Gómez, Fátima |
| format |
text (thesis) |
| author |
Somovilla Gómez, Fátima |
| author_sort |
Somovilla Gómez, Fátima |
| title |
Modelizado y optimización de problemas biomecánicos mediante la combinación del método de los elementos finitos (mef) y técnicas avanzadas de optimización |
| title_short |
Modelizado y optimización de problemas biomecánicos mediante la combinación del método de los elementos finitos (mef) y técnicas avanzadas de optimización |
| title_full |
Modelizado y optimización de problemas biomecánicos mediante la combinación del método de los elementos finitos (mef) y técnicas avanzadas de optimización |
| title_fullStr |
Modelizado y optimización de problemas biomecánicos mediante la combinación del método de los elementos finitos (mef) y técnicas avanzadas de optimización |
| title_full_unstemmed |
Modelizado y optimización de problemas biomecánicos mediante la combinación del método de los elementos finitos (mef) y técnicas avanzadas de optimización |
| title_sort |
modelizado y optimización de problemas biomecánicos mediante la combinación del método de los elementos finitos (mef) y técnicas avanzadas de optimización |
| publisher |
Universidad de La Rioja (España) |
| publishDate |
2020 |
| url |
https://dialnet.unirioja.es/servlet/oaites?codigo=267449 |
| work_keys_str_mv |
AT somovillagomezfatima modelizadoyoptimizaciondeproblemasbiomecanicosmediantelacombinaciondelmetododeloselementosfinitosmefytecnicasavanzadasdeoptimizacion |
| _version_ |
1718346686164631552 |
| spelling |
oai-TES00000229412020-04-22Modelizado y optimización de problemas biomecánicos mediante la combinación del método de los elementos finitos (mef) y técnicas avanzadas de optimizaciónSomovilla Gómez, FátimaBiomechanicsfinite element methodmachine learningresponse surface methodBiomecánicaelementos finitosmachine learningmétodo superficie de respuestaUsually, biomechanical problems present non-linear behaviours produced by mechanical contacts, large deformations, large displacements, hyperelasticity, etc. This type of nonlinear behaviour is difficult to model and optimise by widely used methods such as the Finite Element Method (FEM). First, there is a very high computational cost when only FEM is applied to model and optimise biomechanical problems. In addition, solving this type of biomechanical problems experimentally (through trial-error tests) is nowadays unfeasible for ethical reasons. This thesis presents a methodology that combines FEM with advanced data analysis techniques, such as the Multiple Response Surface Method (MRS) or Machine Learning (ML) algorithms, to model and optimise the biomechanical problems that are present in humans and animals. The application of the proposed methodology in this thesis is carried out in three main phases. The first phase focuses on the creation of FE models, while the second phase is devoted to the generation of prediction models by means of regression techniques, decision trees, neural networks, etc. Finally, in the third phase, an optimisation is performed using either the MRS or genetic algorithms. The proposed methodology can be applied to any biomechanical problem. In this thesis, it has been validated through its implementation in four actual problems that are found in humans and animals. In animals, it is applied to model the biomechanical behaviour of a canine pelvis with two different types of fixation plates (ventral and DPO), used for the treatment of canine pelvic osteotomy. In this case, the FEM is solely applied to analyse and compare the stiffness difference between ventral and DPO fixation plates. In this manner, the experimental cost is reduced and the ethical problem is avoided. In the case of human problems, the methodology that combines the FEM and the MRS with desirability functions is applied for the modelling and optimisation of the biomechanical behaviour of intervertebral discs (DIV) in lumbar functional spinal units (FSU), with the aim of obtaining the most appropriate parameters that define the biomechanical behaviour of the FE models. The advantage of the combined use of FEM and MRS, as proposed in this thesis, is that it allows adjusting and optimising the parameters that define the biomechanical behavior of FE models of complex structures in a more efficient way. Thus, avoiding the arduous adjustment of the parameters to obtain the optimal FE model through the trial-error method. Finally, the proposed methodology is applied to the design of an artificial disc or lumbar prosthesis by combining FEM and ML techniques. In this case, the regression models generated are based on neural networks and regression trees, while the optimisation of the geometry of the artificial disc is carried out through the application of genetic algorithms. This way, it is also possible to obtain, the parameters that best define the geometry proposed for the artificial lumbar disc for different patient’s weights and statures in an efficient manner. Therefore, this methodology is considered to provide an important tool for the design and optimisation of artificial disc prostheses (custom-made prostheses). In short, it has been proven that the methodology proposed in this thesis, which combines several techniques (FEM, MSR MRS and ML) to generate mathematical models or metamodels, is very useful to efficiently model and optimise complex biomechanical problems. The main advantages of the methodology are the following: • It significantly reduces the experimental cost and eliminates the ethical problem associated with the use of cadavers. • It allows to obtain prediction models that are accurate enough, easy to interpret and much more computationally efficient than the models obtained through the FEM to model biomechanical problems. • It allows to optimise complex biomechanical problems, in a more efficient way. That is, it substantially reduces the computational cost as compared to the solely use of the FEM by applying the trial-error method.Los problemas biomecánicos generalmente presentan comportamientos no lineales producidos por contactos mecánicos, grandes deformaciones, grandes desplazamientos, hiperelasticidad, etc. Este tipo de comportamiento no lineal es muy difícil de modelizar y optimizar mediante métodos ampliamente utilizados como es el Método de los Elementos Finitos (MEF). En primer lugar, el coste computacional que requiere el MEF cuando es aplicado de manera individual para modelizar y optimizar problemas biomecánicos es muy elevado. Además, por motivos éticos, resolver este tipo de problemas biomecánicos de modo experimental (mediante prueba-error) resulta hoy en día inviable. Esta tesis presenta una metodología que combina el MEF con técnicas avanzadas de análisis de datos como es el Método de Superficie de Respuesta Múltiple (MSR) y el Machine Learning (ML) para modelizar y optimizar problemas biomecánicos presentes en humanos y en animales. La aplicación de la metodología que se presenta en esta tesis se desarrolla en tres fases principales. La primera fase se centra en la creación de modelos de EF, en la segunda fase se obtienen los modelos de predicción mediante técnicas de regresión, árboles de decisión, redes neuronales, etc. y finalmente, en una tercera fase, se realiza la optimización utilizando la metodología de superficie de respuesta múltiple o algoritmos genéticos. La metodología propuesta puede ser aplicada a cualquier problema biomecánico, si bien en esta tesis se ha validado mediante su implementación en cuatro casos reales encontrados en seres humanos y en animales. En animales, se aplica al modelizado del comportamiento biomecánico de una pelvis canina con dos tipos diferentes de placas de fijación (ventral y DPO), utilizadas para el tratamiento de la osteotomía pélvica canina. En este caso, se aplica de manera individual el MEF con el fin de estudiar y comparar la rigidez entre las placas de fijación. De esta manera, se reduce el coste experimental y se evita el problema ético. En el caso de los seres humanos, se aplica la metodología que combina el MEF y el MSR con funciones de deseabilidad para el modelizado y optimización del comportamiento biomecánico de discos intervertebrales (DIV) en unidades vertebrales funcionales (UVF) lumbares, con el objetivo de obtener los parámetros más adecuados que definan el comportamiento biomecánico de los modelos de EF. La ventaja del uso combinado del MEF y MSR, tal como se propone en esta tesis, es que permite ajustar y optimizar los parámetros que definen el comportamiento biomecánico de los modelos de EF de estructuras complejas de un modo más eficiente, evitando así, el arduo ajuste de los parámetros para obtener el modelo de EF óptimo mediante el método prueba-error. Finalmente, la metodología propuesta se aplica en el diseño de un disco artificial o prótesis lumbar mediante la combinación de MEF y técnicas de ML. En este caso, los modelos de regresión generados se basan en redes neuronales y árboles de regresión, mientras que la optimización de la geometría del disco artificial se realiza mediante la aplicación de algoritmos genéticos. De este modo, también es posible obtener, de una manera eficiente, los parámetros que mejor definen la geometría planteada para el disco artificial lumbar para los diferentes pesos y estaturas de los pacientes, con lo que se considera que proporciona una herramienta importante para el diseño y la optimización de prótesis de disco artificial (diseño de prótesis a medida). En definitiva, la metodología que se propone en esta tesis, la cual combina varias técnicas (MEF, MSR y ML) que generan modelos matemáticos o metamodelos, se muestra como una metodología muy valiosa que permite de una manera eficiente modelizar y optimizar problemas biomecánicos complejos. Las ventajas fundamentales de esta metodología, son las siguientes: • Reduce significativamente el coste experimental y se elimina el problema ético asociado al uso de cadáveres. • Permite obtener modelos de predicción lo suficientemente precisos, fáciles de interpretar y mucho más eficientes computacionalmente que los modelos obtenidos mediante el MEF para el modelizado de problemas biomecánicos. • Permite optimizar problemas biomecánicos complejos, de una manera más eficiente, reduciendo de forma significativa el coste computacional que ocasionaría el uso exclusivo del MEF aplicando el método prueba-error.Universidad de La Rioja (España)González Marcos, Ana (null)Corral Bobadilla, Marina (null)Lostado Lorza, Ruben (null)2020text (thesis)application/pdfhttps://dialnet.unirioja.es/servlet/oaites?codigo=267449spaLICENCIA DE USO: Los documentos a texto completo incluidos en Dialnet son de acceso libre y propiedad de sus autores y/o editores. 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