Rubén Lostado-Lorza

Combination of the Finite Element Method and Data Mining Techniques to Design and Optimize Bearings

Double-Row Tapered Roller Bearings are mechanical systems widely used in vehicles for the transmission of high load and moderate rotation speeds.

Design and Optimization of Welded Products Using Genetic Algorithms, Model Trees and the Finite Element Method

One of the fundamental requirements in the phases of design and manufacture of any welded product is the reduction of residual stresses and strains. These stresses and strains can cause substantial changes in the geometry of the finished products which often require subsequent machining in order to fit to the dimensions specified by the customer, and are usually caused by the contribution of an external heat flux in a small area. All welded joints contain welding seams with more or less regular geometry. This geometry gives the welded product the strength and quality required to support the mechanical demands of the design, and is affected by the parameters controlling the welding process (speed, voltage and current). Some researchers have developed mathematical models for predicting geometry based on the height, width and cord penetration, but is a difficult task as many of the parameters affecting the quality and geometry of the cord are unknown. As the welded product becomes more and more complex, residual stresses and strains are more difficult to obtain and predict as they depend greatly on the sequence followed to manufacture the product. Over several decades, the Finite Element Method (FEM) has been used as a tool for the design and optimization of mechanical components despite requiring validation with experimental data and high computational cost, and for this reason, the models based on FEM are currently not efficient. One of the potential methodologies used for adjusting the Finite Element models (FE models) is Genetic Algorithms (GA). Likewise, Data Mining techniques have the potential to provide more accurate and more efficient models than those obtained by FEM alone. One of the more common Data Mining techniques is Model Trees (MT). This paper shows the combination of FEM, GA and MT for the design and optimization of complex welded products.

Improvement in Manufacturing Welded Products through Multiple Response Surface Methodology and Data Mining Techniques

Gas Metal Arc Welding (GMAW) is an industrial process commonly used in manufacturing welded products. This manufacturing process is normally done by an industrial robot, which is controlled through the parameters of speed, current and voltage. These control parameters strongly influence the residual stress and the strength of the welded joint, as well as the total cost of manufacturing the welded components. Residual stress and tensile strength are commonly obtained via standardized hole-drilling and tensile tests which are very expensive to routinely carry out during the mass production of welded products. Over the past few decades, researchers have concentrated on improving the quality of manufacturing welded products using experimental analysis or trial-and-error results, but the cost of this methodology has proved unacceptable. Likewise, regression models based on Data Mining (DM) techniques have been used to improve various manufacturing processes, but usually require a relatively large amount of data in order to obtain acceptable results. By contrast, multiple response surface (MRS) methodology is a method for modelling and optimizing, which aims to identify the combination of input parameters that give the best output responses with a reduced number of data sets. In this paper, power consumption, cord area, tensile strength and tensile stress were modelled with quadratic regression (QR) models using Response Surface Methodology (RSM) and were compared with regression models based on DM (linear regression (LR), isotonic regression (IR), Gaussian processes (GP), artificial neural networks (ANN), support vector machines (SVM) and regression trees (RT)). The optimization of the parameters was conducted using RSM with quadratic regression and desirability functions, and was achieved when the residual stresses and power consumption were as low as possible, while strength and process speed were as high as possible.

Using the Finite Element Method to Determine the Influence of Age, Height and Weight on the Vertebrae and Ligaments of the Human Spine

This study uses the Finite Element Method (FEM) to analyze the influence of age, height and weight on the vertebrae and ligaments of the human functional spinal unit (FSU). Two different artificial segments and the influence of the patient’s age, sex and height were considered. The FSU analyzed herein was based on standard human dimensions. It was fully parameterized first in engineering modelling format using CATIA© software. A combination of different elements (FE) were developed with Abaqus© software to model a healthy human FSU and the two different sizes of artificial segments. Healthy and artificial FSU Finite Element models (FE models) were subjected to compressive loads of differing values. Spinal compression forces, posture data and male/female anthropometry were obtained using 3DSSPP© software Heights ranging from 1.70 to 1.90 meters; ages, between 30 and 80 years and body weights between 75 and 90 kg were considered for both men and women. Artificial models were based on the Charite prosthesis. The artificial prosthesis consisted of two titanium alloy endplates and an ultra-high-molecular-weight polyethylene (UHMWPE) core. An analysis in which the contacts between the vertebrae and the intervertebral disc, as well as the behavior of the seven ligaments, were taken into consideration. The Von Mises stresses for both the cortical and trabecular bone of the upper and lower vertebrae, and the longitudinal stresses corresponding to the seven ligaments that connect the FSU were analyzed. The stresses obtained for the two geometries that were studied by means of the artificial FE models were very similar to the stresses that were obtained from healthy FE models.

The design of a knee prosthesis by Finite Element Analysis

The purpose of this paper is to study two types of knee prosthesis that are based on the Finite Element Method (FEM). The process to generate the Finite Element (FE) models was conducted in several steps. A 3D geometric model of a healthy knee joint was created using 3D scanned data from an anatomical knee model. This healthy model comprises a portion of the long bones (femur, tibia and fibula) as well as by the lateral and medial meniscus, cartilage and ligaments. The digital model that was obtained was repaired and converted to an engineering drawing format using CATIA© software. Based on the foregoing format, two types of artificial knee prostheses were designed and assembled. Mentat Marc© software was used to model the healthy and artificial knee FE models. The healthy and artificial knee FE models were subjected to different loads. The an-thropometry of the human body that was studied and the combination of loads to apply to the knee were obtained by use of 3D Static Strength Prediction software (3DSSPP©). The Von Mises stresses, as well as all the relative displacements of the components of the healthy and artificial knee FE model, were obtained from the Mentat Marc© software. The Von Mises stresses for both the cortical and the trabecular bone of the artificial and healthy knee FE model were analyzed and compared. The stresses that were obtained from the two knee prosthesis that were studied based on the artificial FE models were very similar to those stresses that were obtained from healthy FE models.

Characterization of a Composite Material Reinforced with Vulcanized Rubber

The paper is intended to propose a method to characterize the adhesion of a thermoplastic matrix composite material that is reinforced with continuous fibers and over-injected vulcanized rubber. The behaviour of the material based on the thermoplastic matrix and the adhesive is studied. In addition, the combination of factors that provides the greatest possible adhesion of the rubber to the composite is analyzed. Test methods are also analysed and suggested to characterize the adhesion force of the vulcanized rubber to the thermoplastic composite.

Comparison Analysis of Regression Models Based on Experimental and FEM Simulation Datasets Used to Characterize Electrolytic Tinplate Materials

Currently, processes to characterize materials are mainly based on two methodologies: a good design of experiments and models based on finite element simulations. In this paper, in order to obtain advantages and disadvantages of both techniques, a prediction of mechanical properties of electrolytic tinplate is made from the data obtained in both methodologies. The predictions, and therefore, the comparative analysis are performed using various machine learning techniques: linear regression, artificial neural networks, support vector machines and regression trees. Data from both methodologies are used to develop models that subsequently are tested with their own method data and with data obtained from mechanical tests. The obtained results show that models based on design of experiments are more accurate, but the models based on finite element simulations better define the problem space.

Modeling Structural Elements Subjected to Buckling Using Data Mining and the Finite Element Method

Buckling of thin walled welded structures is one of the most common failure modes experienced by these structures in-service. The study of such buckling, to date, has been concentrated on experimental tests, empirical models and the use of numerical methods such as the Finite Element Method (FEM). Some researchers have combined the FEM with Artificial Neural Networks (ANN) to study both open and closed section structures but these studies have not considered imperfections such as holes, weld seams and residual stresses. In this paper, we have used a combination of FEM and ANN to obtain predictive models for the critical buckling load and lateral displacement of the center of the profile under compressive loading. The study was focused on ordinary Rectangular Hollow Sections (RHS) and on the influence of geometric imperfections while taking residual stresses into consideration.

Genetic Algorithms Combined with the Finite Elements Method as an Efficient Methodology for the Design of Tapered Roller Bearings

This research presents an efficient hybrid approach based on soft computing techniques and the finite element method for the design of mechanical systems. The use of non-linear finite element models to design mechanical systems provides solutions that are consistent with experimental results; but this use is often limited in practice by a high computational cost. In order to reduce this cost, we propose a linear finite element model that replaces the non-linear elements of the mechanical system with beam and plate elements of equivalent stiffness that are adjusted by means of genetic algorithms. Thus, the adjusted linear model behaves in the same way as the non-linear model, but with a much lower computational cost, which would allow to redesign any mechanical system in a more efficient and faster way. A case study demonstrates the validity of this methodology as applied to the design of a tapered roller bearing.

Improving Steel Industrial Processes Using Genetic Algorithms and Finite Element Method

Steel industrial engineers must estimate optimal operational parameters of industrial processes and the correct model for complex material behaviour. Common practice has been to base these determinations on classic techniques, such as tables and theoretical calculations. In this paper three successful experiences combining finite element modelling with genetic algorithms are reported. On the one hand, two cases of improvement in steel industrial processes are explained; on the other hand, the efficient determination of realistic material behaviour laws is presented. The proposed methodology optimizes and fully automates these determinations. The reliability and effectiveness of combining genetic algorithms and the finite element method is demonstrated in all cases.