Hilde Pérez

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.

Generic Mathematical Model for Efficient Milling Process Simulation

The current challenge in metal cutting models is to estimate cutting forces in order to achieve a more accurate and efficient machining process simulation and optimization system. This paper presents an efficient mathematical model for process simulation to evaluate the cutting action with variable part geometries of helical cutters and predict the cutting forces involved in the process. The objective of this paper has been twofold: to improve both the accuracy and computational efficiency of the algorithm for cutting force estimation in peripheral milling. Runout effect and the real tool tooth trajectory are taken into account to determine the instantaneous position of the cutting flute. An expression of average chip thickness for the engaged flute in the cut is derived for cutting force calculations resulting in a more efficient process simulation method in comparison with previous models. It provides an alternative to other studies in scientific literature commonly based on numerical integration. Experiments were carried out to verify the validity of the proposed method.

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.

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.

Intelligent Decision System Based on Fuzzy Logic Expert System to Improve Plastic Injection Molding Process

Intelligent Systems are the best way to manage complex industrial processes with a high number of process parameters, like injection molding process. Specifically, Fuzzy Logic is a solution to estimate if the qualitative inspection of parts produced allows us to determine correct process parameter value setting to produce good quality parts. This paper shows an intelligent decision system based on Fuzzy Logic techniques designed using defect behavior tendency curves as membership functions. These functions are improving with dynamics and adaptive regression membership functions based on the assessment of quality for a given part done by an operator. The implementation of this intelligent decision system designed for injection molding process shows that is able to transform a qualitative variable deduced of qualitative injection inspection of part defects, into a quantitative inspection, identifying the correct process parameters. Experimental results show that the effectiveness is improved and also reduces the time of a process in a 40%.

Use of Support Vector Machines and Neural Networks to Assess Boar Sperm Viability

This paper employs well-known techniques as Support Vector Machines and Neural Networks in order to classify images of boar sperm cells. Acrosome integrity gives information about if a sperm cell is able to fertilize an oocyte. If the acrosome is intact, the fertilization is possible. Otherwise, if a sperm cell has already reacted and has lost its acrosome or even if it is going through the capacitation process, such sperm cell has lost its capability to fertilize. Using a set of descriptors already proposed to describe the acrosome state of a boar sperm cell image, two different classifiers are considered. Results show the classification accuracy improves previous results.

Skills Development of Professional Ethics in Engineering Degrees in the European Higher Education Area

In this paper, an experience to approach the competence about ethical aspects of the profession is presented. Following an existing methodology, several cases are presented to the students in order to determine if people involved have had a professional or ethical behaviour. Codes of professional ethics or conduct have been also discussed with the students. The experience has been successful since students have actively participated and valued the methodology positively. This solves the lack of prior training in these ethical aspects.