Determining Surface Topography of a Dressed Grinding Wheel Using Bio-Inspired DNA-Based Computing

Grinding is commonly used for machining parts made of hard or brittle materials with the intent of ensuring a better surface finish. The material removal ability of a grinding wheel depends on whether the wheel surface is populated with a sufficiently high number of randomly distributed active abrasive grains. This condition is ensured by performing dressing operations at regular time intervals. The effectiveness of a dressing operation is determined by measuring the surface topography of the wheel (regions and their distributions on the grinding wheel work surface where the active abrasive grains reside). In many cases, image processing methods are employed to determine the surface topography. However, such procedures must be able to remove the regions where the abrasive grains do not reside while keeping, at the same time, the regions where the abrasive grains reside. Thus, special kinds of image processing techniques are needed to distinguish the non-grain regions from the grain regions, which requires a heavy computing load and long duration. As an alternative, in the framework of the “Biologicalisation in Manufacturing” paradigm, this study employs a bio-inspiration-based computing method known as DNA-based computing (DBC). It is shown that DBC can eliminate non-grain regions while keeping grain regions with significantly lower computational effort and time. On a surface of size 706.5 μm in the circumferential direction and 530 μm in the width direction, there are about 7000 potential regions where grains might reside, as the image processing results exhibit. After performing DBC, this number is reduced to about 300 (representing a realistic estimate). Thus, the outcomes of this study can help develop an intelligent image processing system to optimize dressing operations and thereby, grinding operations.

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.

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.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Surface roughness evaluation based on acoustic emission signals in robot assisted polishing

The polishing process is the most common technology used in applications where a high level of surface quality is demanded. The automation of polishing processes is especially difficult due to the high level of skill and dexterity that is required. Much of this difficulty arises because of the lack of reliable data on the effect of the polishing parameters on the resulting surface roughness. An experimental study was developed to evaluate the surface roughness obtained during Robot Assisted Polishing processes by the analysis of acoustic emission signals in the frequency domain. The aim is to find out a trend of a feature or features calculated from the acoustic emission signals detected along the process. Such an evaluation was made with the objective of collecting valuable information for the establishment of the end point detection of polishing process. As a main conclusion, it can be affirmed that acoustic emission (AE) signals can be considered useful to monitor the polishing process state.

A case of Paget's disease in an ultracentenarian subject with endoprosthesis of Moore type without cementation

Paget's disease is an osteopathy the incidence of which increases with aging; in the 8th decade, it occurs in 11% of the population. Complications of this pathology often occur, and appear prevalently in form of basilar disease. We report a case of an ultracentenarian patient (aged 107) who suffered a cervical right femur fracture after falling in her own house at the age of 104 years. Radiological and laboratory findings revealed a mono-osseal Paget's disease of the right femur. Because of her good conditions, the patient underwent an operation of non-cemented endoprosthesis of Moore type, with a good post-operatory course and a physiokinetic therapeutic treatment. A kinesiologic evaluation was made, by measuring articular range of motion (ROM) through the neutral zero methods and evaluating the muscular strength: it showed a constantly progressive functional recovery of the lower limbs in both subjective and objective terms.

Aspects of sleep in centenarians

Sleeping habits of 48 Calabrian centenarians (12 males, 36 females) were evaluated. Their average age was 102 +/- 1.87 years (range 100-107 years). Quantitative and qualitative aspects of sleep were recorded, such as the length of sleeping (hrs), the time of falling asleep (regular or variable) and the time of awakening (regular or variable). Moreover, we recorded and included in the global hours an eventual afternoon sleeping, during a 24-hour-period. The quality of sleeping was classified as: night sleeping, and morning sleeping; and for the subjects were grouped as short sleepers (subjects that fall asleep easily), and long sleepers (subjects with problems to fall asleep). We also considered if sleeping was uninterrupted or interrupted during the night and if the subjects followed particular habits to facilitate the sleeping (sleeping pills, alcohol, etc.). This study demonstrated that all the examined centenarians go to sleep early in the evening, have no problems in falling asleep, wake up early in the morning, take a nap in the afternoon and do not take pills before going to bed. Among the environmental factors, the quality, the quantity and the habits of sleeping might have great influence for longevity.

Modeling and Experimental Analysis of Acoustic Emission from Metal Cutting

Testing parameters characterizing acoustic emission (AE) detected during metal cutting may be theoretically correlated, in a simple manner, to work material properites, cutting conditions, and tool geometry. Experimental results, obtained during turning by different researchers using different AE techniques, are presented and critically assessed with reference to their reciprocal agreement as well as their agreement with theoretical predictions. A review of current methods for AE analysis is also presented and the correlations between different AE parameters and energy and power of the detected signals are reported.