This assessment of impairment in drivers of carbon steel through intelligent system

Este artículo describe la aplicación de la integración entre un sistema de lógica difusa y una red neuronal artificial Fuzzy ArtMap además del procesamiento digital de imágenes, con el objetivo de reconocer patrones en la microestructura de los materiales de acero al carbón SA 210 Grado A-1, además de estimar el daño presente en el material a partir de imágenes que presentan diversos estados físicos del mismo material. Los patrones estudiados en la microestructura del material SA 210 Grado A-1 son: perlita laminar, esferoidización y grafitización. Los resultados obtenidos muestran que la estimación del daño y el reconocimiento del patrón en el material fueron realizados de mejor forma por el sistema en comparación con expertos del área.This paper describes the development of an intelligent integrated system comprised of a fuzzy logic architecture developed from descriptive statistics and an artificial neural network Fuzzy ArtMap applied in pattern recognition with digital image processing. The studied patterns are from the microstructure of carbon steel SA 210 Grade A-1. The purpose is to estimate the damage present in the material from the determination of the physical state of the material. Studied patterns in the microstructure of the material were: pearlite lamellar, spheronization and graphitization. The results showed that the damage estimation and pattern recognition in the material were correctly predicted with the developed system compared to the human expert

COMPARACIÓN DE LOS PARÁMETROS IDENTIFICADOS DE LAS FILOSOFÍAS TOC Y JIT, INDICADORES Y VARIABLES MEDIANTE ESCENARIOS DE SIMULACIÓN (COMPARISON OF IDENTIFIED PARAMETERS OF TOC AND JIT PHILOSOPHIES, INDICATORS AND VARIABLES THROUGH SIMULATION SCENARIOS)

Resumen En este artículo se presenta el análisis del desempeño de dos filosofías de producción, como son Teoría de restricciones (TOC, por sus siglas en inglés) y Justo a tiempo (JIT, por sus siglas en inglés), a través de escenarios de simulación empleando el software Promodel. Para el análisis se comparan las herramientas e indicadores empleados en las filosofías TOC y JIT, así como las variables proporcionadas por el software, esto con el fin de evaluar y seleccionar la filosofía que ofrezca mejores resultados. Esto servirá como pauta para que las empresas puedan seleccionar e implementar la filosofía que se ajuste mejor a sus sistemas de producción y contribuya a la mejora de sus procesos. Palabras clave: sistemas de producción, manufactura esbelta, teoría de restricciones (TOC), justo a tiempo (JIT), simulación. Abstract This article presents the performance analysis of two production philosophies, Theory of Contraints (TOC) and Just in Time (JIT), through simulation scenarios using Promodel software. On the analysis, the tools and indicators used on TOC and JIT philosophies and the variables provided by the software are compared, in order to evaluate and select the philosophy that offers the best results. This will serve as a guideline to companies so they can select and implement the philosophy that suits better to their production systems and contribute to the improvement of their processes. Keywords: production systems, lean manufacturing, theory of constraints (TOC), just in time (JIT) and simulation

Comparación de predicción basada en redes neuronales contra métodos estadísticos en el pronóstico de ventas

La intención del presente artículo es realizar la comparación y selección de un método para pronosticarlas ventas de forma eficiente y que beneficie a organizaciones que ofrecen sus productos al mercado ya que los pronósticos de ventas son datos de entrada a diferentes áreas de la empresa y de ser imprecisos pueden generar gastos para la organización. El caso de estudio en este artículo fue llevado a cabo dentro de la empresa Productos Frugo S.A. de C.V., dedicada a la comercialización de productos alimenticios. Los métodos y metodologías utilizados y posteriormente comparados al pronosticar las ventas de la empresa antes mencionada son: Método de Hold, Winters, la metodología Box Jenkins (ARIMA) y una Red Neuronal Artificial. Los resultados muestran que la red neuronal artificial obtuvo un mejor desempeño logrando el menor error cuadrático medio, de esta forma es posible establecer un panorama adecuado para el uso de la inteligencia artificial dentro de la industria

EVOLUTION OF 2K FACTORIAL DESIGN: EXPANSION AND CONTRACTION OF THE EXPERIMENTAL REGION WITH A FOCUS ON FUZZY LEVELS

This research presents a novel approach of an evolution of the 2k factorial design (E2kFD) which is based on a fuzzy search heuristic technique. Moreover, this approach allows multiple horizontal and vertical exploration spaces, expanding and contracting the experimental region within the region of operability. It can be achieved by defining a variable position for each point or treatment of study factors in its geometric representation, as an alternative to the conventional 2k factorial design in which fixed positions at the vertices of its geometric representations are defined. In general, the main disadvantage of model-based DoE methods is the requirement for fast and reliable decision making in setting the deterministic value for each level. It is often that the value of the levels is unknown in advance inducing imprecision and vagueness when these are determined by experts based on their heuristic knowledge. For this reason, the proposed method combines the main advantages to use values with uncertainty for each one of the levels and to be able to explore inside and outside of the experimental region to assign variables positions to coded levels, and therefore the method works iteratively. Moreover, in the proposed fuzzy search heuristic technique approach the high and low levels of each factor are considered as linguistic variables. These are classified into three linguistic labels in a simple and clear language as: regular, major and minor which are used as an indicator of strength. A maximum fuzzy operator in the implication and aggregation stages are used to search the highest membership value and their position to assign feasible fuzzy levels as one of the scientific contributions of the present investigation. The method is demonstrated with a simulation, which shows the potential of the proposed approach. Additionally, the traditional 2k factorial and our evolved E2kFD expert designs were validated conducting the experimental tests in a textile company in southern Guanajuato, Mexico. Finally, by comparing the results between the traditional and our proposed designs, it will be shown that better explanation and prediction models for the response variables under study are obtained with the E2kFD proposed design

Quantitative Analysis of the Balance Property in Factorial Experimental Designs 24 to 28

Experimental designs are built by using orthogonal balanced matrices. Balance is a desirable property that allows for the correct estimation of factorial effects and prevents the identity column from aliasing with factorial effects. Although the balance property is well known by most researchers, the adverse effects caused by the lack or balance have not been extensively studied or quantified. This research proposes to quantify the effect of the lack of balance on model term estimation errors: type I error, type II error, and type I and II error as well as R2, R2adj, and R2pred statistics under four balance conditions and four noise conditions. The designs considered in this research include 24–28 factorial experiments. An algorithm was developed to unbalance these matrices while maintaining orthogonality for main effects, and the general balance metric was used to determine four balance levels. True models were generated, and a MATLAB program was developed; then a Monte Carlo simulation process was carried out. For each true model, 50,000 replications were performed, and percentages for model estimation errors and average values for statistics of interest were computed

Multivariate Pattern Recognition in MSPC Using Bayesian Inference

Multivariate Statistical Process Control (MSPC) seeks to monitor several quality characteristics simultaneously. However, it has limitations derived from its inability to identify the source of special variation in the process. In this research, a proposed model that does not have this limitation is presented. In this paper, data from two scenarios were used: (A) data created by simulation and (B) random variable data obtained from the analysed product, which in this case corresponds to cheese production slicing process in the dairy industry. The model includes a dimensional reduction procedure based on the centrality and data dispersion. The goal is to recognise a multivariate pattern from the conjunction of univariate variables with variation patterns so that the model indicates the univariate patterns from the multivariate pattern. The model consists of two stages. The first stage is concerned with the identification process and uses Moving Windows (MWs) for data segmentation and pattern analysis. The second stage uses Bayesian Inference techniques such as conditional probabilities and Bayesian Networks. By using these techniques, the univariate variable that contributed to the pattern found in the multivariate variable is obtained. Furthermore, the model evaluates the probability of the patterns of the individual variables generating a specific pattern in the multivariate variable. This probability is interpreted as a signal of the performance of the process that allows to identify in the process a multivariate out-of-control state and the univariate variable that causes the failure. The efficiency results of the proposed model compared favourably with respect to the results obtained using the Hotelling’s T2 chart, which validates our model

Multivariate Pattern Recognition in MSPC Using Bayesian Inference

Multivariate Statistical Process Control (MSPC) seeks to monitor several quality characteristics simultaneously. However, it has limitations derived from its inability to identify the source of special variation in the process. In this research, a proposed model that does not have this limitation is presented. In this paper, data from two scenarios were used: (A) data created by simulation and (B) random variable data obtained from the analysed product, which in this case corresponds to cheese production slicing process in the dairy industry. The model includes a dimensional reduction procedure based on the centrality and data dispersion. The goal is to recognise a multivariate pattern from the conjunction of univariate variables with variation patterns so that the model indicates the univariate patterns from the multivariate pattern. The model consists of two stages. The first stage is concerned with the identification process and uses Moving Windows (MWs) for data segmentation and pattern analysis. The second stage uses Bayesian Inference techniques such as conditional probabilities and Bayesian Networks. By using these techniques, the univariate variable that contributed to the pattern found in the multivariate variable is obtained. Furthermore, the model evaluates the probability of the patterns of the individual variables generating a specific pattern in the multivariate variable. This probability is interpreted as a signal of the performance of the process that allows to identify in the process a multivariate out-of-control state and the univariate variable that causes the failure. The efficiency results of the proposed model compared favourably with respect to the results obtained using the Hotelling’s T2 chart, which validates our model

System for the recognition of wear patterns on microstructures of carbon steels using a multilayer perceptron

Este artículo describe la aplicación de un sistema de reconocimiento de patrones de desgaste presente en aceros al carbón, el sistema clasifica la microestructura de los materiales los cuales presentan tres condiciones a lo largo de su vida útil en plantas termoeléctricas. El enfoque propuesto emplea la red neuronal artificial perceptrón multicapa, en conjunto con el procesamiento digital de imágenes para reconocer los diferentes estados físicos de los materiales utilizados como conductores en condiciones de altas temperaturas. La microestructura de las condiciones estudiadas son esferonización, descarborización y grafitización. La microestructura se revela a partir de imágenes de microscopio obtenidos en el Laboratorio de Pruebas de Equipos y Materiales de la Comisión Federal de Electricidad de México (CFE-LAPEM). El sistema propuesto, en comparación con el humano experto, obtuvo una exactitud promedio del 96.82 % con un menor tiempo de análisis y costo de inspección

System for the recognition of wear patterns on microstructures of carbon steels using a multilayer perceptron

This paper describes the application of a recognition system wear patterns present in carbon steel, the system classifies the microstructure of the materials which have three conditions throughout life-time in thermoelectric plants. This approach employs the artificial neural network multilayer perceptron in conjunction with the digital image processing to recognize the different physical states of the materials used as conductors in conditions of high temperatures. The studied patterns in the microstructure are spheronization, decarburization and graphitization. The microstructure is revealed from microscope images obtained in the Testing Laboratory Equipment and Materials of the Federal Electricity Commission in Mexico (LAPEM-CFE). The proposed system compared to the human expert, obtained an accuracy of 96.83 % with a shorter analysis time and inspection cost.Este artículo describe la aplicación de un sistema de reconocimiento de patrones de desgaste presente en aceros al carbón, el sistema clasifica la microestructura de los materiales los cuales presentan tres condiciones a lo largo de su vida útil en plantas termoeléctricas. El enfoque propuesto emplea la red neuronal artificial perceptrón multicapa, en conjunto con el procesamiento digital de imágenes para reconocer los diferentes estados físicos de los materiales utilizados como conductores en condiciones de altas temperaturas. La microestructura de las condiciones estudiadas son esferonización, descarborización y grafitización. La microestructura se revela a partir de imágenes de microscopio obtenidos en el Laboratorio de Pruebas de Equipos y Materiales de la Comisión Federal de Electricidad de México (CFE-LAPEM). El sistema propuesto, en comparación con el humano experto, obtuvo una exactitud promedio del 96.82 % con un menor tiempo de análisis y costo de inspección