Ricardo Rezende Zucchini
SIMPLIFIED EVALUATION OF THE SMALLEST SAMPLE MASS THAT SHOULD BE TAKEN FOR AN ACCURATE CHEMICAL ANALYSIS

Technology has been developed very much during the last decades. In order to get better materials, with precise chemical composition, electrical properties, thermal properties, etc, it is a must to be capable to accomplish chemical analysis of blends, sometimes dealing with components in very low levels. Today, there are a number of new analytical techniques which allows quantifying extremely low levels of analytes, even employing very small samples. However, using small samples could generate sampling errors. Experimental determination of the minimum sample masses in order to get good accuracy can be a very costly operation, and a time demanding one. In this paper is proposed a simple method to evaluate the minimum sample mass that should be used in chemical analysis, to get low dispersion results.

Giovanni Betta, Domenico Capriglione, Antonio Pietrosanto
FAULT ACCOMMODATION OF MASS AIR FLOW SENSORS IN DIESEL AUTOMOTIVE ENGINES

This paper deals with the design and the application of Artificial Neural Networks to the fault accommodation of the mass air flow meter in diesel engines. Several architectures are proposed and tested. In order to verify their real applicability to the automotive context, making use of suitable graphical tools and computational load indexes, their performance was compared in terms of accuracy and resource requirements.

Guillermo de Anda-Rodríguez, Eduardo Castillo-Castañeda, Salvador Guel-Sandoval, Juan Hurtado-Ramos, Maria Eugenia Navarrete-Sánchez
ON-LINE WEAR DETECTION OF MILLING TOOLS USING A DISPLACEMENT FIBER OPTIC SENSOR

One of the major problems in precision machining processing can be related to the failure condition of the cutting tool. Hence online tool condition-monitoring is of much interest in metal-mechanics industry. The spontaneous problem of failure in a cutting tool, such as chipping and breakage, is becoming more and more important in the manufacturing technology and is usually associated with the stresses put on the tool during the cutting process. Other abnormal tool conditions in metal-machining includes: tool wear, tool-work piece, etc. In this work a method is presented for determining the wear and fracture of a cutting tool, by means of a fiber optic sensor with high resolution and large bandwidth, used during the manufacturing process. The sensor has been configured with two groups of fibers, distributed in a random fashion. One of the groups works as a light emitter that illuminates the tool, while the other captures the light reflected by the tool surface itself. The light is generated by a photodiode, which does not represent any risk to the health. This technique will allow observing the wear and breakage of the tool in real time while the tool is rotating, with a high degree of accuracy.

László Monostori, Zsolt János Viharos
TECHNICAL MONITORING AND DIAGNOSTICS: INDISPENSABLE ELEMENT OF INTELLIGENT MANUFACTURING SYSTEMS

The application of pattern recognition techniques, expert systems, artificial neural networks, fuzzy systems and nowadays hybrid artificial intelligence techniques in manufacturing can be regarded as consecutive elements of a process started two decades ago. The paper outlines the most important steps of this process. Agent-based systems are highlighted as promising tools for managing complexity, changes and disturbances in production. Further integration of approaches is predicted.

Chingiz Hajiyev
TWO-INTERVAL OVERLAP TEST FOR FAILURE PREDICTION IN KALMAN FILTER

An approach to failure prediction in Kalman filter is developed which uses confidence and tolerance intervals for a innovation sequence. The algorithm proposed is based on the criterion of overlapping these intervals and allows the detection of potential failures in Kalman filter. The problem of failure prediction in multidimensional Kalman filters is solved too. In this case a multidimensional innovation sequence is replaced by one-dimensional sequences.