Behavior Of Machine Research Articles

Thermal Analysis of a Flux-Switching Permanent Magnet Machine for Hybrid Electric Vehicles

This paper investigates the loss and thermal characteristics of a three-phase 10 kW flux-switching permanent magnet (FSPM) machine, which is used as an integrated starter generator (ISG) for hybrid electric vehicles (HEVs). In this paper, an improved method considering both DC-bias component and minor hysteresis loops in iron flux-density distribution is proposed to calculate core loss more precisely. Then, a lumped parameter thermal network (LPTN) model is constructed to predict transient thermal behavior of the FSPM machine, which takes into consideration various losses as heat sources determined from predictions and experiments. Meanwhile, a simplified one-dimensional (1D) steady heat conduction (1D-SHC) model with two heat sources in cylindrical coordinates is also proposed to predict the thermal behavior. To verify the two methods above, transient and steady thermal analyses of the FSPM machine were performed by computational fluid dynamics (CFD) based on the losses mentioned above. Finally, the predicted results from both LPTN and 1D-SHC were verified by the experiments on a prototyped FSPM machine.

A Comparison of Behavior Cloning Methods in Developing Interactive Opposing-Force Agents

Modern modeling and simulation environments, such as commercial games or military training systems, frequently demand interactive agents that exhibit realistic and responsive behavior in accordance with a predetermined specification, such as a storyboard or military tactics document.Traditional methods for creating agents, such as state machines or behavior trees, necessitate a significant amount of effort for developing state representations and transition processes through manual knowledge engineering. On the other hand, newer techniques for behavior generation, such as deep reinforcement learning, require a vast amount of training data (centuries in many cases), and there is no guarantee that the generated behavior will align with intended objectives and courses of action. This paper examines the application of behavior cloning approaches in designing interactive agents. In our approach, users start by defining desired behavior through straightforward methods such as state machine models or behavior trees. Behavior cloning methods are then used to transform ground-truth trajectory data sampled from these models into differentiable policies that are further refined through engagement with interactive game environments. This method results in improvements in training results when compared on dimensions of task performance and stability of training.

Experimental Investigation on Surface Characteristics of Nickel-Based Super Alloy in Powder Mixed Electric Discharge Machining

Abstract: Electrical discharge machining behavior of nickel based super alloy using titanium carbide powder mixed dielectric fluid turned into studied. Experiments were completed consistent with face centered central composite design. The addition of powder particles in suspension in the dielectric modifies some process variables and creates hard and brittle materials with nano surface finish, high tolerance and accuracy to achieve a material removal rate increase. The effect of input parameters such as current, pulse on-time and flushing pressure on the output responses like material removal rate and tool wear rate are evaluated. Dielectric fluid used in this experiment was kerosene mixed with titanium carbide powder.

Force and Roughness Analysis for Natural Bagasse Fiber Reinforced Composites Under Milling Machining: An Experimental Study

ABSTRACT The machining behaviour of bagasse fibre-reinforced polymer composites is investigated in terms of cutting forces and surface roughness during the milling process in this study. The impact of fibre orientation and cutting parameters is found while quantifying cutting forces. A tool dynamometer connected to the universal milling machine measures the cutting forces during the milling process. The hand lay-up method is used to create composite specimens with three different fibre orientations to the cutting direction, such as 0°, 45° and 90° (θ). Cutting forces in the X, Y, and Z directions are calculated using cutting parameters such as speed and depth of cut. Surf experiments were used to assess the surface roughness of the composite specimens. The microscopic images aid in understanding the fibre orientation and its impact on fibre damage during the milling process.

Magnetic field effects on nonlinear dynamic behavior in electro-dissolution and pulse electrochemical machining of Ti-48Al-2Cr-2Nb alloy

Magnetic field effects on nonlinear dynamic behavior in electro-dissolution and pulse electrochemical machining of Ti-48Al-2Cr-2Nb alloy

USE OF THE THEORY OF FINITE AUTOMATA FOR SUPERVISION OF THE OPERATION OF THE COMPRESSOR UNIT

The purpose of the publication is to develop a methodology for presenting the unit "6 kV compressor-synchronous electric motor" in the form of a finite state machine.
 It is hypothesized that the behavior of the compressor unit can be associated with the behavior of an abstract deterministic finite state machine.
 The theory of finite state machines and graphs is applied to analyze the operation of a compressor installation using the example of a compressor of a large-tonnage shop for the production of ammonia. It is shown that the theory of finite state machines allows combining all possible modes of operation and shutdown of compressor units. Coding of reasons for compressor shutdown due to problems in the electrical or mechanical parts of the compressor unit is proposed. A diagram of state transitions has been developed for it. Such a diagram can be useful for compiling a concise description and understanding of the operation of a compressor unit, advanced training of technological personnel during industrial training, for emergency training, etc. 
 A graphical analysis of the obtained diagram was carried out. The optimal (most perceived in terms of energy efficiency) route of the graph is determined.
 The proposed form of abbreviated recording of the compressor operation cycle and the electronic mileage log, which allows you to calculate the remaining resource of the compressor installation and the compressor operation time.
 The implemented methods make it possible to identify bottlenecks in the complex electrical and technological equipment of industrial enterprises.

Retracted: Effect of SiC Particle Incorporated Dielectric Medium on Electrical Discharge Machining Behavior of AA6061/B4Cp/SiCp AMCs

Retracted: Effect of SiC Particle Incorporated Dielectric Medium on Electrical Discharge Machining Behavior of AA6061/B4Cp/SiCp AMCs

Evaluation of the injection molding process behavior during start-up and after parameter changes using dynamic time warping correspondences

Injection molding is one of the most widely used plastic processes due to its mass production capability and low production costs. Both simple and complex part geometries can be produced in short cycle times which, with other manufacturing processes, would require long production times. To control the process, it is adjusted by a variety of parameters. In addition to the machine configurations, disturbances, the thermal behavior of the machine components and the process control influence the process behavior and the resulting part quality.Due to the large number of machine parameters and the complexity of the process, it is not apparent to the machine operator how long the machine needs after switching on until the process runs stable. Up to now, the quality characteristics of the injection molded parts are determined by extensive tests in order to evaluate the process behavior. Thereby mostly the weight is used representatively as quality characteristic for process and quality control because it can be determined non-destructively, and it is closely related to other quality characteristics (such as dimensional properties).The aim of this research was to analyze the time series of the machine process data and to assess an automated method for evaluating the start-up behavior and the machine behavior after machine parameter changes. For this purpose, dynamic time warping correspondences (DTW) was used to investigate the process behavior based on high-resolution process data (here: injection pressure, flow rate, and screw volume).The method presented in this paper makes a significant contribution to automating the start-up process and reducing the testing effort. In addition, the investigations provide insightful findings for analyzing the process behavior after parameter changes. To improve the outcome, the mold temperature and cavity pressure should also be evaluated with the presented method in future investigations.

Machinability comparison of additively manufactured and traditionally wrought Ti-6Al-4V alloys using single-point cutting

Post-machining is often needed to provide high dimensional accuracy and fine surface finish for additively manufactured Ti-6Al-4 V (Ti64). The material inhomogeneity, such as pores and microstructures, can affect the machining behavior of this already difficult-machine alloy. This study adopts a holistic approach to compare the machinability of additively manufactured and traditionally wrought Ti64 in terms of key machining factors, including forces, temperature, and vibration and the major machining outcomes including tool life, surface finish, and dimensional accuracy. Stress-relief annealing is applied to each of the part conditions as a secondary variable to observe the additional effects. The results show that AM is not particularly more difficult to machine in terms of cutting force and temperature, but it creates high cutting vibrations across a wide range of frequencies (to and over 5 kHz). The high vibrations do not lead to worse surface finish or dimensional accuracy but tend to worsen the tool life by chipping off the cutting edge. The vibration can be attributed to the brittle martensitic microstructure found in additively manufactured Ti64, which is also evidenced by the more serrated chips. Stress-relieving is found to change the microstructure and reduce the level of vibration to that of the wrought counterpart.

Hydrogenerator early fault detection: Sparse Dictionary Learning jointly with the Variational Autoencoder

Monitoring the continuous health status of a Hydraulic Turbine Generator Unit (HTGU) is a strategic task to prevent any unexpected downtime. In addition to the loss of energy production following a prolonged shutdown, the various maintenance costs represent an undesirable effect due to unanticipated failure. One of the main functions of a condition-based maintenance (CBM), is the early detection of any unexpected changes in a machine behavior. Indeed, being able to detect any drift or change in behavior compared to a reference behavior represents a major challenge in the monitoring of complex machines like a HTGU. In this paper, an early anomaly detection model for HTGU using Variational Autoencoders (VAEs) and Sparse Dictionary Learning (SDL) is proposed. The combined reconstruction error thus obtained from the VAE-SDL model is used as an early fault detection. Experimental tests on vibratory signals show that the two models thus joined increase the sensitivity as well as the robustness of the anomaly detection.

AM20, AJ21 ve AS21 Magnezyum Alaşımlarının Aşınma ve İşlenebilirlik Davranışları Üzerine Karşılaştırmalı Bir Çalışma

This paper is a comparative study on mechanical properties, wear resistance and machinability behaviors of AM20, AJ21 and AS21 (Mg-2Al-X) cast magnesium alloys. The effects of the changing in third alloy component (x:0.5 Mn, 1 Sr and 1 Si, and constant 2 Al, wt.%) in these alloys on microstructure (intermetallic phases), mechanical and wear properties and machinability (cutting force, surface roughness, FBU and also chip formation) were comparatively analyzed. The highest mechanical properties and wear resistance were in AS21 (i.e. 49.2 hardness and 181.5 MPa UTS) alloy and the highest machinability were in AM20 (cutting force 35.6N) alloy. The machinability of the alloys were increase with increasing cutting speed.

Microstructural Evolution of Shear Localization in High-Speed Cutting of CoCrFeMnNi High-Entropy Alloy

Shear localization is one of the most important failure mechanisms subjected to high-strain-rate deformation and has significant effects on the process, plastic deformation, and catastrophic failure of a material. Shear localization was observed in serrated chips produced during the high-speed cutting of the CoCrFeMnNi high-entropy alloy. Electron backscatter diffraction was performed to systematically investigate microstructural evolution during shear banding. The elongation and subdivision of the narrow grains were observed in the areas adjacent to the shear band. The microstructure inside the shear band was found to be composed of equiaxed ultrafine grains. The results reveal that grain subdivision and dynamic recrystallization might have significant roles in the microstructural evolution of shear bands. These results offer key insights into our understanding of shear localization and high-speed machining behavior for high entropy alloys.

The analytical beam element model: novel approach for fast calculation of vibrations in electric machines

The vibration and acoustic behavior of electric machines is an important aspect of the design process. A crucial part of the modeling is the correct prediction of the stator’s vibration behavior, characterized by the stator’s eigenfrequencies and eigenmodes. For this purpose, a calculation approach called the analytical beam element model (ABM) was presented in a previous paper [18], where Euler–Bernoulli beams were used to describe the vibration behavior of the stator. The ABM introduced offers an alternative to the finite element (FE) method and to classical analytical models. In this paper, the model is examined and extended further. Different approaches conceived to better describe the influence of the stator yoke’s thickness by using Timoshenko beams and a multi-layer discretization are presented and discussed. Furthermore, a new feature that considers the lever arm effect of the stator teeth with respect to the yoke is introduced. The results are compared to FE calculations and measurements. Lastly, the improved ABM is used to calculate the vibration behavior of four stators with outer diameters ranging from 160,text{mm} to 7,text{m}. The results are compared to FE results to prove the accuracy of the ABM.

Efficient and low-damage machining of Ti6Al4V: laser-assisted CBN belt grinding

ABSTRACT This study introduces a new method for efficient and low-damage machining of Ti6Al4V: laser-assisted CBN belt grinding. The impact of laser power and grinding depth on the machining behavior of the material is investigated, the elemental distribution and chemical state of the ground surface are analyzed, and the removal mechanism of laser-assisted CBN belt grinding is described. The results show that increasing laser power will improve processing efficiency, enhance plastic material removal, and improve surface quality; the tangential grinding force is decreased by 63.5% and the normal grinding force is decreased by 72.6% at the maximum laser power; a large amount of Ti and N are present on the ground surface, and they are present in the form of TiN compounds. This study is a guide to the research on efficient and low-damage machining of hard-to-process materials like titanium alloys.

A study of surface roughness and material removal rate for optimal parametric combination in turning of GFRP composites

High surface quality and/or dimensional accuracy of the products produced by machining plays an important role in the part performance. The use of polymer materials is gradually replacing that of metallic materials. Due to their anisotropy and non-homogeneity, their machining behavior differs greatly from that of normal metallic materials. Because the phenomena responsible for material removal while cutting fiber reinforced plastic composite materials and those of typical metals and their alloys differ fundamentally, particular attention must be paid to the choice of the proper tool and machining conditions. The present study examines various turning process parameters, including cutting speed, feed rate, depth of cut, and their significance in determining the surface roughness and material removal rate of glass fiber reinforced polyester (GFRP) tubes. The experimental work is carried out on a CNC lathe. After turning the external diameter of the workpiece according to predetermined turning conditions, average surface roughness (Ra) was measured, and material removal rate (MRR) was calculated. The study utilizes the methodology of Grey Relational Analysis (GRA) and Analysis of Variance (ANOVA) to determine the optimal parametric combination giving the smallest values of Ra and the highest values of MRR.

Application of the vibro-acoustic signal to evaluate wear in the spindle bearings of machining centres. In-service diagnostics in the automotive industry

The correct, undisturbed operation of machines and their components is one of the essential manifestations of an efficient production process. Continuous observation of a machine behaviour is essential to ensure correct and safe operation. The authors of current research focused on evaluation of the using a vibro-acoustic signal to diagnose the wear of spindle bearings of numeric machine tools applied in the automotive industry. More precisely, the effects of using the roll bearings vibrations for the operational diagnostics of spindles in numerical machining centres were investigated. The following parameters were used for analyzing such signals: the acceleration of vibration, the acceleration envelope and the velocity of the vibration. The first part of the research focused on determining the warning thresholds for the observed parameters. In the next stage of the research, the trends of the measured parameters were observed until the warning thresholds were exceeded and the alarm states occurred. An attempt was made to find the reasons for the observed alarm states by analyzing the frequency spectra of the recorded signals at the points where these states occurred. In addition, the diagnosed machine parts (machine tool spindle) were dismantled and inspected in order to link the damage found with the symptoms observed during the recording of diagnostic signals. The relationship between both the signal values and the condition of the diagnosed components as well as between the measured parameters was clearly demonstrated. In the presented article, the authors describe an experiment during which it was possible to register both the normal and emergency state of the object, which is extremely important in industrial diagnostics because one of its limitations is the lack of data from emergency states. In the final conclusions, the authors confirmed the validity of the assumed thesis that the vibroacoustic signal received from the spindle bearings of machines used in mass production in the automotive industry can be used to diagnose the spindles themselves and detect the symptoms of their wear. What can have a significant impact on the quality of manufactured products. This article summarizes the experimental research to date.

Performance Analysis of Electrical Machines Using a Hybrid Data- and Physics-Driven Model

In the design phase of an electrical machine, finite element (FE) simulation are commonly used to numerically optimize the performance. The output of the magneto-static FE simulation characterizes the electromagnetic behavior of the electrical machine. It usually includes intermediate measures such as nonlinear iron losses, electromagnetic torque, and flux values at each operating point to compute the key performance indicators (KPIs). We present a data-driven deep learning approach that replaces the computationally heavy FE calculations by a deep neural network (DNN). The DNN is trained by a large volume of stored FE data in a supervised manner. During the learning process, the network response (intermediate measures) is fed as input to a physics-based post-processing to estimate characteristic maps and KPIs. Results indicate that the predictions of intermediate measures and the subsequent computations of KPIs are close to the ground truth for new machine designs. We show that this hybrid approach yields flexibility in the simulation process. Finally, the proposed hybrid approach is quantitatively compared to existing deep neural network-based direct prediction approach of KPIs.

Accelerated Aging Procedure and Online Method for Stator Insulation Monitoring of Induction Motors

Online methods for stator insulation monitoring have been investigated with increasing interest in the last 15 years. These methods rely on the high-frequency behavior of the electric machine. In converter-fed machines, the high-frequency behavior is visible through the current ringings occurring during the switching commutations of the power electronics. As the lifetime of electric machines can last several decades, a faster way to evaluate insulation monitoring methods is through accelerated aging tests. This work aims to perform an accelerated aging procedure on an induction motor to test an online insulation monitoring method. The motor has a randomly-wound stator winding and it is aged by means of thermal cycles without the need for an oven. A custom converter is employed during the accelerated aging procedure to apply the thermal cycles and to record the current ringings. The proposed insulation monitoring method is based on a metric that quantifies the current ringing variations in a simple manner, following the quasi-linear variation of the parasitic capacitances during the motor aging.

OpenStackDP: a scalable network security framework for SDN-based OpenStack cloud infrastructure

Network Intrusion Detection Systems (NIDS) and firewalls are the de facto solutions in the modern cloud to detect cyberattacks and minimize potential hazards for tenant networks. Most of the existing firewalls, perimeter security, and middlebox solutions are built on static rules/signatures or simple rule matching, making them inflexible, susceptible to bugs, and difficult to introduce new services. This paper aims to improve network management in OpenStack Clouds by taking advantage of the combination of software-defined networking (SDN), Network Function Virtualization (NFV), and machine learning/artificial intelligence (ML/AI) and for making networks more predictable, reliable, and secure. Artificial intelligence is being used to monitor the behavior of the virtual machines and applications running in the OpenStack SDN cloud so that when any issues or degradations are noticed, the decision can be quickly made on how to handle that issue, being able to analyze data in motion, starting at the edge. The OpenStackDP framework comprises lightweight monitoring, anomaly-detecting intelligent sensors embedded in the data plane, a threat analytics engine based on ML/AI algorithms running inside switch hardware/network co-processor, and defensive actions deployed as virtual network functions (VNFs). This network data plane-based architecture makes high-speed threat detection and rapid response possible and enables a much higher degree of security. We have built the framework with advanced streaming analytics technologies, algorithms, and machine learning to draw knowledge from this data that is in motion before the malicious traffic goes to the tenant compute nodes or long-term data store. Cloud providers and users will benefit from improved Quality-of-Services (QoS) and faster recovery from cyber-attacks and compromised switches. The multi-phase collaborative anomaly detection scheme demonstrates an accuracy of 99.81%, average latencies of 0.27 ms, and response speed within 9 s. The simulations and analysis show that the OpenStackDP network analytics framework substantially secures and outperforms prior SDN-based OpenStack solutions for Cloud architectures.

What is it like to be idle versus busy for a service machine?

AbstractCustomers have since long received service from various machines, and this development is expected to accelerate when AI‐powered synthetic agents—such as chatbots and embodied service robots—become more common. Existing research on customers' interactions with service machines is typically focused on perceptions of machine attributes when the machine is busy. However, many machines are idle for a considerable time (i.e., they are not used), and little is known about consumer perceptions of machine idleness—despite the fact that idle machine behavior can contribute to the user experience, too. In the present study, it is assumed that (a) idleness and busyness represent differently valenced states in a human‐to‐human context (i.e., idleness is more negatively charged than busyness for most humans). It is also assumed that (b) anthropomorphism can occur in relation to a service machine, and that (c) beliefs about idleness and busyness from a human‐to‐human context can carry over and inform views of machines' minds. Three experiments were conducted to explore these assumptions, and they show that an idle service machine is attributed less positively charged mind states than a busy service machine. The results also show that such attribution activities affect the overall evaluation of the service machine.