Process Related Characteristic-Based Topography Evaluation of Wear Conditions On Grinding Wheels

Non-productive auxiliary processes affect the single part and small badge production of milling tools. The key production process grinding is inevitably linked to the auxiliary conditioning process. The time demand of those process steps decreases the overall productivity of the manufacturing process. However, today the machine operator decides on conditioning cycles individually by the use of experience. Until today, there is no objective data based approach available that supports the initiation of these conditioning processes or the adaption of the grinding process itself in order to improve its process efficiency. For this purpose, a process related topography evaluation method of the grinding wheel surface is developed within this study. For the measurement an optical method based on laser triangulation is used. The measurement system is implemented into a common tool grinding machine tool. In addition, characteristic topography values are defined that show the wear conditions of the grinding tool. Moreover, the data is summarized in a database of wear conditions. The developed measurement method can save grinding and dressing tool resources, process times and minimizes scrap parts. In addition, an adaptation of the process and a targeted launch of auxiliary processes can be enabled. The novel characteristic-based topography measurement creates the opportunity to enhance the tool life of the grinding wheels up to 30 % without losing productivity.

Experimental Analysis of a Grinding Wheel by Increasing Multiple Passes in Dressing

When sharpness of grinding wheel becomes dull, a dressing is an operation performed because of glazing and loading, dulled grains and chips are removed (crushed or fallen) with a proper dressing tool to make sharp cutting edges and simultaneously, make recesses for chips by properly extruding to grain cutting edges. The basic dressing operation consists of removal of grains and swarfloaded, generation and exposure of the new cutting edges on the cutting surface of the grinding wheel. The former is obtained by digging out the swarf and the latter is achieved by fracturing the existing abrasive grains and allowing desired protrusion of abrasive particles on the cutting surface. Both of the above operations are carried out using a diamond dresser (3 carat). A part of this work focuses on finding the optimum dressing parameters (depth of cut of the dresser and the number of passes) which gives optimum cutting condition for grinding using conventional method of grinding using diamond dresser, Water Jet Machine and Abrasive Water Jet Machine. The trial run on the conventional machine with different parameters was performed. The results obtained prove the possibility of using directly in the industrial practices.

Design and Improving Vital Sign with Parameter Body Temperature (axilla) and Oxymetry for Patient Monitoring

In most hospitals, nurses routinely calculate and document primary vital signs for all patients 2-3 times per day to get information on the patient's condition. Vital Sign Monitor is made for medical devices that can diagnose patients who need intensive care to determine patient needs. Some parameters used in patient renewal: Oxygen saturation (SPO2), and body temperature. This makes additional tasks very important to be evaluated for medical staff and equipment manufacturers. This evaluation is needed to get the real condition of the patient. With the large number of patients who need evaluation, it is not possible to see the condition of some medical workers who work. This medical service is expected to reduce the workload of nurses with doctors and improve the quality of patient care. The large demand for these devices, mostly in hospital intensive rooms, is the basis for researching the output of data from multiple vital sensor monitor monitors to obtain accurate and precise outputs. The output of the two sensors is processed by Arduino Mega2560 and requested on a 5 inch TFT LCD in the form of body temperature and oxygen saturation. Comparison of module results with standard measuring instruments calibrated to reference this module is used for accurate and precise results. According to the assessment and reversing tool data with the dressing tool, the highest error value is 1%. With a maximum permitted permission of 5%.

Tool Condition Monitoring in Grinding Operation Using Piezoelectric Impedance and Wavelet Transform

The purpose of the present study is to monitor tool condition in a grinding operation through the electromechanical impedance (EMI) using wavelet analysis. To achieve this, a dressing experiment was conducted on an industrial aluminum oxide grinding wheel by fixing a stationary single-point diamond tool. The proposed approach was verified experimentally at various dressing tool conditions. The signals obtained from an EMI data acquisition system, composed of a piezoelectric diaphragm transducer attached to the tool holder, were processed using discrete wavelet transform. The approximation and detail coefficients obtained from wavelet decomposition were used to estimate tool condition using the correlation coefficient deviation metric (CCDM). The results show excellent performance in tool condition monitoring by the proposed technique, which effectively contributes to modern machine tool automation.

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 2—Neural Networks and K-Nearest Neighbor Classifier Approach

This paper presents an approach for impedance-based sensor monitoring of dressing tool condition in grinding by using the electromechanical impedance (EMI) technique. This method was introduced in Part 1 of this work and the purpose of this paper (Part 2) is to achieve an optimal selection of the excitation frequency band based on multi-layer neural networks (MLNN) and k-nearest neighbor classifier (k-NN). The proposed approach was validated on the basis of dressing tool condition information obtained from the monitoring of experimental dressing tests with two industrial stationary single-point dressing tools. Moreover, representative damage indices for diverse damage cases, obtained from impedance signatures at different frequency bands, were taken into account for MLNN data processing. The intelligent system was able to select the most damage-sensitive features based on optimal frequency band. The best models showed a general overall error lower than 2%, thus robustly contributing to the efficient automation of grinding and dressing operations. The promising results of this study foster the EMI-based sensor monitoring approach to fault diagnosis in dressing operations and its effective implementation for industrial grinding process automation.

Dressing Tool Condition Monitoring through Impedance-Based Sensors: Part 1—PZT Diaphragm Transducer Response and EMI Sensing Technique

Low-cost piezoelectric lead zirconate titanate (PZT) diaphragm transducers have attracted increasing attention as effective sensing devices, based on the electromechanical impedance (EMI) principle, for applications in many engineering sectors. Due to the considerable potential of PZT diaphragm transducers in terms of excellent electromechanical coupling properties, low implementation cost and wide-band frequency response, this technique provides a new alternative approach for tool condition monitoring in grinding processes competing with the conventional and expensive indirect sensor monitoring methods. This paper aims at assessing the structural changes caused by wear in single-point dressers during their lifetime, in order to ensure the reliable monitoring of the tool condition during dressing operations. Experimental dressing tests were conducted on aluminum oxide grinding wheels, which are highly relevant for industrial grinding processes. From the results obtained, it was verified that the dresser tip diamond material and the position of the PZT diaphragm transducer mounted on the dressing tool holder have a significant effect on the sensitivity of damage detection. This paper contributes to the realization of an effective monitoring system of dressing operations capable to avoid catastrophic tool failures as the proposed sensing approach can identify different stages of the dressing tool lifetime based on representative damage indices.