Monitoring Single-point Dressers Using Fuzzy Models

AbstractGrinding causes progressive dulling and glazing of the grinding wheel grains and clogging of the voids on the wheel's surface with ground metal dust particles, which gradually increases the grinding forces. The condition of the grains at the periphery of a grinding wheel strongly influences the damage induced in a ground workpiece. Therefore, truing and dressing must be carried out frequently. Dressing is the process of conditioning the grinding wheel surface to reshape the wheel when it has lost its original shape through wear, giving the tool its original condition of efficiency. Despite the very broad range of dressing tools available today, the single-point diamond dresser is still the most widely used dressing tool due to its great versatility. The aim of this work is to predict the wear level of the single-point dresser based on acoustic emission and vibration signals used as input variables for fuzzy models. Experimental tests were performed with synthetic diamond dressers on a surface-grinding machine equipped with an aluminum oxide grinding wheel. Acoustic emission and vibration sensors were attached to the tool holder and the signals were captured at 2MHz. During the tests, the wear of the diamond tip was measured every 20 passes using a microscope with 10 to 100 X magnification. A study was conducted of the frequency content of the signals, choosing the frequency bands that best correlate with the diamond's wear. Digital band-pass filters were applied to the raw signals, after which two statistics were calculated to serve as the inputs for the fuzzy models. The results indicate that the fuzzy models using the aforementioned signal statistics are highly effective for predicting the wear level of the dresser

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

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

time domain analysis based on the electromechanical impedance method for monitoring of the dressing operation

Abstract Among the methods used in structural health monitoring (SHM), the electromechanical impedance technique (EMI), which uses piezoelectric transducers of lead zirconate titanate (PZT), stands out for its low cost. Then, this paper presents a new approach for monitoring of the dressing operation from the digital processing of voltage signals based on the time-domain response of a PZT transducer by EMI method. Experimental tests of the dressing process were performed by using a single-point dresser equipped with a natural diamond. The voltage signals in the time-domain were collected in different damage levels. By using temporal statistics, it was possible to qualify different damage levels that the diamond suffered during the dressing operation, observing variations from the magnitude of the signals. The dressing operation is of utmost importance for the grinding process and the dresser wear negatively affects the result of the process, which owns high added value. In this way, this work contributes with a new monitoring tool which aims ensuring a consistent dressing operation

Smart process monitoring of machining operations

The following thesis explores the possibilities to applying artificial intelligence techniques in the field of sensory monitoring in the manufacturing sector. There are several case studies considered in the research activity. The first case studies see the implementation of supervised and unsupervised neural networks to monitoring the condition of a grinding wheel. The monitoring systems have acoustic emission sensors and a piezoelectric sensor capable to measuring electromechanical impedance. The other case study is the use of the bees' algorithm to determine the wear of a tool during the cutting operations of a steel cylinder. A script permits this operation. The script converts the images into a numerical matrix and allows the bees to correctly detect tool wear