Neural Networks Tool Condition Monitoring in Single-point Dressing Operations

Abstract Cognitive modeling of tool wear progress is employed to obtain a dependable trend of tool wear curves for optimal utilization of tool life and productivity improvement, while preserving the surface integrity of the ground parts. This paper describes a method to characterize the dresser wear condition utilizing vibration signals by applying a cognitive paradigm, such as Artificial Neural Networks (ANNs). Dressing tests with a single-point dresser were performed in a surface grinding machine and tool wear measurements taken along the experiments. The results show that ANN processing offers an effective method for the monitoring of grinding wheel wear based on vibration signal analysis

Prediction of Dressing in Grinding Operation via Neural Networks

Abstract In order to obtain a modelling and prediction of tool wear in grinding operations, a Cognitive System has been employed to observe the dressing need and its trend. This paper aims to find a methodology to characterize the condition of the wheel during grinding operations and, by the use of cognitive paradigms, to understand the need of dressing. The Acoustic Emission signal from the grinding operation has been employed to characterize the wheel condition and, by the feature extraction of such signal, a cognitive system, based on Artificial Neural Networks, has been implemented

Tool Condition Monitoring of Single-point Dressing Operation by Digital Signal Processing of AE and AI

Abstract This work aims at determining the right moment to stop single-point dressing the grinding wheel in order to optimize the grinding process as a whole. Acoustic emission signals and signal processing tools are used as primary approach. An acoustic emission (AE) sensor was connected to a signal processing module. The AE sensor was attached to the dresser holder, which was specifically built to perform dressing tests. In this work there were three types of test where the edit parameters of each dressing test are: the passes number, the dressing speed, the width of action of the dresser, the dressing time and the sharpness. Artificial Neural Networks (ANNs) technique is employed to classify and predict the best moment for stopping the dressing operation. During the ANNs use, the results from Supervised Neural Networks and Unsupervised Neural Networks are compared

Damage patterns recognition in dressing tools using PZT-based SHM and MLP networks

Abstract In order to promoting the optimization of the theme: "grinding-dressing", this study intends to contribute to the fill the gap of works completed with the damage diagnostic systems in dressing tools. For this purpose, this work aims to use neural models based on multilayer Perceptron networks (MLP) to improve the damage pattern recognition in diamond dressing tools based on electromechanical impedance (EMI). Thus, experimental dressing tests were performed with a single-point diamond-dressing tool and a low-cost lead zirconate titanate (PZT) transducer to acquire the impedance signatures at different dressing passes. The proposed approach was able to select the optimal frequency range in impedance signatures to determine the dressing tool condition. To achieve this, representative damage indices in several frequency bands were considered as input to the proposed intelligent system. This new approach open the door to effective implementation of future works for a broader situation in grinding process

Acoustic image-based damage identification of oxide aluminum grinding wheel during the dressing operation

Abstract Grinding is a finish process of parts that require high precision and tight dimensional tolerance, which owe high value-added. As the grinding process takes place, the cutting surface of the grinding wheel undergoes wear and then its cutting capacity is reduced. On the other hand, the dressing operation is responsible for restoring the cutting surface of the grinding wheel and, therefore, plays a key role in the grinding process. This work aims at obtaining acoustic images of the grinding wheel surface to identify its conditions during the dressing operation. Experimental tests were conducted with a single-point diamond dresser in a surface grinding machine, which was equipped with an oxide aluminum grinding wheel in which specific marks were intentionally made on its surface to simulate damages for identification. An acoustic emission sensor was fixed to the dresser holder and the signal were acquired at 5 MHz. The signal spectrum was investigated and a frequency band was carefully selected, which represented the conditions of grinding wheel surface. The root mean square values were then computed from the raw signal with and without filtering for several integration periods, and the acoustic images obtained. The results show that the proposed technique is efficient to identify the damage on the wheel surface during the dressing operation as well as its location

Type IIB orientifolds on Gepner points

We study various aspects of orientifold projections of Type IIB closed string theory on Gepner points in different dimensions. The open string sector is introduced, in the usual constructive way, in order to cancel RR charges carried by orientifold planes. Moddings by cyclic permutations of the internal N=2 superconformal blocks as well as by discrete phase symmetries are implemented. Reduction in the number of generations, breaking or enhancements of gauge symmetries and topology changes are shown to be induced by such moddings. Antibranes sector is also considered; in particular we show how non supersymmetric models with antibranes and free of closed and open tachyons do appear in this context. A systematic study of consistent models in D=8 dimensions and some illustrative examples in D=6 and D=4 dimensions are presented.Comment: 67 pages, no figures References added, typos correcte