Wear Measurements Research Articles

Hydroabrasive wear of jet pump nozzles

The measurements of hydroabrasive wear of the jet pumps nozzles, used on dredges operating on transferring pulp, are carried out. The measurement result is compared with the result of modelling the flow in the pump suction chamber near the nozzles. The modelling is carried out using the Fluent program complex as a part of the Ansys program; the pure water is being modelled in the suction pipe. This assumption is made taking into account that pulp contains approximately 10 percent by volume of sand, therefore the trajectories of sand particles movement coincide with the streamlines of water. The results of modeling have shown, that the angle of attack on the surface of nozzle nose part nearest to the pulpline outlet is close to zero, whereas the angles of attack on the surface of the nozzle nose part furthest from the pipeline outlet differ considerably from the zero and range from 20 to 50 degrees, that is the values at which the hydroabrasive wear increases severalfold compared to the angles close to zero. The results of modelling are confirmed by the examination of the worn nozzles. It is turned out that the nose part of the nozzles wears out along a circle unevenly, the part furthest from the pipeline outlet wears at higher rate, that leads to the bevel of the plane of the nozzle nose opening, the bevel angle reaches 20–25 degrees. The microhardness values of the nose part of the worn nozzle are also in agreement with the results of modelling. The measurements have shown, that the microhardness of the nose part of the worn nozzle is uneven along a circle, and the microhardness of the most worn side of the nozzle nose is least. This fact indicates that the different zones of the surface of the nozzle nose part differ from each other by the rigidity of stress state scheme in hydroabrasive attack. It can be used later in choosing the regime of laboratory tests on hydroabrasive wear.

On the Polygonal Wear Evolution of Heavy-Haul Locomotive Wheels due to Wheel/Rail Flexibility and Its Mitigation Measures

Wheel polygonal wear can immensely worsen wheel/rail interactions and vibration performances of the train and track, and ultimately, lead to the shortening of service life of railway components. At present, wheel/rail medium- or high-frequency frictional interactions are perceived as an essential reason of the high-order polygonal wear of railway wheels, which are potentially resulted by the flexible deformations of the train/track system or other external excitations. In this work, the effect of wheel/rail flexibility on polygonal wear evolution of heavy-haul locomotive wheels is explored with aid of the long-term wheel polygonal wear evolution simulations, in which different flexible modeling of the heavy-haul wheel/rail coupled system is implemented. Further, the mitigation measures for the polygonal wear of heavy-haul locomotive wheels are discussed. The results point out that the evolution of polygonal wear of heavy-haul locomotive wheels can be veritably simulated with consideration of the flexible effect of both wheelset and rails. Execution of mixed-line operation of heavy-haul trains and application of multi-cut wheel re-profiling can effectively reduce the development of wheel polygonal wear. This research can provide a deep-going understanding of polygonal wear evolution mechanism of heavy-haul locomotive wheels and its mitigation measures.

Contact stress prediction and fretting wear measurement of aeronautic crowned involute splines

Splines are widely used in aeronautic applications; however, misaligned splines often suffer contact stress concentration and fretting wear fatigue failure. Crowned modification is a useful way to relieve contact stress concentration and mitigate fretting wear for misaligned splines, but it is unclear how this method works. In this study, contact stresses for involute splines are calculated using finite elements and correlated to first-hand experimental results on fretting wear obtained using a dedicated test rig. Both contact stress distribution prediction and fretting wear measurement indicate that the crowned magnitude of splines should be selected according to the operating angular misalignment.

A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction.

The monitoring of the lifetime of cutting tools often faces problems such as life data loss, drift, and distortion. The prediction of the lifetime in this situation is greatly compromised with respect to the accuracy. The recent rise of deep learning, such as Gated Recurrent Unit Units (GRUs), Hidden Markov Models (HMMs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention networks, and Transformers, has dramatically improved the data problems in tool lifetime prediction, substantially enhancing the accuracy of tool wear prediction. In this paper, we introduce a novel approach known as PCHIP-Enhanced ConvGRU (PECG), which leverages multiple-feature fusion for tool wear prediction. When compared to traditional models such as CNNs, the CNN Block, and GRUs, our method consistently outperformed them across all key performance metrics, with a primary focus on the accuracy. PECG addresses the challenge of missing tool wear measurement data in relation to sensor data. By employing PCHIP interpolation to fill in the gaps in the wear values, we have developed a model that combines the strengths of both CNNs and GRUs with data augmentation. The experimental results demonstrate that our proposed method achieved an exceptional relative accuracy of 0.8522, while also exhibiting a Pearson's Correlation Coefficient (PCC) exceeding 0.95. This innovative approach not only predicts tool wear with remarkable precision, but also offers enhanced stability.

Reliability of river fleet vessels hull elements

Improving the process of fault detection of metal ship hull elements by using a probabilistic approach is considered in the paper. The most labor-intensive part of the defect detection report, namely, measurement and assessment of hull structures individual elements wear, accounting for more than 90 % of the total measurements, is reviewed. Defect detection of a ship hull means the examination, measurement and assessment of defects in each element of the ship hull in order to establish method and volumes of repairs that ensure its reliable operation until the next survey under specified operating conditions. The necessity of carrying out a preliminary calculation stage for defect detection is substantiated. It allows you to significantly reduce the number of measurements of hull structures elements, which will have a positive effect on reducing the cost of repair both due to a smaller number of measurements and the duration of repairs. It has been proved that using the probability approach to predict the residual thickness of individual elements also takes into account the effect on the total strength, which guarantees the reliability of the ship hull in operation. Tables to assess the technical condition of individual hull elements based on their level of wear have been developed, and a method for filling them has improved. The tables are made in two versions, namely, paper and electronic, the most convenient for a specific user — the designer of the defect detection report tables. A test example of using the electronic tables for assessing technical condition by wear level, which proves that the predicted residual thicknesses obtained by calculation will in all cases, be less, than similar ones obtained from measurements results, is carried out using a representative vessel. This proves the reliability and feasibility of using the preliminary defect detection stage, which is essentially an analogue of the concept of the zero stage in ship repair. Using the probability approach to determine the residual thicknesses will allow you not only to reduce the costs of defect detection of the hull itself, but also at early stages (before placing the ship on a slip or dock) to reduce the volume of hull structures repairs, and to choose the most appropriate and effective method of repair.

Different Methods of Scan Alignment in Erosive Tooth Wear Measurements: An In Vitro Study.

Model alignment in cases of erosive tooth wear can be challenging, and no method has been reported to outweigh the others. Extracted human teeth were mounted on two models and scanned at different times, from 1 h to 2 weeks, with an intraoral scanner (3Shape TRIOS 4) before and after immersion in Monster® energy drink and tap water. The scans were superimposed (3Shape TRIOS Patient Monitoring, Version 2.2.3.3, 3Shape A/S, Copengagen, Denmark). Best fit, best-fit tooth comparison, reference best fit using fillings, and palatal rugae as reference points were used for alignment. Surface profile differences were calculated in a cross-section view. The nonparametric Bland-Altman and Kruskal-Wallis tests were used. First, statistically significant differences were marked after 4 days of immersion. The measurements obtained after 2 weeks of immersion were statistically significantly different from the measurements obtained at the different time points until 1 week. No statistically significant differences (p < 0.05) were observed among the alignment methods at any time. In comparison to the best-fit model, both palatal rugae and fillings can be used. The best-fit tooth comparison method is a reliable option; however, it should be used with caution in cases of major surface loss.

Hybrid prognostics to estimate cutting inserts remaining useful life based on direct wear observation

The aim of this study is to monitor tool wear through recurrent direct observation, to automatically and optimally assess when it is really necessary to change tools. To achieve this goal, a hybrid prognosis algorithm is formulated to estimate cutting tools’ remaining useful life. Since cutting speed, and more generally process parameters, influences the rate of tool degradation, an adaptive prognosis strategy is presented on the basis of flank wear measurements through the application of a Particle Filter (PF) framework. The adaptability feature allows tracking changes in flank wear evolution. The idea is to fit available degradation curves of cutting tool flank land measurements, through the use of data-driven models, i.e. Multi-Layer Perceptrons (MLP) and cubic polynomials (P3). The Remaining Useful Life of the cutting tool is estimated together with its probability density function, by using a PF framework to adapt MLP weights (or P3 coefficients) along with online flank wear measurements. The devised algorithm was proven to adapt to wear trends from the field, obtained with cutting parameters not previously tested, making it suitable for a robust implementation. The approach was tested when trained upon a single run-to-failure, and validated upon four run-to-failures in different cutting conditions according to a cross-validation inspired technique. P3 was found to be more reliable (from the metrics perspective), whereas MLP allowed to be accurate with greater advance, offering a practical advantage. The proposed algorithm may also be adapted to integrate physical features, like specific force coefficients, with direct wear measurements.

An error compensation method for single point oblique axis grinding considering the grinding wheel wear

With the increasing amount of high quality optical products, the manufacturing requirement for the high-precision optical glass lenses is on the rise. Grinding is typically the main manufacturing process for the optical mold. The degree of wheel wear has a direct impact on the precision of the grinding process. In this paper, the wear rule of grinding wheel in the grinding process of optical aspheric surface is studied with the in-site wear measurement system. A new image processing method and a wear degree evaluation method are proposed. The wear prediction model for the grinding wheel is established. Then a modified compensation method of optical aspheric surface considering grinding wheel wear is proposed. The optical aspheric surface is then compensated using a modified multinomial equation considering grinding wheel wear. The results demonstrate that the profile error of optical aspheric surface can be reduced to less than 150 nm. The proposed compensation method that takes into account the grinding wheel wear can highly improve the grinding precision and efficiency of optical aspheric surface as compared with the conventional compensation method.

Complexity of the Male Perfume of Eulaema nigrita from Forest and Woody Physiognomies of the Brazilian Savanna: Is There a Relationship with Body Size and Wing Wear?

Male orchid bees collect volatile and semi-volatile compounds from the environment for storage and accumulation in specialized hind legs. Later, these compounds form a perfume blend used during courtship to lure conspecific females for mating. Male orchid bees perfume has been suggested to play an important role as a sexual signaling trait involved in pre-mating isolation of species, functioning as an indicator of male genetic quality. Eulaema nigrita Lepeletier (Apidae: Euglossini) is a common species in both forested and woody savanna (Cerrado stricto sensu) physiognomies of the Brazilian savanna biome. By identifying the chemical composition of male E. nigrita perfume, we tested for differences in the bouquet chemical profile in populations from remnants of seasonal semideciduous forest and woody savanna. In addition, we assessed the relation between perfume complexity and morphological traits associated with size and age of males. Our analysis showed a low effect of physiognomies on differences in the perfume chemical profile of sampled males. Nevertheless, we observed significant differences in the chemical profile of individuals from two seasonal semideciduous forest remnants, which suggests an environmental effect in individual bouquet. Wing wear measurements were positively related to perfume complexity, consistent with the premise that perfumes from older individuals are indicators of survival capacity in male orchid bees.

Radioactive tracers in industry: Thin layer activation of carbon-based materials for wear measurement

Thin layer activation (TLA) is already routinely utilised for online wear monitoring of metallic components with the radioisotope concentration (RIC) method for various tribological applications. However, many components in tribological systems are made of carbon-based bulk material (e.g. polymers) or have carbon-based wear resistant coatings (e.g. diamond-like carbon). By utilising the nuclear reaction 12C(3He,x)7Be, TLA can thus be employed in carbon-containing components. To evaluate the applicability of this approach for TLA in combination with the RIC method, three materials EPDM (ethylene propylene diene monomer), PEEK (polyether ether ketone) and DLC (diamond-like carbon), which are vastly utilised in tribology, were activated. Subsequently, the modulus of elasticity and hardness of the activated specimens were measured and evaluated if the material works for TLA. As EPDM developed cracks after the irradiation process, it is regarded to be not applicable for TLA and subsequent wear measurements. While the PEEK material revealed small changes in hardness values compared to non-irradiated samples, the irradiated DLC coating showed no detectable material changes. Both irradiated materials have been applied to representative tribological wear studies, for which the RIC results showed a good correlation with optical wear measurements.

Hybrid prognosis of drill-bits based on direct inspection

Within the paradigm of industry 5.0, manufacturing systems are seeking for human-centred production, where the operator finds high-level supervision tasks. In this context, low-level decision making should be performed by machines themselves. In this paper, a hybrid prognosis algorithm is developed to automatically inspect the cutting edges of drill-bits and to predict their Remaining Useful Life (RUL) and the associated probability density function. The solution relies on the automatic measurement of flank wear through convolutional filtering and edge detection. Prognosis exploits particle filter, which updates multi-layer perceptron with online data, to adaptively predict drill-bits RUL. The solution reduces the experimental preliminary run-to-failures needed for training standard machine learning algorithms, exploiting them in a real-time adaptive scenario, while predicting tool RUL under untested and variable cutting process operations. The algorithm uses direct wear observations, taken during set-up times (e.g., tool changes, workpiece change), thus not interfering with the process.

A Generalized Multi-Stage Deep Machine Learning Framework for Tool Wear Level Prediction in Milling Operations

This work proposes a tool condition monitoring system based on a generalized multi-stage deep machine learning framework for real-time measurement of flank wear in milling processes. At low computational cost, a deep wavelet scattering convolution neural network framework was developed and optimized to generate low-variant features. Discriminative features were automatically selected using a neighborhood component analysis. A gaussian process regression (GPR) model was developed for feature fusion and tool wear measurement. The model was benchmarked against different machine learning algorithms. Extensive experimental validation tests demonstrated the GPR model superiority under various cutting conditions, which facilitates its implementation in industrial facilities.

Machining Tool Wear Detection and Measurement Based on Edge Extraction and Subpixel Fitting

Machining Tool Wear Detection and Measurement Based on Edge Extraction and Subpixel Fitting

Estimating Remaining Useful Life of Cutting Tools in Machining Using an Extended Kalman Filter

Monitoring the condition of the cutting tool and forecasting the evolution of its wear during machining are vital to ensure workpiece quality and the safety of machine elements. The accurate prediction of tool wear is crucial to advance predictive maintenance in machining. This article presents a methodology to estimate the remaining useful life (RUL) of cutting tools. The methodology involves extracting features from vibration signals using discrete wavelet transform (DWT) and computing the concomitant indicators of tool health by applying distance metrics to the features. These indicators have a high correlation with tool wear measurements. The RUL is estimated by fusing these indicators in a support vector regression (SVR) algorithm. The SVR outputs then serve as inputs to an extended Kalman filter (EKF) that employs a rectified linear activation unit (ReLU) function for state evolution, enabling real-time estimation of the RUL. The proposed methodology is demonstrated on the IEEE PHM 2010 dataset, showcasing its reliability and effectiveness in accurately estimating the RUL for cutting tools.

Design and Development a Software for Wear Measurement of Control Rod Guide Card Based on Machine Vision

The control rod guide tube (CRGT) is used to protect the rod cluster control assembly (RCCA) from bending while providing guidance for the up and down movement of the RCCA. This ensures that the dropping time of the RCCA meets nuclear safety requirements and stops nuclear reactions within the specified time frame. During the up and down movement of the RCCA, the control rod guide cards (CRGC) may experience axial and vibration wear. If the wear is severe, the CRGC may lose its guiding function, resulting in serious problems such as delayed dropping time. In this paper, a machine vision method is proposed to measure the dimensions of the CRGC. Edge extraction, image rectification, and template matching are used to realize autonomous measurement of the CRGC based on its features. The developed measurement software has been used 5 times in domestic nuclear power plant overhauls and has detected 15 CRGTs that were close to the wear limit. This provided data support for the replacement of the CRGTs and effectively improved the quality of nuclear power operation.

Tool Condition Monitoring for milling process using Convolutional Neural Networks

Tool condition monitoring has emerged as an essential approach in the machining industry to drive cost reductions and enhance productivity. In the era of Industry 4.0, tool wear monitoring can be done using various Internet of Things sensors, such as those recording acoustic emission, vibration, temperature, and pressure. Signals acquired can be processed by extracting numeric features or converting them into images.In this study, experimental milling campaigns with different input machine parameters were conducted, leading to the creation of a dataset based on tool wear measurements and acoustic emission registrations. Then we investigated a deep convolutional neural networks approach to classify image representations of the registered acoustic signals, aiming to predict the status of tool wear. Since the dataset was imbalanced, a class weights approach was used to improve model performance. An accuracy exceeding 90% was reached, demonstrating a great potential of the model in predicting tool wear state.

Rail Wear Evolution on Small-Radius Curves under Mixed Traffic Conditions, In-Field Investigations

This paper’s aim is to assess the rail wear evolution of railway tracks under mixed traffic conditions for sharp curves. Field investigations were conducted over a period of five years, at 800 points on a rail track, for two curves that underwent maintenance interventions: sleeper and rail replacements during measurements. Lateral and vertical wear measurements were monitored with mechanical instruments, and Prokon and Sap simulations were performed in order to determine the bearing capacity of the rail at different stages of wear. The results obtained after five years of investigation show wear speed propagation on the outer rail of small-radius curves as well as the bending moments and admissible stress variation. At more than 18% of the studied points, lateral wear exceeded the maximum admissible limit, showing a faster propagation speed on the outer rail compared to the vertical wear rate.

TO THE DEVELOPMENT OF STATISTICAL MODELS OF WEAR ROLLS OF LONG PRODACTS MILLS

For long products rolling mills, a change in the indicators of wear and costs in a relatively wide range is characteristic, depending on the type of rolled product, the features of the technology, the materials used in the rolls, and other factors. This largely determines the level of operating costs when rolling profiles. Therefore, increasing the wear resistance of long product mill rolls was and remains an urgent task in metallurgy.&#x0D; The purpose of the work is to analyze the structure of known mathematical models of wear of rolls of rolling mills, features of wear of rolls of individual long-products rolling mills, and to determine a general approach to the construction of statistical models for predicting the wear of such rolls.&#x0D; From the review of the literature, it was established that the structure of most wear models includes technological parameters, that are most significant for a certain state, and an empirical coefficient. For the conditions of long products mill, in each of the stands, most of the parameters change within rather narrow limits due to technological deviations. Therefore, for stands mills in such conditions, it is advisable to use "local" statistical models obtained from the results of wear measurements. The structure of such models can be represented as the product of the empirical coefficient, which indirectly reflects the influence of the main technological parameters, and the friction way, as the main factor of the Archard model.&#x0D; As an empirical coefficient, it is recommended to use the indicative wear intensity of the rolls, defined as the ratio of the amount of wear of the groove roll to the mass of rolled products obtained from this pass. The revealed features of the change in the wear intensity indicator are due to the processes of processing the initial surface and the formation of a certain microstructure. After the rolling of the threshold mass of rolled products, these processes are completed and the indicator of wear intensity reaches a stable value. The initial and permanent value of the wear intensity indicator, as well as the threshold value of the mass of rolled steel, are determined based on empirical data on wear and mass of rolled steel for each of the stands mill.&#x0D; The proposed method of determining wear using the wear intensity indicator as an empirical coefficient that indirectly reflects the influence of all significant factors of the process provides sufficient accuracy of wear prediction and can be used to determine the costs of rolls.

Development of A Tool Condition Monitoring System for Flank Wear in Turning Process Using Machine Learning

Computer-numerical control (CNC) machining has become the norm in most manufacturing businesses. In order to maximize machining efficiency, prevent unintended damage, and maintain product quality all at once, tool wear measurement is essential. Wear on the tools is typically an inevitable side effect of machining. Because it endangers the machining process, flank wear, the most frequent type of tool wear, should be avoided. This study's objective is to fill this growing need by developing a system that can use machine learning to monitor tool condition during the turning process using MATLAB. The regression method with boosted decision trees and SVR with Gaussian kernels are applied to predict the flank wear based on the vibration signal and cutting parameters. This study found that the regression with boosted decision tree method has a lower mean average percentage error, 6.43%, while SVR is 10.11%. Plus, R2 for regression is slightly better than SVR. It shows that the system successfully produced an accurate prediction of flank wear.

Mechanical cleaning of food soil from a solid surface: A tribological perspective

In this work, a tribological approach was used to distinguish the synergistic effects of mechanical removal and chemical removal (i.e. dissolution) of a layer of representative food soil from a solid surface, using a tribometer, Mini Traction Machine (MTM). Gravimetric and wear measurements of the soil were used to calculate the cleaning rates of burnt tomato puree on a stainless-steel disc, and the corresponding frictional characteristics offers insight of the mechanical removal. The cleaning due to soil dissolution (chemical removal) was quantified by UV–Vis measurements. The overall cleaning rates of food soil featured a linear reduction in mass over time, with a scaled removal rate k = 0.0046 s−1 (5 N applied force and 100 mm s−1 relative velocity), for most cases studied. It was observed that the cleaning rate can be improved with an increasing mechanical load or speed (50% from 1 to 2.5 N and 13% from 50 to 100 mm s−1), but is independent of the initial mass. UV–Vis measurements show that by increasing the load or speed the removal of chunks of burnt tomato puree was enhanced more than removal attributed to dissolution. Similar values of cleaning rates for most experimental parameters were extracted from both the gravimetric and wear measurements. Adhesion and cohesion measurements of the burnt tomato puree were conducted with a micromanipulator. It was found that adhesion forces are higher than cohesion for short soaking times, but for longer times the adhesion forces became weaker and with the additional shear rate in the MTM cleaning experiment, adhesion failure was observed in many cases by the end of the experiment. Indentation measurements showed the change in mechanical properties of the food foulant with a few minutes of soaking in water.