Behavior Of Machine Research Articles

Adjustment of uncertain model parameters to improve the prediction of the thermal behavior of machine tools

Computer models, which simulate the thermo-elastic behavior of machine tools, are used for optimizing machine designs and more recently for controller-integrated online corrections. Sufficiently accurate corrections of errors caused by the thermal machine behavior require quick and precise identification of uncertain and irregularly distributed parameters based on measurements. The paper presents a systematic approach to conduct parameter identification during machine commissioning. In particular, the acquisition of required data for parameter adjustment and model optimization is addressed. Finally, the procedure is demonstrated on the example of a machine tool's feed axis.

Spark erosion machining behaviour of coconut shell ash reinforced silicon metal matrix

In recent days, the production of a metal matrix with excellent properties is a challenging task. The contribution of silicon in industries particularly, the demand is increased in electronics industries. The quality of the metal matrix is focused on new reinforcements and composition. In this concept, coconut shell ash is applicable as a strengthening element. It has accumulated on silicon using stir cast. The composite is machined by Spark Erosion Machining (SEM) after the evaluation of substance properties. The Material Subtraction Rate (MSR) is determined to change the control parameters. The MSR is enhanced through graphite mixed dielectric fluid. The maximum MSR and its optimal parameters are found through Taguchi optimization.

Parametric Optimization of High Speed Turning Operation of Hybrid Aluminium Composites Using Taguchi Based Grey Relational Analysis

In the field of metal matrix composites (MMCs), most of researches are being carried out with carbide, alumina and boride as reinforcement. Nitrides and oxides are also suitable candidates to be used as reinforcement. There is a huge scope to study the effect of nitride and oxide on the mechanical and machining behaviour of composite materials. Therefore, this paper has set the primary objective of developing the hybrid aluminium-based composite and studying the hardness and machining behaviour of high hardness hybrid composite of MMCs by reinforcing with nitride and oxide. The composition chosen are the first sample composition with 6% of silicon nitride (Si3N4) and 3% of magnesium oxide and another with 3% Si3N4 and 3% MgO. Al6061 matrix with blended magnesium oxide and silicon nitride is produced using stir casting process. Computer numerical control (CNC) turning operation has been carried out on the composite developed. The main responses considered in this study are surface roughness (SR) and material removal rate (MRR) and machining parameters such as feed rate, depth of cut and cutting speed, are chosen. Among these, feed and depth of cut are most influencing factor for the machining of hybrid composites.

On-Load Analytical Modeling of Slotted Interior Magnet Synchronous Machines Using Magnetic Islands Method

In general, the Magnetic Circuit (MC) is an effective and valuable tool for calculating the PM flux and predicting machine behavior during the no-load operation. Since the rotor PMs are the sole source of the flux, the flux path and the respective MC model is independent of the rotor position. However, this is not the case for the loaded operating condition. As the rotor spins, the path of the armature flux necessarily changes to pass through the rotor with minimum reluctance. Therefore, the MC model would vary at every rotor position and is no longer a feasible solution particularly, for cases with complex rotor structure. To resolve this, an analytical solution procedure based on the concept of the Magnetic Islands (MI) is presented to predict the on-load response of the motor such as armature flux and the winding inductance. The air-gap is defined as a function along the perimeter of the stator to include the stator slotting effect. In addition, the presented method accounts for the magnetic saturation. To assess the accuracy of the model, analytical results are compared against the finite element results.

The Application of Epidemiology for Categorising DNS Cyber Risk Factors

This Epidemiology can be applied to cybersecurity as a novel approach for analysing and detecting cyber threats and their risks. It provides a systematic model for the analysis of likelihood, consequence, management, and prevention measures to examine malicious behaviours like disease. There are a few research studies in discrete cybersecurity risk factors; however, there is a significant research gap on the analysis of collective cyber risk factors and measuring their cyber risk impacts. Effective cybersecurity risk management requires the identification and estimation of the probability of infection, based on a comprehensive range of historical and environmental factors, including human behaviour and technology characteristics. This paper explores how an epidemiological principle can be applied to identify cybersecurity risk factors. These risk factors comprise both human and machine behaviours profiled as risk factors. This paper conducts a preliminary analysis of the relationships between these risk factors utilising Domain Name System (DNS) data sources. The experimental results indicated that the epidemiological principle can effectively examine and estimate cyber risk factors. The proposed principle has a great potential in enhancing new machine learning-enabled intrusion detection solutions by utilising this principle as a risk assessment module of the solutions.

Data Processing Model to Perform Big Data Analytics in Hybrid Infrastructures

Big Data applications are present in many areas such as financial markets, search engines, stream services, health care, social networks, and so on. Data analysis provides value to information for organizations. Classical Cloud Computing represents a robust architecture to perform complex and large-scale computing for these areas. The main challenges are the user's unknowledge about Cloud infrastructure, the requirement needed for improving performance, and the resource management to maintain stable processing. In these difficulties, an inadequate solution can lead to users overestimate or underestimate the number of computational resources, which drives to the budget increases. One way to work around this problem is to make use of Volunteer Computing since it provides distributed computational resources at free monetary cost. However, a volatile machine behavior is a problem to address in Big Data data distributions. Thus, this work proposes a data distribution model composed of Cloud Computing and Volunteer Computing environments in a hybrid fashion for Big Data analytics. The contributions of this work are: i) the required evaluation to enable efficient deployment of Big Data in hybrid infrastructures; ii) the development of an HR_Alloc Algorithm for establishing the data placement to Big Data applications; iii) a model to resource allocation in hybrid infrastructures. The obtained results indicate the feasibility of using a hybrid infrastructure with up to 35% of unstable machines in the worst-case scenario, without losing performance and a monetary cost lower than 20% in comparison to Classical Cloud Computing. Also, communication costs decrease up to 57.14% in the best-case scenario due to load balancing.

LINEAR FRICTION WELDING – PROCESS CONTROL AND MACHINE TECHNOLOGY

Linear Friction Welding (LFW) is well adapted to produce titanium aircraft engine and structure parts, as an alternative to machining from solid, from forging or from electron beam welded blanks. The process can be described through a small number of mechanical variables: the amplitude and frequency of oscillation motion, the contact pressure and the axial displacement resulting of hot material flowing outside of the joint interface. Nonetheless, because of the high speeds and loads, the correct control and monitoring of linear friction welding requires special care on machine and tooling design. The main principles of LFW machine design are described, with a special focus on the consistency between process quality requirements and machine characteristics: the static and dynamic machine behavior, the control system performance, the sensor technology and the tooling clamping solutions shall allow a good control of the weld parameters and an accurate monitoring of the physical phenomena at the joint interface.

Modeling and Simulation of a Tubular Linear Switched Reluctance Actuator

In this paper, 2-D finite element analysis and MATLAB/Simulink software are used to model and simulate the proposed tubular linear switched reluctance actuator. The analysis of the actuator by finite element is essential for determining the magnetization characteristics. The obtained data from the analysis is useful for testing and verifying the machine operation performance and behavior. According to the analysis, when a step current signal of 3A was applied to the actuator, oscillation occurred at beginning of the motion with maximum overshooting of 2mm and settling time of 0.15s. Besides, the force analysis showed there was nonlinear force behavior between - 3.5N and 2N observed from the actuator motion. The saturation and nonlinear magnetization curve of materials causes the nonlinearity characteristics of thrust force and magnetic flux which affect the performance of the actuator. The determination of the characteristics and performance is crucial for the proposed actuator to realize a precision positioning system in the future.

Examination on Mechanical Behavior and Drilling Analysis of Fiber Metal Laminates

Fiber Metal Laminates (FML) are a class of composites that are recently employed to substitute sole metals in various applications like aerospace applications. In this investigation, a new type of FML was successfully fabricated using compression moulding in which Aluminium and ceramics mat are stacked in the presence of epoxy resin. To improve the bonding by ensuring the flow of resin through the laminates, drilling with various pattern on the Aluminium sheet and ceramic mat were performed before subjected to compression to form FML. Aluminium sheets with Zig-zag pattern performed in a better way due to the improvements in bonding. In addition, drilling operation was done on the FML to ensure the de-lamination resistance and machinability. The drill bit before and after drilling was inspected by using optical microscope to understand the machinability behaviour of the FML.

Review of achievements and research directions in computer modeling of machines and mechanisms in KhNAHU

The paper presents the implementation of computer-aided design technologies in the educational process, diploma design, as well as in scientific researches. It is underlined that a qualitative leap in the possibilities of modeling machines and mechanisms occurred in 2006, with the start of the Autodesk Inventor Series package using. It allowed creating a detailed model of the product – a digital prototype – simulating not only the geometry but also the basic physical properties of the parts. An important and actual problem is the analysis of the methods used for models creations, the generalization of the results obtained and the development of algorithms and modeling methods for specific machines and mechanisms. The created models of construction and road machines were described, as well as the elements of a crank, link, pendulum, and other mechanisms. With the addition of a dynamic modeling environment and calculations based on the finite element method, it became possible not only to simulate motion but to conduct virtual experiments with developed digital prototypes. First of all, it is the study of the stability of machines, the determination of tip-ping angles, the analysis of forces and support reactions in the elements of running equipment when overcoming obstacles. In conclusion, it was noted that computer modeling makes possible to solve a wide range of engineering and research problems. Geometric modeling is worked out most deeply, and the task is to create parametric families of parts. It is more difficult to model the behavior of machines, their components, assemblies and components. These problems should be solved by the method of successive approximation of virtual objects to real machines with acceptable simplifications. It is also necessary to conduct computer studies of physical phenomena as applied to the design of construction and road machines, including the possibility of integration with settlement systems (such as MatLab, Simulink, Ansys). The results of the research will be presented in future publications.

Micro-structuring silicon compound ceramics using nanosecond pulsed laser assisted by hydrothermal reaction

In the machining of silicon compound ceramic materials via laser beam, processing in water has been found to enhance machining quality, while processing in air has limitations such as the generation of a recast layer, heat affected zone, and thermal crack. However, few studies have investigated the effects of water on the ablation rate of silicon compound ceramics. In this research, the ablation behavior and machining principles of the laser beam machining of silicon compound ceramics in water are investigated. The research concluded that the increase in temperature induced by laser irradiation promotes hydrothermal reaction between the silicon compound ceramics and water. The hydrothermal reaction assists in the removal of the oxide layer at the machined area and enhances the efficiency of the process in water. The effects of water on ablation are confirmed for various machining parameters such as the flow rate of sprayed water on the workpiece and laser conditions. Finally, the various micro-structures were fabricated on the silicon compound ceramics to evaluate the processability.

Microstructure, Microhardness and Machining Characteristics of Al6063-SiC Composites

Machining of materials is an important operation in the manufacturing industry to develop different structures. Compared with metals and alloys, metal matrix composites (MMCs) respond differently during machining due to the presence of different phases possessing different physical and mechanical properties. In the present study, machining behavior of Al6063-SiC composites has been investigated to assess the role of dispersing phase on machining characteristics. Composites with varying SiC content (4% and 8% by Wt.) were produced by stir casting. From the micro structural studies, decreased grain size was observed with increased SiC dispersion. Higher hardness was measured for the composite with 4% SiC. From the machining studies during turning experiments, the variations within the cutting forces was found to be higher for the composites compared with the base material. Cutting forces were also measured as increased with the increase of SiC dispersion. From the results, it can be understood that the presence of SiC significantly influence the machining behavior of Al6063-SiC composites which is a valid factor must be considered while performing turning operations.

A Hybrid Approach for Modeling Machining Performance of Aluminum Metal Matrix Composites Reinforced with Industrial Waste Materials

This current study is intended to examine the machining behaviour of red mud reinforced aluminum metal matrix composites with different parameters specifically, cutting speed, feed, depth of cut and nose radius. A two hybrid networks model is developed to envisage the performance of machining parameters. Artificial Neural Network (ANN) model is developed by with machining parameters and combined by captured vibration and power signals throughout machining operation. Grey relational grade which is obtained from vibration and power signals is used with the primary machining parameters to construct the hybrid network model. A newly developed grey based neural network (hybrid) model is found to offer better prediction on output responses compared to the neural network model. The predicted result obtained from the based neural network model is good agreement with investigational results.

Design and Development of Techniques for Equipment Health Monitoring System

Machines in Industries are often subjected to enormous wear and tear, which if unnoticed, may lead to production delays and increased maintenance cost. Machines must be able to analyse and provide statistics about its health, so that preventive measures can be taken to avoid catastrophes in the industries. Thus, there is a need of automated fault detection and prediction of system’s condition. The concept of equipment health monitoring is a crucial step in the field of research and development in the manufacturing industries. This equipment makes it handy in situations where machines require continuous monitoring and is difficult for humans to provide such attention , especially in the case of unmanned vehicles. Prediction of the status of equipment by acquisition of data from industrial machinery is the critical step in building such a system. Health of machines can be estimated by the data collected by the sensors-temperature, accelerometer, etc integrated with an embedded computing system, like a Raspberry Pi. This IoT model consisting of embedded system with wireless connectivity collects real time data from the equipment/machinery used in industries. This data is used to analyse and predict the health of the equipment, examine the steady-state characteristics using Machine Learning technique, Hidden Markovian Model. The concept of the proposed IoT model is evaluated over a conveyor belt test rig under various conditions, like different loads placed on various locations of conveyor belt and the belt is made to run at different speeds and data is collected over all these conditions. Then, a data model is created using Hidden Markovian Model which is further used in predicting the state of the belt based on the sequential data, here it is the sensor data. Given a state of the belt, this model can predict whether the belt is in proper condition or not, and if human intervention is required. Thus, at any point of time, having this setup on the machinery which needs to be monitored can be used in predicting the faults and notifying the user in case of any faulty behaviour or malfunctioning of machines. This setup can be used for any machines which are subjected to any motion, vibration and thermal changes. This helps in creating a completely automated fault detection systems in the present Industries.

Aging monitoring of electrical machines using winding high frequency equivalent circuits

Abstract This study defines a monitoring method based on impedance analysis in the upper part of the spectrum. It focuses on the analysis of the high frequency behavior of electrical machines windings considering the turn-to-turn capacitance variations (delta-C) due to insulation aging. An equivalent circuit is automatically generated from the turn arrangements in coils; parameters are computed considering the coil shape and the characteristics of insulation materials. PSpice analysis of the equivalent circuit yields the resonance frequencies to be monitored. With this software and a database giving the relationship between the turn-to-turn capacitance variation delta-C and the reduction of insulation performances due to aging, it is possible to build a monitoring system able to produce an alert when the probability of failure becomes higher than a predetermined level.

Preventing undesirable behavior of intelligent machines.

Intelligent machines using machine learning algorithms are ubiquitous, ranging from simple data analysis and pattern recognition tools to complex systems that achieve superhuman performance on various tasks. Ensuring that they do not exhibit undesirable behavior-that they do not, for example, cause harm to humans-is therefore a pressing problem. We propose a general and flexible framework for designing machine learning algorithms. This framework simplifies the problem of specifying and regulating undesirable behavior. To show the viability of this framework, we used it to create machine learning algorithms that precluded the dangerous behavior caused by standard machine learning algorithms in our experiments. Our framework for designing machine learning algorithms simplifies the safe and responsible application of machine learning.

Accelerating the Shuttling in Hydrogen-Bonded Rotaxanes: Active Role of the Axle and the End Station.

The relation between the chemical structure and the mechanical behavior of molecular machines is of paramount importance for a rational design of superior nanomachines. Here, we report on a mechanistic study of a nanometer scale translational movement in two bistable rotaxanes. Both rotaxanes consist of a tetra-amide macrocycle interlocked onto a polyether axle. The macrocycle can shuttle between an initial succinamide station and a 3,6-dihydroxy- or 3,6-di-tert-butyl-1,8-naphthalimide end stations. Translocation of the macrocycle is controlled by a hydrogen-bonding equilibrium between the stations. The equilibrium can be perturbed photochemically by either intermolecular proton or electron transfer depending on the system. To the best of our knowledge, utilization of proton transfer from a conventional photoacid for the operation of a molecular machine is demonstrated for the first time. The shuttling dynamics are monitored by means of UV–vis and IR transient absorption spectroscopies. The polyether axle accelerates the shuttling by ∼70% compared to a structurally similar rotaxane with an all-alkane thread of the same length. The acceleration is attributed to a decrease in activation energy due to an early transition state where the macrocycle partially hydrogen bonds to the ether group of the axle. The dihydroxyrotaxane exhibits the fastest shuttling speed over a nanometer distance (τshuttling ≈ 30 ns) reported to date. The shuttling in this case is proposed to take place via a so-called harpooning mechanism where the transition state involves a folded conformation due to the hydrogen-bonding interactions with the hydroxyl groups of the end station.

Analysis of artificial Intelligence Technology in Electric Automation Control

Electrical engineering is an important part of mechanical engineering. With the development of science and technology, automation has gradually replaced the artificial labor force, and artificial intelligence has played an important role in electrical engineering automation. Artificial intelligence is embodied in the three levels of thinking, perception and behavior. It mainly simulates eyes and expands human intelligence. Its research contents can be divided into machine thinking and thinking machine, machine behavior and behavior machine, machine perception and perception machine, three levels. In the control process of electrical automation engineering, intelligent control technology not only strengthens the ability of automation and control of electrical equipment, but also lays a solid foundation in the fast and safe operation process.

Coupled physics solvers for analysing the magneto‐thermal behaviour of a permanent magnet synchronous machines

AbstractThe aim of this study is to present a thermal analysis of a permanent magnet synchronous machine based on the finite element method. The developed model can be used to predict temperature distribution inside the studied motor during the rated operation. Electromagnetic computation (EM) is carried out with the aid of two 2D finite‐element (FE) simulations on the cross‐section of the PM motor. To analyse the process of heat transfer (HT) in an electrical machine, empirical correlations are used to describe the convective (HT) from the different surfaces of the PM motor. The (HT) coefficient is determined using dimensionless numbers. The proposed numerical method to solve the coupled problem is based on independently and successively solving the (EM) and thermal problems and then transferring the results between the two sub‐problems. The results obtained by the model are compared with experimental results from testing the prototype electric motor.

Analytical evaluation of an innovative decision-making algorithm for VM live migration

In order to achieve the virtual machines live migration, the two pre-copy and post-copy strategies are presented. Each of these strategies, depending on the operating conditions of the machine, may perform better than the other. In this article, a new algorithm is presented that automatically decides how the virtual machine live migration takes place. In this approach, the virtual machine memory is considered as an informational object that has a revision number and it is constantly changing. We have determined precise criteria for evaluating the behavior of a virtual machine and automatically select the appropriate live migration strategy. Also in this article, different aspects of required simulations and implementations are considered. Analytical evaluation shows that using the proposed scheme and the presented algorithm, can significantly improve the virtual machines live migration process.