Machining Techniques Research Articles

Influence of Ceramic Particles Additives on the Mechanical Properties and Machinability of Carbon Fiber/Polymer Composites

Hybrid polymer composites have become the interest of the world, especially in mechanical and electronic applications. Recently, advanced machining techniques such as abrasive water jet machining of hybrid polymer composites have been used to solve many problems including the ability to form complex shapes, high performance, better surface features, and high levels of accuracy. In this study, the effect of ceramic particle (SiC and Al2O3) constituents on the mechanical properties and machinability of the hybrid polymer composites was investigated. The results of mechanical tests showed that an improvement in the tensile and flexural properties appeared using a hybrid polymer constituent (70 wt% Epoxy + 15 wt% CF + 10 wt% Sic + 5 wt% Al2O3). However, this hybrid polymer constituent includes the lowest value of impact strength. Also, the morphological analysis indicated the uniform distribution of particles, the best defect-free surface, and the bonding strength between the reinforcement and the epoxy matrix can be obtained using this constituent which has contributed to the improvement of the mechanical properties. The abrasive water jet automation process was also performed based on the design of experiment (Taguchi L18 design) to study the machinability the hybrid polymer composites in terms of surface roughness, hardness, and kerf width. The analysis of variance concluded that the constituent (70 wt% Epoxy + 15 wt% CF + 10 wt% Sic + 5 wt% Al2O3) has the most influential factor on the machinability of the hybrid polymer composite followed by traverse velocity, and then stand of distance that used during abrasive water jet machining. Moreover, the grey relational analysis was beneficially used to determine the multi-optimization of abrasive water jet machining parameters.

Experimental modelling and optimisation of WECSC of polymer nanocomposite

ABSTRACT Slicing of material using a thin wire reduces the loss of material and provides better cutting quality compared to any other machining techniques. Moving wire electrochemical spark machining (WECSM) is a hybrid, non-conventional wire slicing process, specially for non-conducting materials such as ceramics, composites and glasses. In this research article, alumina epoxy nanocomposite (AENC) has been taken as a workpiece. AENC is a polymer-based nanocomposite with a property of mixed nature such as ceramics and polymer. Due to its high electrical insulation performance, AENC is in demand in various applications. However, its mixed properties limit the machining capabilities for various applications. In the present work, modelling of the moving WECSM process has been performed using response surface methodology, and GRA with entropy method was applied for finding the optimum set of parameters. The results show that the optimal machining performances for material removal rate, surface roughness and kerf taper were obtained at 55 V applied voltage, 2.5 m/min wire velocity, 200 g/l electrolyte concentration and 200 µs pulse-on time.

Software Defined Networking Based Detection of DDoS Attacks

Software-defined networking (SDN) separates network management from data-traffic routes. More businesses are adopting it because of its flexibility, adaptability, and ability to improve traffic movement. SDN, or security-by-design, might be a desirable alternative for securing networks. While there have been many advancements in SDN technology, it is still susceptible to DDoS assaults. The growing frequency and scope of DDoS attacks pose a threat to network security, despite the availability of various methods for detecting and countering such attacks. There are two main methods to spot a distributed denial of service (DDoS) attack: signature recognition and abnormality detection. When personal characteristics such as fingerprints or iris scans are used to verify identity. Anomaly-based detection, which relies on network behavior, employs machine learning methods. We present a strategy for SDNs to identify DDoS assaults in this article. In the proposed architecture, DDoS attacks are detected using the Advanced Support Vector Machine (ASVM) technique. When compared to the SVM method, ASVM has the benefit of significantly less testing and training time. To evaluate the effectiveness of the suggested system, we use the Hierarchical Task Analysis (HTA) method of measuring human error.

An automated drug dependence detection system based on EEG

ObjectiveSubstance abuse causes damage to the brain structure and function. This research aim is to design an automated drug dependence detection system based on EEG signals in a Multidrug (MD) abuser. MethodsEEG signals were recorded from participants categorized into MD-dependents (n = 10) and Healthy Control (HC) (n = 12). The Recurrence Plot investigates the dynamic characteristics of the EEG signal. The entropy index (ENTR) measured from the Recurrence Quantification Analysis was considered the complexity index of the delta, theta, alpha, beta, gamma, and all-band EEG signals. Statistical analysis was performed by t-test. The support vector machine technique was used for the data classification. ResultsThe results show decreased ENTR indices in the delta, alpha, beta, gamma, and all-band EEG signal and increased theta band in MD abusers compared to the HC group. That indicated the reduction of complexity in the delta, alpha, beta, gamma, and all-band EEG signals in the MD group. Additionally, the SVM classifier distinguished the MD group from the HC group with 90% accuracy, 89.36% sensitivity, 90.7% specificity, and 89.8% F1 score. Conclusions and significanceThe nonlinear analysis of brain data was used to build an automatic diagnostic aid system that could identify HC people apart from those who abuse MD.

Multi-dimension SVPWM-based sensorless control of 7-phase PMSM drives

A new control technique for the 7-phase permanent magnet synchronous machine (PMSM) when a failure in the speed sensor is introduced. This will make the whole drive system more robust and at the same time reduce the cost. The speed and the position of the shaft of the motor are obtained by tracking the saturation saliency of the 7-phase motor when a failure in speed sensor is occurred. The proposed saliency-tracking algorithm is based on measuring the derivative of the stator currents of the 7-phase motor after the switching of the insulated gate bipolar transistor (IGBT) of the 7-phase inverter due to the implementation of the multi-dimension space vector pulse width modulation (SVPWM). This modulation technique is used in the 7-phase drives to suppress the 3rd and 5th harmonics. Simulation results show that the 7-phase motor drive could track the reference speed at different load conditions when a failure in the speed sensor is occurred without compromising the performance.

Evaluating face recognition with different texture descriptions and convolution neural network

Extracting the remarkable attributes of the image objects is an issue of ongoing research special in the face recognition problem. This paper presents two directions. The first is a comparison between the local binary patterns (LBP) and its modified center symmetric LBP drawn from localized facial expressions and due to the efficiency, K-nearest neighbor (KNN) and the support vector machine (SVM) techniques play significant roles in this research used to implement the proposed system efficiently. The second direction proposes an efficient architecture by depending on deep learning convolution neural network (CNN) to implement face recognition. Such a design consists of two parts: a convolutional learning feature model and a classification model. The first one learns the important feature,while the second part produces a score class for each sample input. Many experiments are implemented on the known dataset once for the number of nearest neighbors (K value), and then decrease the number of expression samples for each individual the other time. The cross-validation method is used to provide a true picture of the accuracy of the face recognition system. In all experiment results, the center symmetric LBP with KNN outperforms the classic LBP. While significant progress in the results accuracy recognition ratio of the CNN model compared with other methods used.

Role of biodegradable dielectrics toward tool wear and dimensional accuracy in Cu-mixed die sinking EDM of Inconel 600 for sustainable machining

NetZero advancements for the manufacturing industry are aspiring for precision machining of difficult-to-cut materials with a high degree of accuracy, particularly for Ni-based super alloys while meeting climate protocols. The applications and properties of the foregoing materials warrant the usage of the electric discharge machining (EDM) technique. However, given the rising scarcity of resources, there is a compelling need to make this process sustainable. The standard dielectric (kerosene) employed in EDM is hazardous to one's health and fails in terms of long-term productivity. As a result, an effort has been made in this study to evaluate the potential of biodegradable dielectrics, at different copper powder concentrations for sustainable machining of Inconel 600. This investigation evaluated three powder concentrations (0.5, 1.0, and 1.5) g/100 ml for each dielectric. Output responses including electrode wear rate and overcut explicitly aid in ensuring long-term productivity. For example, EWR represents economic assessment, while overcut determines dimensional accuracy. The full factorial design method was used in the experiments, followed by a full statistical examination encompassing, parametric effects and interactions, and signal-to-noise ratio. The findings of this study revealed a decreased EWR (2.69 mm3/min), as well as a minimal overcut (0.33 mm). In case of EWR, about 39% variation in the magnitude has been noted when dielectrics have been varied, whereas approximately 30.9% variation in EWR was observed against the change in the powder concentration. For overcut, the influence of the dielectric on the response magnitude has been further improved as it produces about 61% variation in the OC value. However, the impact of the change in powder concentration for overcut is 8%. It was found that Amla oil with a powder concentration of 0.5 g/100 ml produced the least tool wear, but Sunflower oil at minimum powder concentration (0.5 g/100 ml) produced the minimum overcut. As a result, the combined effect of all of the aforesaid strategies results in more sustainable and dimensionally accurate machining.

Development of Laser Processing Carbon-Fiber-Reinforced Plastic.

Due to its exceptional advantages, such as high specific strength, high specific modulus, and good fatigue resistance, carbon-fiber-reinforced plastic (CFRP) is frequently utilized in aerospace, aviation, automotive, rail transportation, and other areas. Composite components typically need to be joined and integrated. In the equipment manufacturing industry, the most used methods for processing composite components are cutting, drilling, and surface treatment. The quality of CFRP is significantly impacted by traditional mechanical processing, causing flaws like delamination, burrs, and tears. Laser processing technology has emerged as a crucial method for processing CFRP for its high quality, non-contact, simple control, and automation features. The most recent research on the laser processing of CFRP is presented in this paper, supporting scientists and engineers who work in the field in using this unconventional manufacturing technique. This paper gives a general overview of the key features of laser processing technology and the numerous machining techniques available. The concepts and benefits of laser processing technology are discussed in terms of the material properties, mode of operation, and laser characteristics, as well as the methods to achieve high efficiency, low damage, and high precision. This paper reviews the research development of laser processing of carbon-fiber-reinforced plastics, and a summary of the factors affecting the quality of CFRP laser processing. Therefore, the research content of this article can be used as a theoretical basis for reducing thermal damage and improving the processing quality of laser-processed composite materials, while, on this basis, we analyze the development trend of CFRP laser processing technology.

Security System For Motorcycle Using Raspberry Pi and Fingerprint With Mobile-Based Finite State Machine (FSM) Method

Data from the BPS (Central Statistical Agency) show that private car theft in Indonesia is on the rise year after year, especially for two-wheeled vehicles or motorcycles. According to the BPS Crime Statistics Report, there were around 35,000 motor vehicle theft events in Indonesia in 2017 and 38,000 instances in 2018. People's concerns regarding the security of these two-wheeled vehicles are growing as a result. This study presents a fingerprint- and Raspberry Pi-based motorcycle security system. Bikes may be monitored, and their ignitions can be controlled remotely using a mobile-based Finite State Machine (FSM) technique. The Neo 6 GPS module will send longitude and latitude in real time to the Android smartphone application, which generates a map or location point so that it will send notifications if there is movement on the motorcycle. The voltage sensor connected to the Raspberry Pi circuit can detect the motor voltage. A system that uses the FSM (Finite State Machine) approach to identify changes in location data from designated parking lots and deliver messages to a push bullet is the study's output. Controlling the motorcycle ignition, which may remotely switch off the motorcycle when one is taken, is another way to provide early notice.

Investigation study of electrical discharge machining parameters on material removal rate for AISI M2 material

EDM (Electrical Discharge Machining) is a common non-traditional machining technique for manufacture geometry parts made of intricate or extremely rigid metals that are challenging to manufacture using conventional manufacturing techniques. Electrical discharge machining by utilizing electrical discharge erosion, classify the meaning of material removal (MR). This paper's main objective is to discuss the ideal EDM parameters in order to use high-speed steel as workpiece AISI M2 and with using brass & copper as electrodes. Pulse on-time are (100, 150, and 200 µs), Current (10, 24 and 42 A) and Pulse off-time (4, 12 and 25 µs) are the input parameters effect on the material removal rate (MRR) used in the experimental work. The present study's findings shown that the highest MRR with using copper & brass as electrodes with pulse on-time 200 µs, pulse off-time 12 µs and current 42 A, at (0.31284 g/min and 0.18769) respectively, and the lowest average value of the removed material was when evaluating current 10 A, Ton is 100 µs, Toff is 4 µs, at (0.05451g/min and 0.01898 g/min) respectively.

Detecting impersonation episode using teaching learning‐based optimization and support vector machine techniques

AbstractOver the last few decades, computer and internet security has become a vital area because of eye‐opening numbers of data breaches, intruders on perilous infrastructure and malware attacks that are increasing day by day. In order to monitor abnormal activities and identify unusual attacks, several security solutions have been proposed in the recent past years. To protect computer system, intrusion detection system (IDS) opens up great opportunities to determine vulnerabilities and detect anomalies in security system. For detecting new attacks or building security solutions, in this study we present a new wrapper technique for security specialists that can help to detect more complicated attacks by combining teaching learning‐based optimization with opposition learning scheme and simulated annealing method. In order to address advance attack, firstly opposition learning strategy is used to update population generation of teaching learning‐based optimization that affect the robustness of the model after that simulated annealing is integrated into the teaching learning‐based optimization. In proposed method to choose the relevant features, we have used support vector machine as a fitness function that can be helped to recognize attacks precisely. The proposed algorithm is evaluated on three popular datasets namely NSL‐KDD, ISCX 2012 and UNSW‐NB15. Experimental results show that the proposed method is superior to other existing wrapper algorithms in terms of detection rate, accuracy and false alarm rates.

Application of Soft Computing Techniques in River Flow Modeling

Modeling of data is critical in the analysis and evaluation of hydrological behavior. River flow data is one of the most important data in explaining hydrology. Management of water resources; It takes place in the literature as an area that needs to be investigated in order to provide early warning for undesirable situations such as floods and drought. For this reason, it is of important to develop different techniques for the estimation and modeling of river flow or to make comparisons between techniques. In this study, the flow data of fourteen stations located in the Euphrates-Tigris basin between 1981 and 2010 were used. Adaptive Network Based Fuzzy Inference Systems (ANFIS), Support Vector Machine (SVM) techniques that are frequently used in the literature, and newly introduced Gaussian Process Regression (GPR), Extreme Learning Machine (ELM) and Emotional Neural Network (ENN) artificial intelligence techniques are compared. In addition, considering all performance indices, it was determined which technique gave better results with rank analysis. Although all models worked well, it was seen that the methods were ranked as ELM, GPR, ENN, SVM and ANFIS starting from the best. This has shown that ELM, GPR and ENN methods, which have been used recently in flow modeling, give better results than traditional methods with complex structures. In addition, flow values were used in the whole study and these values were examined in 3 different combinations. It was seen that the model structure that gave the best performance was the model structure that used the flow data from one, two and three days ago as an estimator. The results were analyzed with a Taylor diagram and time series graphs.

Analysis of bioconvection and oxytactic microorganisms in a porous cavity with nano-enhanced phase change materials and quadrant heater: Application of support vector regression based model

In this numerical study, two-dimensional simulations are performed to obtain the bioconvection flow in a quadrant heater located porous closed space saturated with nano-enhanced phase change material (NePCM). The Galerkin finite element method (GFEM) is employed to compute the governing equations for the considered system by developing a computer code. The study is performed for wide range of parameters as aspect ratio, Darcy number, Hartmann number, nanoparticle fraction, bioconvection Rayleigh number and natural convection Rayleigh number were tested. Also, the analysis is supported via artificial intelligent by using multiple support vector regression based model. The tests were performed with artificial intelligence on temperature values. After the analysis, it is found that radius of the curvilinear shaped heater plays role on both heat transfer and fluid flow, oxygen distribution, density of motile microorganism and heat capacity. Application of support vector machine technique gives good agreement with computational fluid dynamics results. Heat and mass transfer are improved with increasing of nanoparticle addition but decrease with increasing of strength of magnetic field.

Effect of Process Parameters on Material Removal Rate and Surface Characteristics in WEDM Machining of Titanium Grade 7 Alloy

Wire Electrical Discharge Machining (WEDM) is a modern machining technique. WEDM is electro-thermal non-conventional machining processes follow to cut very hard materials. WEDM is non-traditional material cutting operation employed to machine complex geometries and shapes. The present paper attempts to study surface and sub-surface variations during WEDM of titanium grade 7 alloy with Brass wire coated with Zn. This paper presents experimental studies with varying three key process parameters viz. pulse ON time (TON), pulse OFF time (TOFF), peak current (IP). The investigation is focused on assessment of material removal rate, surface roughness as well as recast layer. Scanning electron Microscope (SEM) examinations are carried out on surface textures and are discussed in the article. The obtained results indicated that the stretched pulse time discharges more energy causes rough surface finish. The MRR is also strongly influenced by TON.

Bacteria-Specific Feature Selection for Enhanced Antimicrobial Peptide Activity Predictions Using Machine-Learning Methods.

There are several classes of short peptide molecules, known as antimicrobial peptides (AMPs), which are produced during the immune responses of living organisms against various infections. In recent years, substantial progress has been achieved in applying machine-learning methods to predict the activities of AMPs against bacteria. In most investigated cases, however, the outcome is not bacterium-specific since the specific features of bacteria, such as chemical composition and structure of membranes, are not considered. To overcome this problem, we developed a new computational approach that allowed us to train several supervised machine-learning models using a specific set of data associated with peptides targeting E. coli bacteria. LASSO regression and Support Vector Machine techniques have been utilized to select, among more than 1500 physicochemical descriptors, the most important features that can be used to classify a peptide as antimicrobial or ineffective against E. coli. We then performed the classification of active versus inactive AMPs using the Support Vector classifiers, Logistic Regression, and Random Forest methods. This computational study allows us to make recommendations of how to design more efficient antibacterial drug therapies.

ANALISIS PERBANDINGAN ALGORITMA SUPPORT VECTOR MACHINE (SVM) DAN CONVOLUTIONAL NEURAL NETWORK (CNN) UNTUK SISTEM DETEKSI KATARAK

One of the most vital senses for humans is sight. Humans utilize their eyes to take in visual information that is used for a variety of tasks, but vision problems are prevalent, ranging from minor disorders to serious disorders that can result in blindness. Cataracts are one of the factors contributing to this vision loss. In Indonesia, cataracts alone account for 81 percent of cases of blindness, and 40 percent of those affected don't even realize they have them. A technique for early cataract identification using digital photographs is one way to lower the incidence of cataract-related blindness. We used Support Vector Machine (SVM) and Convolutional Neural Network (CNN) techniques to create a cataract detection system. These two techniques are used to categorize normal eye classes, immature cataracts, and mature cataracts. A digital image that has been downsized to 64x64 pixels in Joint Photographic Group (JPG) format that was taken from earlier study serves as the input data. Support Vector Machine (SVM) and Convolutional Neural Network (CNN) methods used in the cataract detection process provide optimal results. The Support Vector Machine (SVM) method itself produces an accuracy value of 96.67%, while the Convolutional Neural Network method produces a better accuracy value of 98.89%.
 Keywords: Cataract, Digital Image, Machine Learning, Support Vector Machine, Convolutional Neural Network

Improving Students’ Machining Competence by Applying Peer Tutoring Methods

The aims of this study were analyzing the improvement of student competence in attitudes, knowledge, and skills by applying peer tutoring in machining practice learning. The research method uses classroom action research with cycle stages: planning, action, observation, and reflection. The action research cycle was carried out three times, preceded by a pre-cycle. The action taken is the application of peer tutoring with the following steps: (1) Selecting a tutor, (2) Forming groups, (3) Discussing practical assignments, (4) Tutors guiding other friends to work on assignments, and (5) Observing and monitoring learning activities for each student. The subjects of this study were 32 students who took the lathe machining technique at SMKN 2 Depok Sleman Yogyakarta. The implementation of research was carried out in a machining engineering workshop. As a learning tool, there are eight manual lathes and additional tools for operating the lathe. Methods of collecting data were observation, knowledge test, skill test, and documentation. Analysis of the data used is descriptive statistical analysis. The study results: The application of peer tutoring can improve student competence. Improvements in attitudes, knowledge, and skills competencies occur gradually during the implementation of peer tutoring. Achievement of the minimum competency criteria can be achieved in practicum learning and improving students’ skills in operating machine tools.

Surface integrity of Ti-6Al-4V alloy in hybrid electrical discharge machining processes

Titanium alloy is one of the difficult-to-machine materials. The final conditions of the machined surface of this alloy are influenced by electro-discharge machining. High temperatures and thermal cycles in machining operation induce undesirable alterations in the surface quality of Ti-6Al-4V. One of the ways to improve the negative effects of electrical discharge machining is to use hybrid methods. There is no comprehensive research on the effect of hybrid electrical discharge machining process on the surface integrity of the Ti-6Al-4V alloy. In this paper, hybrid electrical discharge machining techniques including ultrasonic-assisted electrical discharge machining, powder-mixed dielectric electrical discharge machining using Al2O3 nanopowders and powder-mixed dielectric combined with ultrasonic-assisted electrical discharge machining, were used for processing of this alloy. The hybrid electrical discharge machining processes were evaluated by basic specifications of surface integrity including surface cracks, heat affected area, residual stress, and corrosion resistance. It was found that in powder-mixed dielectric combined with ultrasonic-assisted electrical discharge machining process, developed microcracks were very low and this process developed a thinner heat-altered layer, compared to traditional electrical discharge machining process, which produced a thicker heat-altered layer. The findings also, demonstrated that ultrasonic waves have reduced residual stresses compared to other methods and its stress diagram was distinct in comparison with the other processes. The corrosion rate of samples machined by electrical discharge machining process was higher than other hybrid processes; in powder-mixed dielectric electrical discharge machining process, the machined samples had the highest corrosion resistance.

Integrity Study of the Machined Surface Quality in Hard Turning

Since their appearance, machining techniques have undergone multiple improvements. The processes of shaping materials by removing material have been constantly questioned in order to stay in step with industrial requirements, whatever economic or ecological. Today, the manufacturing engineer must therefore be able to answer a multitude of questions in order to quickly produce parts of the required quality and at low cost. Hard turning is a process that can be described as standard in a number of sectors such as the production of bearing steel 100Cr6 chrome with a hardness of 62 RC, but also turning auto parts steel chromium-manganese cemented hardened gears as 27MnCr5 a hardness of 62 RC. The technical and economic feasibility of machining parts such harshness was made possible thanks to the use of new tool materials with a very high hardness and high chemical stability at high temperatures, such as cubic boron nitride CBN, and with the arrival of new machine tools with significant stability and high precision. Hard turning allows today to produce high-quality surfaces from competing in many cases, operations traditionally reserved for rectification. High speed machining has also showed its competitiveness in applications such as turning parts for gearboxes and drivesystems in hardened materials. This article is a contribution to the study of the integrity of the machined surface in hard turning.

An unsupervised machine learning technique to identify knock from a knock signal time-frequency analysis

Knock is a characteristic phenomenon of spark ignition engines that can result in lower fuel efficiency and higher combustion temperatures. When it is not adequately identified and controlled, knock might lead to intensive engine damage. Knock detection methods are based on recognising the large in-cylinder pressure oscillations that resonate in the combustion chamber as a consequence of the rapid auto-ignition of the end-gas. However, in-cylinder pressure sensors may be invasive and expensive. In this work, a method for knock classification is proposed based on the knock sensor measurement, which is a low-cost and easy-to-implement vibration sensor. Unfortunately, the vibration of the engine block contains information from several phenomena, and only intensive knock can be identified by classical methods. To counteract the amount of noise, machine learning (ML) techniques are applied to the knock sensor signal. A training database composed of several operating conditions is used to extract the main characteristics of knock. The signal is analysed in the time-frequency domain by using the short-time Fourier transform (STFT), the features of the signal are obtained by singular value decomposition (SVD), and finally, support vector machine (SVM) techniques are used to identify outliers. Experimental data from a spark-ignited engine at 2000 rpm and 3000 rpm with spark advance steps was used to train and validate the method. Results are compared to state-of-the-art knock recognition methods based on pressure and knock sensors. The technique was able to detect 73% of the knock cycles, which was similar to the results achieved using the pressure sensor data (77%). Compared to another method using the knock sensor signal as well, the percentage of false negative cases was reduced by 20%.