Fátima Somovilla Gómez

Improving the Process of Adjusting the Parameters of Finite Element Models of Healthy Human Intervertebral Discs by the Multi-Response Surface Method

The kinematic behavior of models that are based on the finite element method (FEM) for modeling the human body depends greatly on an accurate estimate of the parameters that define such models. This task is complex, and any small difference between the actual biomaterial model and the simulation model based on FEM can be amplified enormously in the presence of nonlinearities. The current paper attempts to demonstrate how a combination of the FEM and the MRS methods with desirability functions can be used to obtain the material parameters that are most appropriate for use in defining the behavior of Finite Element (FE) models of the healthy human lumbar intervertebral disc (IVD). The FE model parameters were adjusted on the basis of experimental data from selected standard tests (compression, flexion, extension, shear, lateral bending, and torsion) and were developed as follows: First, three-dimensional parameterized FE models were generated on the basis of the mentioned standard tests. Then, 11 parameters were selected to define the proposed parameterized FE models. For each of the standard tests, regression models were generated using MRS to model the six stiffness and nine bulges of the healthy IVD models that were created by changing the parameters of the FE models. The optimal combination of the 11 parameters was based on three different adjustment criteria. The latter, in turn, were based on the combination of stiffness and bulges that were obtained from the standard test FE simulations. The first adjustment criteria considered stiffness and bulges to be equally important in the adjustment of FE model parameters. The second adjustment criteria considered stiffness as most important, whereas the third considered the bulges to be most important. The proposed adjustment methods were applied to a medium-sized human IVD that corresponded to the L3-L4 lumbar level with standard dimensions of width = 50 mm, depth = 35 mm, and height = 10 mm. Agreement between the kinematic behavior that was obtained with the optimized parameters and that obtained from the literature demonstrated that the proposed method is a powerful tool with which to adjust healthy IVD FE models when there are many parameters, stiffnesses, and bulges to which the models must adjust.

A Proposed Methodology for Setting the Finite Element Models Based on Healthy Human Intervertebral Lumbar Discs

The human intervertebral lumbar disc is a fibrocartilage structure that is located between the vertebrae of the spine. This structure consists of a nucleus pulposus, the annulus fibrosus and the cartilage endplate. The disc may be subjected to a complex combination of loads. The study of its mechanical properties and movement are used to evaluate the medical devices and implants. Some researchers have used the Finite Element Method (FEM) to model the disc and to study its biomechanics. Estimating the parameters to correctly define these models has the drawback that any small differences between the actual material and the simulation model based on FEM can be amplified enormously in the presence of nonlinearities. This paper sets out a fully automated method to determine the most appropriate material parameters to define the behavior of the human intervertebral lumbar disc models based on FEM. The methodology that is proposed is based on experimental data and the combined use of data mining techniques, Genetic Algorithms (GA) and the FEM. Firstly, based on standard tests (compression, axial rotation, shear, flexion, extension and lateral bending), three-dimensional parameterized Finite Element (FE) models were generated. Then, considering the parameters that define the proposed parameterized FE models, a Design of Experiment (DoE) was completed. For each of the standard tests, a regression technique based on Support Vector Machines (SVM) with different kernels was applied to model the stiffness and bulges of the intervertebral lumbar disc when the parameters of the FE models are changed. Finally, the best combination of parameters was achieved by applying evolutionary optimization techniques that are based on GA to the best, previously obtained regression models.

A Soft Computing Approach to Optimize the Production of Biodiesel

There is an increasing global concern for environmental protection for the conservation of non-renewal natural resources. It needs to be obtain an alternative, renewable and biodegradable combustible like biodiesel. Waste cooking oil is a potential replacement for vegetable oils in the production of biodiesel. Biodiesel is synthesized by direct transesterification of vegetable oils, which is controlled by several inputs or process variables, including the dosage of catalyst, process temperature, mixing speed, mixing time, humidity and impurities of waste cooking oil. This study proposes a methodology to improve the production of biodiesel based on the use of soft computing techniques to predict several features of biodiesel production. The method selected a group of regression models based on Support Vector Machines (SVM) techniques to perform a prediction of several properties of a biodiesel sample taking into account a configuration of 7 test inputs. This test inputs were: molar ratio, dosage of catalyst, temperature, mixing speed, mixing time, humidity and impurities. Then and based on these inputs, the features to predict were: yield, turbidity, density, viscosity and high heating to obtain a better understanding of the process. Finally, considering the samples of the design of experiments studied, it has been observed that SVM models, based on a radial basic function kernel, record accurate results, with the best performance in four of the five features, improving in all the cases the accuracy obtained using linear regression.

Adjust the Thermo-Mechanical Properties of Finite Element Models Welded Joints Based on Soft Computing Techniques

An appropriate characterization of the thermo-mechanical behavior of elastic-plastic Finite Element (FE) models is essential to ensure realistic results when welded joints are studied. The welded joints are subject to severe angular distortion produced by an intense heat concentration on a very small area when they are manufactured. For this reason, the angular distortion and the temperature field, which the joints are subjected, is very difficult to model with the Finite Element Method (FEM) when nonlinear effects such as plasticity of the material, radiation and thermal contacts are considered. This paper sets out a methodology to determine the most appropriate parameters needed for modelling the thermo-mechanical behavior in welded joints FE models. The work is based on experimental data (temperature field and angular distortion) and the combined use of Support Vector Machines (SVM) and Genetic Algorithms (GA) with multi-objective functions. The proposed methodology is applied for modelling Butt joint with single V-groove weld manufactured by Gas Metal Arc Welding (GMAW) process when the parameters of speed, current and voltage are, respectively, 6 mm/sec 140 amps and 26 V.

Improvement in the Process of Designing a New Artificial Human Intervertebral Lumbar Disc Combining Soft Computing Techniques and the Finite Element Method

Human intervertebral lumbar disc degeneration is painful and difficult to treat, and is often magnified when the patient is overweight. When the damage is excessive, the disc is replaced by a non-natural or artificial disc. Artificial discs sometimes have the disadvantage of totally different behavior from that of the natural disc. This affects substantially the quality of treated patient’s life. The Finite Element Method (FEM) has been used for years to design an artificial disc, but it involves a high computational cost. This paper proposes a methodology to design a new Artificial Human Intervertebral Lumbar Disc by combining FEM and soft computing techniques. Firstly, a three-dimensional Finite Element (FE) model of a healthy disc was generated and validated experimentally from cadavers by standard tests. Then, an Artificial Human Intervertebral Lumbar Disc FE model with a core of Polycarbonate Polyurethane (PCU) was modeled and parameterized. The healthy and artificial disc FE models were both assembled between lumbar vertebrae L4-L5, giving place to the Functional Spinal Unit (FSU). A Box-Behnken Design of Experiment (DoE) was generated that considers the parameters that define the geometry of the proposed artificial disc FE model and the load derived from the patient’s height and body weight. Artificial Neural Networks (ANNs) and regression trees that are based on heuristic methods and evolutionary algorithms were used for modeling the compression and lateral bending stiffness from the FE simulations of the artificial disc. In this case, ANNs proved to be the models that had the best generalization ability. Finally, the best geometry of the artificial disc proposed when the patient’s height and body weight were considered was achieved by applying Genetic Algorithms (GA) to the ANNs. The difference between the compression and lateral bending stiffness obtained from the healthy and artificial discs did not differ significantly. This indicated that the proposed methodology provides a powerful tool for the design and optimization of an artificial prosthesis.

Cyclone Performance Prediction Using Linear Regression Techniques

A wide range of industrial fields utilize cyclone separators and so, evaluating their performance according to different materials and varying operating conditions could contribute useful information and could also save these industries significant amounts of capital. This study models cyclone performance using linear regression techniques and low errors were obtained in comparison with the values obtained from real experiments. Linear regression and generalized linear regression techniques, simple and enhanced with Gradient Boosting techniques, were used to create linear models with low errors of approximately 0.83 % in cyclone performance.

A Soft Computing Approach to Optimize the Clarification Process in Wastewater Treatment

The coagulation process allows for the removal of colloidal particles suspended in wastewater. Estimating the amount of coagulant required to effectively remove these colloidal particles is usually determined experimentally by the jar test. The configuration of this test is often performed in an iterative manner which has the disadvantage of requiring a significant period of experimentation and an excessive amount of coagulant consumption. This study proposes a methodology to determine the optimum natural coagulant dose while at the same time eliminating the maximum amount of colloidal particles suspended in the wastewater. An estimation of the amount of colloidal particles removed from the wastewater is determined by the turbidity in a standardized jar test, which is applied to the wastewater at the wastewater treatment plant in Logrono (Spain). The methodology proposed is based on the combined use of soft computing techniques and evolutionary techniques based on Genetic Algorithms (GA). Firstly, a group of regression models based on neural networks techniques was performed to predict the final turbidity of a wastewater sample taking into consideration a configuration of jar test inputs. The jar test inputs are: initial turbidity, natural coagulant dosage, temperature, mix speed and mix time. Finally, the best combination of jar test inputs to obtain the optimum natural coagulant dose, while also eliminating the maximum amount of colloidal particles, was achieved by applying evolutionary optimization techniques to the most accurate regression models obtained beforehand.

Design of a Device to Eliminate Isocyanuric Acid from Water

This paper shows the design of a device for partial eliminating of isocyanuric acid (ICN) from swimming pool water using melamine additives. The renewal process of swimming pool water through its own purification makes absolutely necessary the elimination of isocyanuric acid that has been accumulated in the water over time. An excess of isocyanuric acid in water will then prevent chlorine effectiveness in the pool water and as a result, becomes harmful to human health. Therefore, the disinfection stage is considered as well as Isocyanuric acid (ICN) stabilization and as doing this is achieved through melamine-photometry filtering of insoluble complex ICN-M. The overall objective of these stages of purification is to eventually eliminate ICN from swimming pool. The overall objective of this device is to eventually eliminate ICN from swimming pool and then make it safe for human uses, a case that has been considered viable technologically and economically in the system treatment.

Study of Different Biomaterials for Artificial Lumbar Disc Prosthesis Using FEM

The purpose of this study was to analyze the behavior of a lumbar spine disc prosthesis with different materials. The study was performed at L4-L5 lumbar motion segment using the finite element method (FEM). A healthy Finite Element (FE) model was used as a reference with which to compare the results of the FE simulations of the artificial discs. The healthy and the artificial FE models were subjected to a combination of 0.5 MPa Compression pre-load and 10Nm of Flexion moment. The artificial FE models were based on Maverick artificial disc, and the three materials proposed for study the artificial disk were Titanium, Ceramic and CrCoMo alloy. The most suitable material for developed the artificial disc was the CoCrMo alloy due to: The von Mises stresses on the bone with which this artificial disc was in contact were reduced as much as possible and also, were very similar to the von Mises stresses obtained in the bones from the healthy disc.

Design of an Electromagnetic Servo Brake with ABS Function

The focus of this paper is to show a novel electromagnetic servo brake with ABS function. This electro-mechanical device is designed to be installed in mid-range commercial vehicles, and its operation is performed by the battery of the vehicle itself. A control system based on a linear controller was designed to regulate the electric current used by the servo brake. The device designed was constructed and experimentally validated using a test bench. The good agreement obtained from the experiments suggests that the servo brake with ABS function designed could be used in mid-range commercial vehicles in order to reduce the speed of the wheels while simultaneously preventing the wheels from locking safely.