Lidia Sánchez-González

Coupling the PAELLA Algorithm to Predictive Models.

This paper explores the benefit of using the PAELLA algorithm in an innovative way. The PAELLA algorithm was originally developed in the context of outlier detection and data cleaning. As a consequence, it is usually seen as a discriminant tool that categorizes observations into two groups: core observations and outliers. A new look at the information contained in its output provides ample opportunity in the context of data driven predictive models. The information contained in the occurrence vector is used through the experiments reported in a quest for finding how to take advantage of that information. The results obtained in each successive experiment guide the researcher to a sensible use case in which this information proves extremely useful: probabilistic sampling regression.

The Boundary Element Method Applied to the Resolution of Problems in Strength of Materials and Elasticity Courses.

Strength of materials and elasticity are included within the core subjects in the School of Engineering. Modeling and simulation are fundamental in early stages of engineering design and testing. For this reason, we propose the use of a Java application named BEMAPEC in combination with hand made problems to improve the motivation of the engineering students. BEMAPEC applies the Boundary Element Method (BEM) to solve thermoelastic problems of one 3D solid, among other functionalities. Thus, with this application the students could compare solutions and visualize how the tractions and displacements are distributed on the typical problems solved in Strength of Materials and Elasticity courses. BEMAPEC provides a helpful tool for self-learning and a better understanding of the theoretical concepts.

Tool Wear Estimation and Visualization Using Image Sensors in Micro Milling Manufacturing

This paper presents a reliable machine vision system to automatically estimate and visualize tool wear in micro milling manufacturing. The estimation of tool wear is very important for tool monitoring systems and image sensors configure a cheap and reliable solution. This system provides information to decide whether a tool should be replaced so the quality of the machined piece is ensured and the tool does not collapse. In the method that we propose, we first delimit the area of interest of the micro milling tool and then we delimit the worn area. The worn area is visualized and estimated while errors are computed against the ground truth proposed by experts. The method is mainly based on morphological operations and k-means algorithm. Other approaches based on pure morphological operations and on Otsu multi threshold algorithms were also tested. The obtained result (a harmonic mean of precision and recall 90.24 (±2.78)%) shows that the machine vision system that we present is effective and suitable for the estimation and visualization of tool wear in micro milling machines and ready to be installed in an on-line system.

Design and Implementation of a Stereo Vision System on an Innovative 6DOF Single-Edge Machining Device for Tool Tip Localization and Path Correction

In the current meso cutting technology industry, the demand for more advanced, accurate and cheaper devices capable of creating a wide range surfaces and geometries is rising. To fulfill this demand, an alternative single point cutting device with 6 degrees of freedom (6DOF) was developed. Its main advantage compared to milling has been the need for simpler cutting tools that require an easier development. To obtain accurate and precise geometries, the tool tip must be monitored to compensate its position and make the proper corrections on the computer numerical control (CNC). For this, a stereo vision system was carried out as a different approach to the modern available technologies in the industry. In this paper, the artificial intelligence technologies required for implementing such vision system are explored and discussed. The vision system was compared with commercial measurement software Dino Capture, and a dedicated metrological microscope system TESA V-200GL. Experimental analysis were carried out and results were measured in terms of accuracy. The proposed vision system yielded an error equal to ±3 µm in the measurement.

PAELLA as a Booster in Weighted Regression

This paper reports the use of the PAELLA algorithm in the context of weighted regression. First, an experiment comparing this new approach versus probabilistic macro sampling is reported, as a natural extension of previous work. Then another different experiment is reported where this approach is tested against a state of the art regression technique. Both experiments provide satisfactory results.

Parallel Performance of the Boundary Element Method in Thermoelastic Contact Problems.

This paper proposes two parallel algorithms to optimise a Fortran application that solves thermoelastic contact problems between three-dimensional solids using the Boundary Element Method. Parallel libraries like MPI are of great use when trying to minimise the execution time of numerical codes. Experiments carried out show the effectiveness of parallel programming and the study of the obtained results provides information on the main factors influencing that effectiveness. A reduction in the execution time of a 82.93% has been achieved.

Design and Implementation of a Vision System on an Innovative Single Point Micro-machining Device for Tool Tip Localization.

This paper proposes an innovative single point cutting device that requires less maintenance than traditional micro-milling machines, being the cutting tools required simpler easier to develop. This satisfies the market demands on micro manufacturing, where devices must be more accurate and cheaper, able to create a diverse range of shapes and geometries with a high degree of accuracy. A stereo vision system has been implemented as an alternative to the current technologies in the market to locate the tool tip, and with this, make the proper corrections on the CNC machine. In this paper the development of such system is explored and discussed. Experimental results show the accuracy of the proposed system, given an error in the measurement of \({\pm }3\,{\upmu }\mathrm{m}\).

Data Mining Techniques for the Estimation of Variables in Health-Related Noisy Data

Public health in developed countries is heavily affected by pollution specially in highly populated areas. Amongst the pollutants with greatest impact in health, ozone is particularly addressed in this paper due to importance of its effect on cardiovascular and respiratory problems and their prevalence on developed societies. Local authorities are compelled to provide satisfactory predictions of ozone levels and thus the need of proper estimation tools rises. A data driven approach to prediction demands high quality data but those observations collected by weather stations usually fail to meet this requirement. This paper reports a new approach to robust ozone levels prediction by using an outlier detection technique in an innovative way. The aim is to assess the feasibility of using raw data without preprocessing in order to obtain similar or better results than with traditional outlier removal techniques. An experimental dataset from a location in Spain, Ponferrada, is used through an experimental stage in which such approach provides satisfactory results in a difficult case.