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Abrasive air jet (AAJ) machining is attractive for micromachining hard and brittle materials, while it is usually a big challenge to numerically investigate the particle velocity and concentration distribution in the particle erosion process in the AAJ. In this work, a recently developed semi-resolved CFD-DEM approach which bridges the simulation gap between the resolved and unresolved CFD-DEM, is further improved to reconstruct the background information with a double cosine kernel function. The semi-resolved CFD-DEM is then employed to numerically investigate the gas-particle two-phase flow in the AAJ and numerical results show that this semi-resolved CFD-DEM is more accurate in modeling particulate flow in the fine AAJ nozzle than the conventional unresolved CFD-DEM. We further conduct mechanism investigations on the AAJ micromachining process including particle flow characteristics inside the cylindrical nozzle, velocity, and concentration distribution over the nozzle exit, which are essential jet characteristic features. We identified the particle flow patterns, analyzed the particle distribution, and its correlation with air pressure, abrasive mass flow rate, and turbulence effects. The present simulation results and analyses can be great helpful in understanding the erosion mechanism and optimizing the setting parameters to improve the cutting performance of AAJ. The semi-resolved CFD-DEM approach is used to investigate the gas-particle two-phase flow in AAJ. Unlabelled Image • A double cosine kernel function (SCKF) was introduced into a recently developed semi-resolved CFD-DEM. • The semi-resolved CFD-DEM with SCKF was shown to be effective in investigating abrasive air jet (AAJ). • The particle-scale distribution information in a fine AAJ nozzle was studied. • The effects of process parameters and turbulence on particle distribution were analyzed. [ABSTRACT FROM AUTHOR]

Interest in natural fiber–reinforced composites (NFRCs) is increasing rapidly thanks to their numerous advantages such as low cost, biodegradability, eco-friendly nature, relatively good mechanical properties, and a growing emphasis on the environmental and sustainability aspects of engineering materials. However, large-scale use of NFRCs is still considered as challenging due to the difficulties in manufacturing, limited knowledge of its machinability and appropriate parameter settings, and being prone to machining-induced defects. This article presents a comprehensive review on various aspects of NFRCs, with a focus on the manufacturing and machinability. It covers some recent works related to NFRCs, including the manufacturing processes and parameters, characterization of mechanical properties, applications, and machinability and machining process monitoring, many for the first time. The main challenges associated with machining of NFRCs and the induced damages are outlined, with special attention paid to the effect of physical properties of the fibers and manufacturing process on the machinability, along with the essential machining parameters that affect the quality of the machined surface. The research perspectives and the current application status are also discussed. The article is intended to help readers attain a fundamental understanding of key technologies and the state of the arts in this research area. [ABSTRACT FROM AUTHOR]

This paper aims to investigate the effects of machining parameters such as spindle speed, feed rate and drill diameter on machinability of flax/poly(lactic acid) bio-composites, to analyze the relations among cutting forces, drilling-induced damages and crack propagation of the drilled samples. In particular, a set of drilling experiments were conducted using different drilling conditions and a new low-cost measurement set-up was developed to measure the cutting force during the drilling operation. In addition, the analysis of variance (ANOVA) was applied to identify the significance of each individual cutting parameter. The experimental results indicate the relation between the thrust force and the machinability parameters of flax fiber reinforced bio-composite. The increase in spindle speed reduces thrust force and delamination size of the drilled holes, whereas an increase in feed and drill diameter leads to a considerable increase in both thrust force and delamination factor. The effect of spindle speed on peripheral damage was not significant for the drills tested, though the feed rate was found to play the key role on the delamination damage area. The best hole quality was achieved with the samples drilled at spindle speed and feed rate of 3000 rpm and 0.11 mm/rev, respectively. [ABSTRACT FROM AUTHOR]

Understanding the influences of drilling parameters on machinability characteristics of NFRCs is of value to both academia and manufacturing industries. The present work investigates drilling-induced delamination and hole quality of fully biodegradable flax/poly(lactic acid) composites in response to cutting speed, feed rate, drill diameter, and drill material. Among various types of drilling-induced damages, exit-ply delamination is found to be the most destructive damage and cutting thrust force is identified as the main reason. The experimental results revealed that thrust force is highly dependent on drilling parameters where feed rate and drill material were found to be the most influential parameter on it. The choice of drill bit in terms of diameter and material has a considerable effect on the machinability. Drilling with HSS drills resulted in nearly 60% lower thrust force and better hole quality compared to that with carbide drills. While the size of delamination damage in the samples drilled at the lowest feed (0.02 mm/rev) sounds to be unrelated to cutting forces, the overall trend confirms that decreasing the value of thrust force leads to lowering delamination damages. Hence, an effective strategy in avoiding such undesirable defects is calculating the critical thrust force below which delamination is negligible. An analytical model is developed based on the theory of virtual work to determine the critical thrust force at delamination onset. Through validating with experimental data, the proposed model agrees better with a more accurate prediction than existing models. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study on abrasive waterjet (AWJ) drilling of Inconel 718 and AISI 1040 steel, with an aim to understand the AWJ hole drilling performance and to investigate the process monitoring strategy by using acoustic emission (AE). In the experiments, pressure levels were set from 200 to 350 MPa at intervals of 50 MPa, and drilling time between 5 and 30 s at intervals of 5 s. It is found that the hole depth increases along with the drilling time at a slowing down penetration rate. A higher pressure results in a higher penetration rate. While there exists a direct influence of the material property on the drilling efficiency, it has limited significance with regard to the diameter of the hole produced. AE-RMS signal is demonstrated to be able to reflect the decreasing trend of the penetration rate and the influence of the pressure. A strong correlation between the penetration rate and AE-RMS values in AWJ hole drilling is shown through correlation analysis. The AE-RMS is also substantially affected by the material properties. A discrepancy of the AE-RMS under similar condition may suggest an anomaly in this cutting run. [ABSTRACT FROM AUTHOR]

Development of environment-friendly natural fiber composites has been a recent trend. However, due to the fact that natural fibers permit high level of moisture absorption from the surroundings, it can lead to weak bindings and degradation of composite properties. This paper presents an experimental study on the dynamic performance of flax fiber composite beams manufactured at different relative humidity (RH) levels. Five types of flax fiber-reinforced composite materials were made under different RH values, i.e., dry, 35%, 50%, 70%, and 95% RH, and beam samples were prepared using the composite. Impact hammer testing was conducted to measure the natural frequencies and damping of the beams. It was found that for the first three modes, while the resonant frequencies are very close for most samples, there is a clear drop of frequencies for the composite fabricated at 95% RH. Along with an increase of the RH level, the damping ratios for all the three modes have reported a slight increase, but the variation is not significant. [ABSTRACT FROM AUTHOR]

Chatter is a self-excited vibration that affects the part quality and tool life in the machining process. This paper introduces an intelligent chatter detection method based on image features and the support vector machine. In order to reduce the background noise and highlight chatter characteristics, the average FFT is applied to identify the dominant frequency bands that divide the time-frequency image of the short-time Fourier transform into several sub-images. The non-stationary properties of the machining conditions are quantified using sub-images features. The area under the receiver operating characteristics curve ranks the extracted image features according to their separability capabilities. The support vector machine is designed to automatically classify the machining conditions and select the best feature subset based on the ranked features. The proposed method is verified by using dry micro-milling tests of steel 1040 and high classification accuracies for both the stable and unstable tests are obtained. In addition, the proposed method is compared with two additional methods using either image features from the continuous wavelet transform or time-domain features. The results present a better classification performance than the two additional methods, indicating the efficiency of the proposed method for chatter detection. [ABSTRACT FROM AUTHOR]

This paper introduced how a mechanical engineering course was redesigned by applying experiential learning theory to improve student engagement and learning experience. Design of machine elements has been considered by students to be a difficult course. Traditional teaching methods tend not to be effective in engaging students. Experiential learning is a philosophy of learning by doing. In applying an experiential learning approach to the course, the design project and workshop activities were restructured in such a way that students were engaged in direct experience and focused reflection to construct knowledge. By exploring a physical gearbox model, working on subtasks for the design project, having group discussions, raising questions, getting feedback, and moving forward for next subtasks, students experienced the learning cycles of 'DO, OBSERVE, THINK, and PLAN' many times. Course survey results showed that application of experiential learning helped to improve student engagement and learning experience significantly. [ABSTRACT FROM AUTHOR]

Abstract Chatter is a cause of low surface quality and productivity in milling and crucial features need to be extracted for accurate chatter detection and suppression. This paper introduces a novel feature extraction approach for chatter detection by using image analysis of dominant frequency bands from the short-time Fourier transform (STFT) spectrograms. In order to remove the environmental noises and highlight chatter related characteristics, dominant frequency bands with high energy are identified by applying the squared energy operator to the synthesized fast Fourier transform (FFT) spectrum. The time-frequency spectrogram of the vibration signal is divided into a set of grayscale sub-images according to the dominant frequency bands. Statistical image features are extracted from those sub-images to describe the machining condition and assessed in terms of their separability capabilities. The proposed feature extraction method is verified by using dry milling tests of titanium alloy Ti6Al4V and compared with two existing feature extraction techniques. The results indicate the efficiency of the time-frequency image features from dominant frequency bands for chatter detection and their better performance than the time domain features and wavelet-based features in terms of their separability capabilities. Highlights • Time-frequency image features is applied for chatter detection in milling. • Frequency bands are identified to highlight chatter characteristics. • AUC is used to assess image features in terms of their separability capabilities. • Image features are efficient to discriminate stable and unstable tests. [ABSTRACT FROM AUTHOR]

Chatter detection in metal machining is important to ensure good surface quality and avoid damage to the machine tool and workpiece. This paper presents an intelligent chatter detection method in a multi-channel monitoring system comprising vibration signals in three orthogonal directions. The method comprises three main steps: signal processing, feature extraction and selection, and classification. The ensemble empirical mode decomposition (EEMD) is used to decompose the raw signals into a set of intrinsic mode functions (IMFs) that represent different frequency bands. Features extracted from IMFs are ranked using the Fisher discriminant ratio (FDR) to identify the informative IMFs, and those features with higher FDRs are selected and presented to a support vector machine for classification. Single-channel strategies and multi-channel strategies are compared in low immersion milling of titanium alloy Ti6Al4V. The results demonstrate that the two-channel ( Ay , Az ) strategies based on signal processing and feature ranking/selection give the best performance in classification of the stable and unstable tests. [ABSTRACT FROM AUTHOR]

The prognosis of paraquat (PQ) poisoning is highly correlated to plasma PQ concentration, which has been identified as the most important index in PQ poisoning. This study investigated the predictive value of coagulation, liver, and kidney indices in prognosticating PQ-poisoning patients, when aligned with plasma PQ concentrations. Coagulation, liver, and kidney indices were first analyzed by variance analysis, receiver operating characteristic curves, and Fisher discriminant analysis. Then, a new, intelligent, machine learning-based system was established to effectively provide prognostic analysis of PQ-poisoning patients based on a combination of the aforementioned indices. In the proposed system, an enhanced extreme learning machine wrapped with a grey wolf-optimization strategy was developed to predict the risk status from a pool of 103 patients (56 males and 47 females); of these, 52 subjects were deceased and 51 alive. The proposed method was rigorously evaluated against this real-life dataset, in terms of accuracy, Matthews correlation coefficients, sensitivity, and specificity. Additionally, the feature selection was investigated to identify correlating factors for risk status. The results demonstrated that there were significant differences in the coagulation, liver, and kidney indices between deceased and surviving subjects (p<0.05). Aspartate aminotransferase, prothrombin time, prothrombin activity, total bilirubin, direct bilirubin, indirect bilirubin, alanine aminotransferase, urea nitrogen, and creatinine were the most highly correlated indices in PQ poisoning and showed statistical significance (p<0.05) in predicting PQ-poisoning prognoses. According to the feature selection, the most important correlated indices were found to be associated with aspartate aminotransferase, the aspartate aminotransferase to alanine ratio, creatinine, prothrombin time, and prothrombin activity. The method proposed here showed excellent results that were better than that produced based on blood-PQ concentration alone. These promising results indicated that the combination of these indices can provide a new avenue for prognosticating the outcome of PQ poisoning. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study of the effects of cutting parameters of micro-end-milling process on the machined surface roughness, in order to find the optimal operation conditions for improved surface finish. Three types of metals, namely 6160 aluminium alloy, brass, and AISI 1040 steel, are used as work materials. The effect of material property on the surface roughness is investigated. It is found that under the same machining condition, the machined surface quality of aluminium alloy is the worst, while that of brass is the best. A multiple regression model for the surface roughness is developed, which includes the effects of cutting speed, feedrate, and the interaction between them. Results based on analysis of variance (ANOVA) show that the cutting speed is the most significant factor on surface roughness. The residual analysis indicates that the multiple regression model is valid and agrees with the experimental results. [ABSTRACT FROM PUBLISHER]

Modelling of the cutting forces in micromilling is challenging due to the size effect and existence of a minimum chip thickness. This paper presents the development of a cutting force model for micromilling of brass. The prediction of cutting forces derives from a simplified orthogonal process. A finite element (FE) model is employed to simulate two-dimensional cutting forces in orthogonal microcutting, with the ploughing and tool edge effect taken into consideration. The flow stress of workpiece material is modelled by using the Johnson–Cook constitutive material law. The FE model is used to evaluate the critical chip thickness and to extract the cutting force coefficients. The cutting force coefficients are modelled as a function of instantaneous uncut chip thickness, which is independent of cutting speed but influenced by tool edge radius. To rectify the issue of sharp increase of the force coefficients under very small uncut chip thickness, a critical uncut chip thickness value is introduced and the coefficients are adjusted by using a tangent slope for uncut chip thickness smaller than the critical value. A generalized analytical force model based on numerical findings is developed to predict the micromilling force by considering the tool trajectory and tool runout. The simulation results of micromilling forces are compared against experimental measurement, where an agreement of force trends is shown along with the increasing feedrate and depth of cut. [ABSTRACT FROM AUTHOR]

High resolution melting curve analysis (HRM) is an emerging new method for interrogating and characterizing DNA samples. It has been used as a powerful tool for gene mutation and single-nucleotide polymorphism (SNP) detection with high throughput and low cost. Commercially available HRM analysis systems are mostly proprietary and expensive. It lacks the flexibility for the end users and researchers to incorporate new analysis algorithms into the existing system. This paper presents the development of a MATLAB-based open software program for high resolution melting curve analysis. Key analysis functions, such as to obtain the first derivative curve using Savitzky–Golay filter, to identify the melt region, subtraction of background fluorescence and curve normalization, are introduced, followed by case studies of HRM analysis using the developed software program. [ABSTRACT FROM AUTHOR]

This paper presents an experimental study on abrasive waterjet (AWJ) machining of the most commonly used titanium alloy, Ti-6Al-4V. Two types of machining operations, i.e. drilling (or piercing) and slotting, were conducted. For the drilling experiments, the influences of water pressure and drilling time were investigated. It was found that both the hole depth and diameter increased as drilling time increased but in a decreasing rate. An increase in the water pressure increased both the hole depth and the hole diameter. For the slot cutting, the influence of water pressure and the traverse speed were investigated. A slower traverse speed resulted in a deeper depth of cut. The kerf showed a taper shape with a wider entry on top, and the width decreased as jet cut into the material. At the bottom of the kerf, a pocket was generated. The variation of the depth of cut became insignificant when the traverse speed was increased. [ABSTRACT FROM AUTHOR]

This paper presents a unique dual-sensor signal fusion technology for DSP (digital signal processor)-based wide range vibration detection and active vibration control (AVC), which aims to suppress wide range vibration disturbances up to sub-micron level. In this method, two accelerometer sensors with different measurement ranges and sensitivities are used to detect the vibration disturbances as coarse and fine sensors respectively, and feed the signals simultaneously to a DSP controller. Each sensor is responsible for detecting accelerations in a specific range. By proper incorporation of signals from the two sensors, it is possible to achieve a wide detection range of vibrations at low cost. Simulation study shows that in an AVC system with the proposed dual-sensor signal fusion approach as the vibration detection component, significant improvement can be achieved comparing with traditional single-sensor AVC systems. [ABSTRACT FROM AUTHOR]

Planetary gearboxes exhibit unique challenges in bearing fault detection. This paper presents a hybrid approach for fault diagnosis of planetary bearings using an internal vibration sensor and novel signal processing strategies. An accelerometer is mounted internally on the planet carrier to address the issues of variable transmission path. An effective bearing faults detection algorism is developed by employing several advanced signal processing techniques, including Cepstrum whitening, minimum entropy deconvolution (MED), spectral kurtosis (SK) and envelope analysis. The adverse effect of the electromagnetic interference in the signal due to the use of a slip ring is tackled by optimizing the SK technique for demodulation band selection. The proposed method is assessed by analyzing experimental data from a planetary gearbox test rig with seeded bearing faults. The result shows that the new method can effectively detect both inner race and outer race faults of the planetary bearing. [ABSTRACT FROM AUTHOR]

An analytical modelling approach based on Oxley's predictive machining theory is presented to evaluate the cutting forces, chip thickness and temperature distributions in the orthogonal cutting process. In this approach, the work material properties are modelled using the Johnson–Cook constitutive material law, which represents the flow stress of the material as a function of strain, strain rate, and temperature. For the determination of the tool-chip interface temperature, an evenly distributed rectangular heat source near the cutting edge is used instead of a plane heat source. The tool thermal model is simplified by neglecting the temperature variations along the tool-chip interface to avoid the high cost of computation time. Finite difference method is applied for solution of the thermal model. The performance of the developed model is validated with the experimental data in machining of steel1045. A comparison of the outputs from Oxley's original model and the modified model is provided. The model is further assessed by using two other materials,Al 6086-T6andTi6Al4V. Close agreements with experimental results have been shown. [ABSTRACT FROM PUBLISHER]

In production technology, abrasive particles are increasing being utilised in abrasive jet micromachining. Despite of the many phenomenological investigations, the resolution of the flow field and the description of physical interactions between the carrying and abrasive media have not been thoroughly understood. In this paper, a discrete element method–computational fluid dynamics (DEM–CFD) analysis is performed to better understand the dynamic characteristics of high speed abrasive air jet. Abrasive particles of micron sizes within the air jet are examined to determine the jet evolution and expansion as well as the particle distribution within the flow. In particular, particle sphericity (shape factor) is considered for the particle–fluid interaction in addition to the particle–particle collision. Air and particle velocities for the flow downstream from the nozzle exit are simulated under transient, turbulent, two-phase flow conditions and different inlet conditions. Simulations of the particle flow characteristics using a range of particle shape factors are compared against experimental data with different inlet pressures and nozzle diameters. It is found that the predicted results agree well with the experimental data with particle shape factors of 0.6 and 0.8, which correspond to “edged” and “rounded” abrasive particles. With the consideration of lower shape factors, the increased in the aerodynamic drag resulted in higher particle velocities that allows the abrasive particles to remain more centrally along the centreline of the jet axis, and in turn affects the jet expansion. Nevertheless, the shape factors of the abrasive particles have been found to exert no discernible effect on the high speed air flow. It is also shown that the variation of nozzle sizes has a negligible effect on the maximum air velocities. [ABSTRACT FROM AUTHOR]

This paper presents the development of a cutting force model for the micro-end-milling processes under various cutting conditions using a hybrid approach. Firstly, a finite element (FE) model of orthogonal micro-cutting with a round cutting edge is developed for medium-carbon steel. A number of finite element analyses (FEA) are performed at different uncut chip thicknesses and velocities. Based on the FEA results, the cutting force coefficients are extracted through a nonlinear algorithm to establish a relationship with the uncut chip thickness and cutting speed. Then, the cutting force coefficients are integrated into a mechanistic cutting force model, which can predict cutting forces under different cutting conditions. In order to account for the cutting edge effect, an effective rake angle is employed for the determination of the cutting force. A comparison of the prediction and experimental measured cutting forces has shown that the developed method provides accurate results. [ABSTRACT FROM AUTHOR]

This paper explores the development of an automatic model-centric version control approach for managing the evolution of chemical models and to support model reuse. Unlike traditional versioning which is text-based, the basis of versioning in the proposed approach is based on structural changes of the chemical models. An implemented prototype tool incorporating the proposed approach and the use of an example XML-based representation of chemical models was used to illustrate the associated concepts. Some results associated with a case study are presented. Given that chemical modelling tools like HYSYS has just delivered an XML infrastructure that aims to support collaborative engineering, this proposed technique is timely and relevant. The benefit of the approach is that it provides a way of automatic storage and retrieval of the chemical models as well as the management of the different versions of the model as it evolves, thus allowing chemical process engineers to concentrate on the modelling and simulation. [Copyright &y& Elsevier]

Considers the robust H infinity filtering problem for a general class of uncertain linear systems described by the integral quadratic constraints (IQC). Importance of the problem in many signal processing applications; Integral quadratic constraints; Solution to the problem using linear matrix inequality.

Chatter has always been a detrimental problem restricting the improvement of surface quality and processing efficiency in machining processes. Chatter detection is an essential approach to prevent these harmful effects, which aims to identify chatter occurrence at the early stage. Energy entropic methods explain chatter from randomness perspective of energy distribution and are often applied to detect chatter in machining processes. However, the energy of the milling system at the chatter frequency increase slowly at the onset of chatter, which lead to that energy entropy is sometimes challenging to identify weak chatter. In this paper, a new method based on combination of wavelet packet energy entropy and energy ratio is proposed to monitor chatter. By detecting the energy ratio of chatter frequency band, it is feasible to set a threshold to represent the different milling states and can accurately detect weak chatter. The proposed approach is assessed by using preliminary numerical simulation and several milling experiments, and the results demonstrate that the proposed method can effectively detect chatter. [ABSTRACT FROM AUTHOR]

The relationship between the self-similar propagation region for single pulse and oscillation region for a pair of self-similar pulses are first investigated in our paper. By introducing self-similar coefficient F and RMS width ratio K, we find that self-similar propagation region starts from z = 1.8LD to z = 18LD while F ≤ 10%. The optimum output of self-similar pulse is also achieved when F and K reach a minimum value simultaneously at z = 3.5LD. The sinusoidal fit oscillation region of self-similar pulse pair ranges from 5/8LD to 2LD while F varies from 40.27 to 7.99%, and the dark soliton fit oscillation region ranges from 2LD to 6LD while F varies from 7.99 to 5.32%, indicating that the sinusoidal fit oscillation region almost occurs before the pulses enter the self-similar propagation region and the dark soliton fit oscillation region occurs within the self-similar pulse propagation region. Furthermore, the oscillation characteristics of interacting pulses are also studied numerically by using split-step Fourier method. The results are beneficial in Dense Wavelength Division Multiplexing transmission system which is in heavy demands of light source in wide-range wavelength. [ABSTRACT FROM AUTHOR]

Abstract: The influence of different dispersion-distribution in dispersion-decreasing optical fiber with normal group-velocity dispersion (ND-DDF) on the generation and compression of self-similar optical beam is investigated in this paper. The split-step Fourier numerical method is adopted and the amplitude of the pulses' envelope under different distributed dispersion is determined numerically. The generation and compression of the self-similar optical beam are simulated and analyzed based on the perspective of chirp feature. The result shows that chirp nonlinear region and chirp linearity level in different dispersion distribution fiber affect the generation and compression of the self-similar optical beam significantly. The quality of obtained self-similar and compressed optical beam in different tapered fiber increases in order of: cosinoidally tapered DDF, linearly tapered DDF, exponentially tapered DDF and hyperbolically tapered DDF, from the lowest to the highest. Graphical abstract: [ABSTRACT FROM AUTHOR]

In this paper, a novel, separable two-degrees-of-freedom stage with high-precision motion and resolution is proposed for the application of vibration-assisted micromilling. A separable design was realized on the basis of the detachable structure of the platform. Flexible stages with different dimensions and types can be utilized in the devices. A circular-fillet hinge is selected as the flexible unit with a parallel structure to realize output decoupling and reduce the coupling error between the two vibration directions. Analytical modeling is conducted to explore the static and dynamic characteristics of the stage. Results reveal a good agreement with the finite element simulation result. A series of experiments were conducted to assess the static and dynamic performances of the flexible stage, encompassing tests such as amplitude response, motion trajectory, and coupling trajectory. The results of these tests revealed that the designed vibration-assisted system exhibits precise movement capabilities.Highlights: A novel separable vibration-assisted stage with high-precision motion and resolution is proposed, in which flexible stages with different dimensions and sizes can be utilized. The test results show that the coupling error of the 1D stage is much lower than the 2D stage in one-direction vibration. The test results show that the coupling error of the 1D stage is much lower than the 2D stage in one-direction vibration. [ABSTRACT FROM AUTHOR]

An efficient stability analysis contributes to the improvement of machining stability and chatter suppression. First, this paper presents three chatter detection approaches that were developed on the application of wavelet transforms. Second, the feasibility of the methods for chatter detection is verified by combining numerical simulations and experimental research. Finally, the recognition performance of the three methods is compared. The analysis results indicate that the proposed three methods can distinguish different machining states. The p-leader multifractal method (PLMM) provides the best recognition performance but takes the longest time, the wavelet leader multifractal method (WLMM) comes second, and the wavelet packet method (WPM) is the worst but takes the shortest amount of time. Therefore, the PLMM can be used for identifying signals with high accuracy requirements, whereas the WLMM or WPM can be used otherwise.Highlights: Three methods for judging the stability of micro milling process are proposed by using wavelet packet method (WPM), wavelet leaders multifractal method (WLMM) and p-leader multifractal method (PLMM). The feasibility of WPD, WLMM, and PLMM in stability analysis was preliminarily verified through numerical simulation. The recognition performance of WPD, WLMM, and PLMM for chatter detection is compared. [ABSTRACT FROM AUTHOR]

The purpose of the work is to evaluate various intrinsic noise sources that limit the charge resolution of microelectromechanical systems (MEMS) vibrating-reed (VR) electrometers. We demonstrate a method of reducing total equivalent referred-to-input (RTI) noise voltage per square root hertz for an operational amplifier-based preamplifier (preamp) circuit topology. The proposed circuit’s noise model accurately describes the various noise sources and total RTI noise voltage is experimentally verified. The noise analysis clarifies the path toward a preamp with a noise floor limited by thermal noise due to low-valued shunt resistance at its input. Furthermore, during the charge measurement, a variation of sensitivity occurs by changing the isolation (or dc blocking) capacitor of the preamp. This phenomenon also affects the noise performance of the circuit and the overall charge resolution for the electrometer. RTI noise voltage of 290 nV/ $\surd $ Hz at 5.2 kHz was measured for 50 $\text{M}\Omega $ of shunt resistor and 2 pF of isolation capacitor to implement a prototype of the complete preamp, along with the MEMS device fabricated in silicon-on-glass (SOG) process. Finally, we present a charge resolution model of the proposed SOG-MEMS electrometry system. The model’s underlying design principles reveal low noise preamps accessible to the circuit designers without tangible circuitries and after-the-fact noise measurement. [ABSTRACT FROM AUTHOR]

This study proposes a new kernel extreme learning machine (KELM) parameter tuning strategy using a novel swarm intelligence algorithm called grey wolf optimization (GWO). GWO, which simulates the social hierarchy and hunting behavior of grey wolves in nature, is adopted to construct an effective KELM model for bankruptcy prediction. The derived model GWO-KELM is rigorously compared with three competitive KELM methods, which are typical in a comprehensive set of methods including particle swarm optimization-based KELM, genetic algorithm-based KELM, grid-search technique-based KELM, extreme learning machine, improved extreme learning machine, support vector machines and random forest, on two real-life datasets via 10-fold cross validation analysis. Results obtained clearly confirm the superiority of the developed model in terms of classification accuracy (training, validation, test), Type I error, Type II error, area under the receiver operating characteristic curve (AUC) criterion as well as computational time. Therefore, the proposed GWO-KELM prediction model is promising to serve as a powerful early warning tool with excellent performance for bankruptcy prediction. [ABSTRACT FROM AUTHOR]

Among the diverse challenges in machining processes, chatter has a significant detrimental effect on surface quality and tool life, and it is a major limitation factor in achieving higher material removal rate. Early detection of chatter occurrence is considered a key element in the milling process automation. Online detection of chatter onset has been continually investigated over several decades, along with the development of new signal processing and machining condition classification approaches. This paper presents a review of the literature on chatter detection in milling, providing a comprehensive analysis of the reported methods for sensing and testing parameter design, signal processing and various features proposed as chatter indicators. It discusses data-driven approaches, including the use of different techniques in the time–frequency domain, feature extraction, and machining condition classification. The review outlines the potential of using multiple sensors and information fusion with machine learning. To conclude, research trends, challenges and future perspectives are presented, with the recommendation to study the tool wear effects, and chatter detection at dissimilar milling conditions, while utilization of considerable large datasets—Big Data—under the Industry 4.0 framework and the development of machining Digital Twin capable of real-time chatter detection are considered as key enabling technologies for intelligent manufacturing. [ABSTRACT FROM AUTHOR]

The arterial blood gas (ABG) test is used to assess gas exchange in the lung, and the acid-base level in the blood. However, it is still unclear whether or not ABG test indexes correlate with paraquat (PQ) poisoning. This study investigates the predictive value of ABG tests in prognosing patients with PQ poisoning; it also identifies the most significant indexes of the ABG test. An intelligent machine learning-based system was established to effectively give prognostic analysis of patients with PQ poisoning based on ABG indexes. In the proposed system, an enhanced support vector machine combined with a feature selection strategy was developed to predict the risk status from a pool of 103 patients (56 males and 47 females); of these, 52 subjects were deceased and 51 patients were alive. The proposed method was rigorously evaluated against the real-life dataset in terms of accuracy, sensitivity, and specificity. Additionally, the feature selection was investigated to identify correlating factors for the risk status. The results demonstrated that there were significant differences in ABG indexes between deceased and alive subjects ( p -value < 0.01). According to the feature selection, we found that the most important correlated indexes were associated with partial pressure of carbon dioxide (PCO 2 ). This study discovered the relationship between ABG test and poisoning degree to provide a new avenue for prognosing PQ poisoning. [ABSTRACT FROM AUTHOR]

The early identification of toxic paraquat ( PQ) poisoning in patients is critical to ensure timely and accurate prognosis. Although plasma PQ concentration has been reported as a clinical indicator of PQ poisoning, it is not commonly applied in practice due to the inconvenient necessary instruments and operation. In this study, we explored the use of blood routine indexes to identify the degree of PQ toxicity and/or diagnose PQ poisoning in patients via machine learning approach. Specifically, we developed a method based on support vector machine combined with the feature selection technique to accurately predict PQ poisoning risk status, then tested the method on 79 (42 male and 37 female; 41 living and 38 deceased) patients. The detection method was rigorously evaluated against a real-world data set to determine its accuracy, sensitivity and specificity. Feature selection was also applied to identify the factors correlated with risk status, and the results showed that there are significant differences in blood routine indexes between dead and living PQ-poisoned individuals ( p-value < 0.01). Feature selection also showed that the most important correlated indexes are white blood cell and neutrophils. In conclusion, the toxicity or prognosis of PQ poisoning can be preliminarily ascertained by blood routine testing without PQ concentration data, representing an additional tool and innovative approach to assess the prognosis of PQ poisoning. [ABSTRACT FROM AUTHOR]

Quadrotor type helicopters continue to grow in popularity for academic research and unmanned aerial vehicle applications. However, the model uncertainties caused by battery voltage drop, payload variation and flight condition change, have rarely been considered in control design. This work proposes a quantitative feedback theory based robust design approach to deal with these uncertainties. By analyzing the rigid body dynamics and aerodynamic forces/moments under different voltages, payloads and flight conditions, we model quadrotor dynamics as a set of linear models with parameter uncertainties, which represent a larger flight envelop than models linearized from hover condition. These model uncertainties, as well as the robust stability requirements and performance specifications of flight control system are then used for designing and tuning the controllers, to perform tradeoff between controller complexity, robust requirements, and performance specifications. We also implemented a prototype system, and conducted a serial of experiments in realtime outdoor flights to evaluate its performance. The results show good, robust, and reliable performances of the designed system in autonomous hovering, takeoff, waypoint navigation and landing flights. [ABSTRACT FROM AUTHOR]

Moisture present in plant fibres is considered to be detrimental to the performance of composites. In general, a drying stage is performed on the plant fibre fabrics before manufacturing the composites since it is seemed to allow better output. This work provides an analysis of the effect of moisture in flax fibres on the overall quality of epoxy/flax biocomposites. Flax fibre fabrics were conditioned at different relative humidity (RH) environments and composites were manufactured by vacuum infusion technique. Composites were characterized by mechanical and microstructural analysis. Results showed that manufacturing composites with highly humid fabrics (95% RH) generates post processing deformation of finished parts and also leads to poor microstructural quality. The moisture in the fibres with different RH reduced the stiffness (from 23.74 to 17.67 GPa for Young's modulus and from 16.28 to 11.82 GPa for flexural modulus) but increased their fracture strain (from 1.87 to 2.64). Tensile strength displayed an optimum value (287.96 MPa) for fabrics conditioned at 50% RH, but flexural strength decreases continuously from 225.12 to 152.34 MPa as the moisture in the fabric increases. [ABSTRACT FROM AUTHOR]

Background and Objective: In countries with high prevalence of tuberculosis (TB), clinicians often diagnose tuberculous pleural effusion (TPE) by using diagnostic tests, which have not only poor sensitivity, but poor availability as well. The aim of our study is to develop a new artificial intelligence based diagnostic model that is accurate, fast, non-invasive and cost effective to diagnose TPE. It is expected that a tool derived based on the model be installed on simple computer devices (such as smart phones and tablets) and be used by clinicians widely. Methods: For this study, data of 140 patients whose clinical signs, routine blood test results, blood biochemistry markers, pleural fluid cell type and count, and pleural fluid biochemical tests’ results were prospectively collected into a database. An Artificial intelligence based diagnostic model, which employs moth flame optimization based support vector machine with feature selection (FS-MFO-SVM), is constructed to predict the diagnosis of TPE. Results: The optimal model results in an average of 95% accuracy (ACC), 0.9564 the area under the receiver operating characteristic curve (AUC), 93.35% sensitivity, and 97.57% specificity for FS-MFO-SVM. Conclusions: The proposed artificial intelligence based diagnostic model is found to be highly reliable for diagnosing TPE based on simple clinical signs, blood samples and pleural effusion samples. Therefore, the proposed model can be widely used in clinical practice and further evaluated for use as a substitute of invasive pleural biopsies. [ABSTRACT FROM AUTHOR]

Active techniques that pump energy into MEMS mechanical resonant devices to improve their performance have attracted great research attention. Herein, we introduce a new concept of bi-layered monolithic silicon carbide resonators utilizing thermal-piezoresistive pumping to boost the quality factor. The device operates based on the electrothermal actuation as a result of a superimposed alternating and direct voltages. The structural stress modulates the electrothermal force generated in the device through the piezoresistive effect. Due to the negative piezoresistive coefficient of the actuator, the mechanical vibration of the structure is fed from the DC bias applied to the structure. The unique design of the double SiC layer allow energy pumped into the system via the thermal-piezoresistive coupling in highly doped SiC nano-film, enabling the enhancement of effective quality factor up to 15.5 %, from 12,200 to 14,100. The change in frequency related to the applied power was measured to be less than 1 % of the designed value. The saturation threshold of the pumping effect was reached at an applied power of 0.18 W. This works provides an avenue to improve the effective quality factor in MEMS bridge structure resonators by the coupling of the thermal-piezoresistive pumping and electrothermal actuation. [Display omitted] • The design allows the application of thermal piezoresistive pumping to enhance the effective quality factor. • Highly reliable and stable electrothermal actuation of SiC MEMS resonators has been achieved. • Effective quality factor tuning by electrothermal actuation on an out-of-plane vibration mode of n-type SiC resonator. [ABSTRACT FROM AUTHOR]

The selection of the optimal demodulation frequency band is a significant step in bearing fault diagnosis because it determines whether the fault information can be extracted from the demodulated signal via envelope analysis. Two well-known methods for selecting the demodulation band are the Fast Kurtogram, based on the kurtosis of the filtered time signal, and the Protrugram, which uses the kurtosis of the envelope (amplitude) spectrum. Although these two methods have been successfully applied in many cases, the authors have observed that they may fail in specific environments, such as in the presence of electromagnetic interference (EMI) or other impulsive masking signals. In this paper, a simple spectral kurtosis-based approach is proposed for selecting the best demodulation band to extract bearing fault-related impulsive content from vibration signals contaminated with strong EMI. The method is applied to vibration signals obtained from a planetary gearbox test rig with planet bearings seeded with inner and outer race faults. Results from the Fast Kurtogram and Protrugram methods are also included for comparison. The proposed approach is found to exhibit superior diagnostic performance in the presence of intense EMI. Another contribution of the paper is to introduce and explain the issue of EMI to the condition monitoring community. The paper outlines the characteristics of EMI arising from widely-used variable frequency drives, and these characteristics are used to simulate an EMI-contaminated vibration signal to further test the performance of the proposed approach. Although EMI has been acknowledged as a serious problem in many industrial cases, there have been very few studies showing its adverse effects on machine diagnostics. It is important for analysts to be able to identify EMI in measured vibration signals, lest it interfere with the analysis undertaken. [ABSTRACT FROM AUTHOR]

In this paper, a new support vector machines (SVM) parameter tuning scheme that uses the fruit fly optimization algorithm (FOA) is proposed. Termed as FOA-SVM, the scheme is successfully applied to medical diagnosis. In the proposed FOA-SVM, the FOA technique effectively and efficiently addresses the parameter set in SVM. Additionally, the effectiveness and efficiency of FOA-SVM is rigorously evaluated against four well-known medical datasets, including the Wisconsin breast cancer dataset, the Pima Indians diabetes dataset, the Parkinson dataset, and the thyroid disease dataset, in terms of classification accuracy, sensitivity, specificity, AUC (the area under the receiver operating characteristic (ROC) curve) criterion, and processing time. Four competitive counterparts are employed for comparison purposes, including the particle swarm optimization algorithm-based SVM (PSO-SVM), genetic algorithm-based SVM (GA-SVM), bacterial forging optimization-based SVM (BFO-SVM), and grid search technique-based SVM (Grid-SVM). The empirical results demonstrate that the proposed FOA-SVM method can obtain much more appropriate model parameters as well as significantly reduce the computational time, which generates a high classification accuracy. Promisingly, the proposed method can be regarded as a useful clinical tool for medical decision making. [ABSTRACT FROM AUTHOR]

A computational model using the discrete element method is presented to investigate the impact process of multiple micro-particles on a quartz crystal. It is shown that the impacts initiate median and lateral cracks that degrade the strength of the substrate material and facilitate material removal in subsequent impacts. Elemental materials are removed through breaking their contacts with all adjacent elements by crack propagation and intersection. A larger overlapping impact condition between two impacts is more efficient for material removal in the second impact, but a total overlap does not yield the maximum material removal. The number of micro-cracks and the thickness of the cracked layer left on the eroded specimen are also studied in relation to the impact conditions. [ABSTRACT FROM AUTHOR]

The enhancement of digital color images needs to be performed in accordance with human perception in terms of hue, saturation, and intensity attributes instead of improving only the contrast. Two approaches were developed in this work, which use a correlated adjustment mechanism incorporating intensity and saturation attributes and provide contrast and saturation enhancements together with brightness consistency. In these algorithms, object edges are emphasized for contrast, and image saturation is increased by boosting the salient regions. Furthermore, intensity and saturation enhancements are carried out in a lattice structure where adjustments are made inter-related for better performance. Experiments were conducted with benchmark and real-world images. Results had shown improvements in image qualities both qualitatively and quantitatively. [ABSTRACT FROM AUTHOR]

A computational model using the discrete element method is presented to investigate the high velocity micro-particle impact process on a quartz crystal. The kinetic energy transfer from the impact particles to the target material is discussed. It shows that within the conditions considered 60–88% of the impact energy is consumed for crack formation and propagation, and the initiation of micro-cracks by an impact is mainly attributed to the shear stresses, while tensile stresses create more lateral and median cracks in the subsurface of the target than shear stresses. It also shows that a smaller impact angle with a lower particle velocity within the range considered in this study yields less subsurface damage to the target and also lower material erosion. [ABSTRACT FROM AUTHOR]

Abstract: A computational model for the impacts of ultrahigh velocity micro-particles on steels, at the conditions relevant to abrasive waterjet (AWJ) machining, is developed using the AUTODYN software. By introducing the work hardening effect at the strain rate above 104 s−1 and Bao–Wierzbicki fracture locus into Johnson–Cook material models, the developed model can more realistically reflect the material behaviour subject to ultrahigh velocity micro-particle impacts. An experiment with high velocity (350–700m/s) particle impacts on a high tensile steel was conducted, and the resulting crater volumes were measured and used to assess the computational model. It is found that the average error of the simulated crater volumes from the corresponding experimental data is within 10%. Based on the developed model, the transfer from impact energy to plastic work, and finally to crater volume is studied, along with the relation between plastic deformation, crater volume and material removal as well as the effect of impact variables and the target material yield strength on the impact behaviours. Three material failure modes (failures induced by inertia, elongation and adiabatic shear banding) are identified for spherical particle impacts. It is shown that while the crater formation is caused by plastic deformation near the impact site, the material removal process is associated with ductile failure mechanisms which are not only dependent on the magnitude of the plastic strain, but also the stress and thermal conditions. [Copyright &y& Elsevier]

• Microgrooves are fabricated on glass surface using DLA and LIPAA processing methods. • The morphology of microgrooves fabricated by two methods has been compared. • The influence of laser parameters on morphology of microgrooves has been explored. • The differences in glass chemical components under two methods have been analyzed. This paper presents a comparative study of direct laser ablation (DLA) and laser-induced plasma-assisted ablation (LIPAA) methods to fabricate single-channel microgrooves on the surface of glass substrate by picosecond laser irradiation. The comparative study focuses on the morphological feature and surface chemical components of the micro-groove generated. For this purpose, optical microscope, scanning electron microscope (SEM) and X-ray photoelectron spectroscopy (XPS) are utilized to characterize the changes of morphologic and chemical compositions. The influence of laser parameters on the morphology of the glass surface is explored. For DLA, the microgroove with the least quantity and size of cracks is prepared successfully by setting the parameters as: scanning speed of 1 mm/s at laser output power of 3 W. In case of LIPAA, microgroove with no cracks and greater depth is achieved by setting the parameters as: scanning speed of 1 mm/s at laser output power of 1 W and distance between glass substrate and copper target at 0.06 mm. The result of XPS demonstrates that there is no significant change in chemical elements on glass surface after DLA, while there is Cu element deposits on glass surface after LIPAA. [ABSTRACT FROM AUTHOR]

Tuning the natural frequency of a resonator is an innovative approach for the implementation of mechanical resonators in a broad range of fields such as timing applications, filters or sensors. The conventional electrothermal technique is not favorable towards large tuning range because of its reliance on metallic heating elements. The use of metallic heaters could limit the tuning capability due to the mismatch in thermal expansion coefficients of materials forming the resonator. To solve this drawback, herein, the design, fabrication, and testing of a highly-doped SiC bridge resonator that excludes the use of metallic material as a heating element has been proposed. Instead, free-standing SiC structure functions as the mechanical resonant component as well as the heating element. Through the use of the Joule heating effect, a frequency tuning capability of almost ∆f / f o ≈ 80% has been demonstrated. The proposed device also exhibited a wide operating frequency range from 72.3 kHz to 14.5 kHz. Our SiC device enables the development of highly sensitive resonant-based sensors, especially in harsh environments. Unlabelled Image • Free-standing silicon carbide (SiC) bridges function as both resonant structure and heating element. • Ultra-large frequency tuning capability of almost 80% was demonstrated for SiC resonators by thermal stress. • Frequency tunability of SiC micromechanical resonators is dependent on stress induced by Joule heating. [ABSTRACT FROM AUTHOR]

• An exactly mechanics cutting force model is established in micro end-milling. • Tool trajectory, tool runout, the minimum chip thickness and the material's elastic recovery are considered in the determination of IUCT. • A new model to rectify the issue of tool runout effect on the cutting force. • Force coefficients as nonlinear function of the IUCT are extracted by FE simulation of micro orthogonal cutting. Prediction of cutting force has great significance for controlling the micro-end-milling processes. In this study, a mechanics model for exactly prediction cutting force is comprehensively established by considering the variety of entry and exit angles for each engaged cutting edge and an accurate instantaneous uncut chip thickness (IUCT). The determination of IUCT has considered the combination of the minimum chip thickness, tool run-out, and the material's elastic recovery, which is embedded in the cutting force model. Further, cutting force coefficients as function of uncut chip thickness have been calculated by using finite element method (FEM). To verify the reliability of the presented cutting force model, a series of experiments for cutting force are conducted and experimental results are compared to cutting force predicted. The results demonstrate that the cutting force predicted is well in agreement with that of measured. The effects of elastic recovery and tool run-out on cutting force also are investigated. Some conclusion can be drawn that elastic recovery can more obviously affect the cutting force predicted with smaller the feed per tooth, the errors of experimental and predicted is getting smaller with increasing the cutting depth, the slight change of tool run-out will lead to a great variation in cutting force. Image, graphical abstract [ABSTRACT FROM AUTHOR]