Behavior Of Machine Research Articles

Enhancing power control in doubly fed induction generator based wind turbines: a performance comparison of fuzzy logic and sliding mode control methods

Abstract Effective power regulation is critical for ensuring the stability and performance of Doubly Fed Induction Generator (DFIG)-based wind turbines. This study conducts a comparative study between two control methodologies: fuzzy logic control (FLC) and sliding mode control (SMC), to regulate power in DFIG-based wind turbines. The paper emphasizes the importance of power regulation in wind turbines and its impact on grid stability, presents mathematical equations governing DFIG-based wind turbine systems, encompassing machine equations, power equations, and control objectives related to power regulation. A comprehensive model of the DFIG-based wind turbine system is developed, accounting for dynamic machine behavior and grid interface considerations. The modeli ng process integrates relevant electrical and mechanical components, along with power regulation control strategies. Subsequently, FLC and SMC control methodologies are developed and implemented to regulate desired power. Detailed discussions on controller design and tuning are provided, aligning with specific power control requirements. The results highlight SMC’s effectiveness in achieving precise power regulation within DFIG-based wind turbines, showcasing its superior response characteristics. The study offers valuable insights into the advantages and trade-offs associated with each control approach, aiding researchers and practitioners in selecting the most suitable control methodology for their operational needs. In summary, this paper contributes to the field of wind turbine control systems through a comparative study of FLC and SMC controllers, focusing on regulating power in DFIG-based wind turbines. The findings demonstrate SMC’s superior performance in response, enhancing our understanding of control methodologies for optimizing wind turbine performance and grid integration in sustainable energy systems.

High-frequency Differential Mode Modeling of Universal Motor's Windings

The universal motor is a rotating electrical machine that can operate on either direct current or single-phase alternating current, similar to a DC motor. It has been widely used in various small and inexpensive drives for a long time, mostly in home appliances and hand tools. The noise generated by a universal motor is believed to be closely associated with the electromagnetic torque fluctuations of the machine, which are caused by variations in the current supplied to the motor. The power electronics utilized for controlling the motor's speed are responsible for these current changes. Accurate high-frequency motor models are crucial for reliable electromagnetic interference simulations in motor drive power electronic systems. Research efforts have expanded to explore different realistic configurations that can be used to investigate the electromagnetic compatibility behavior of electrical machines. This study describes a method for predicting the differential mode impedance of universal motor. The behavior of each motor winding has been individually studied through impedance measurements, starting with the armature, followed by the series winding, and finally the inductive compensating winding. The prediction results over a wide frequency range up to 1 MHz are in good agreement with the measurements and enabled us to propose a model circuit for each motor winding.

A model-based approach for gear train whine noise reduction by mesh phasing modification

Heavily loaded gear trains are known to produce a characteristic whine noise and vibration signature, which radiates through the bearings producing an undesired noise emission affecting the NVH behavior of the overall machine. This phenomenon is generated by the variable mesh stiffness and the static transmission error of the gear pairs in mesh within the gear train, but its magnitude strongly depends on how the meshing processes combine between each others. In this context, the present work proposes an analytical methodology to predict amplitude and direction of the variable loads applied on the bearings of idle gears, assuming quasi-static conditions. The dissertation demonstrates that the multiple mesh forces produce a variable load on idle gear bearings describing an ellipse whose shape depends on various geometrical parameters of the system. Numerical examples are shown to assess the potentials of the method and highlight how gear train spatial layout and phasing between different meshings may be used during gearbox design projects to limit the emitted noise of the gear train.

Nothing Like Compilation: How Professional Digital Fabrication Workflows Go Beyond Extruding, Milling, and Machines

Understanding how professionals use digital fabrication in production workflows is critical for future research in digital fabrication technologies. We interviewed thirteen professionals who use digital fabrication for the low-volume manufacturing of commercial products. From these interviews, we describe the workflows used for nine products created with a variety of materials and manufacturing methods. We show how digital fabrication professionals use software development to support physical production, how they rely on multiple partial representations in development, how they develop manufacturing processes, and how machine control is its own design space. We build from these findings to argue that future digital fabrication systems should support the exploration of material and machine behavior alongside geometry, that simulation is insufficient for understanding the design space, and that material constraints and resource management are meaningful design dimensions to support. By observing how professionals learn, we suggest ways digital fabrication systems can scaffold the mastery of new fabrication techniques.

EFFECT OF MACHINABILITY OF GNP–GFRP COMPOSITES ON TENSILE STRENGTH AND FATIGUE BEHAVIOR

The paper focuses on the cutting behavior of Glass Fiber Reinforced Polymer (GFRP) composites and GNP–GFRP composites that contain varying amounts of Graphene Nano Platelets (GNP). GFRP composites are increasingly being used in a variety of industrial applications due to their excellent mechanical properties, such as high strength, stiffness, and low weight. However, their machining and cutting behavior can be challenging due to the presence of the reinforcing fibers. Therefore, the study aims to investigate the machining behavior of GFRP composites and the effect of adding GNP on their cutting behavior. The effect of different parameters such as cutting speed, feed rate and reinforcement rate on cutting forces and delamination factor is investigated. In addition, the tensile strength and fatigue behavior of the composite materials with the best and worst delamination factors were also determined. Addition of up to 0.2 wt.% of GNP to GFRP composites resulted in an increase in cutting forces and delamination factor when drilling GFRP composites. While the cutting force and delamination factor decreased with the increase in cutting speed, the cutting force and delamination factor increased with the increase in the feed rate. Analysis of variance (ANOVA) was performed to determine the effects of drilling parameters and reinforcement ratio on cutting force and delamination factor according to full factorial experimental design. The most efficient factor on the cutting forces is found to be feed rate (84.97%), followed by the reinforced rate (6.48%) and cutting speed (6.13%). The most efficient factor on the delamination factor is determined to be feed rate for (44.49%), followed by the reinforced rate (29.20%) and cutting speed (21.73%).

Microstructure effect on the machinability behavior of additive and conventionally manufactured Inconel 718 alloys

The microstructural characteristics and mechanical properties of forged parts differ significantly from those of additively manufactured parts, which results in different machinability. In this paper, the microstructure effect on the machinability of Inconel 718 parts obtained by different manufacturing processes, laser directed energy deposition (LDED), laser powder bed fusion (LPBF) and forging, is evaluated and critically discussed in terms of milling forces, surface morphology, surface roughness, chip morphology and subsurface microstructure. The results show that due to having the smallest grain size, high dislocation density, and precipitation hardening provided by the reinforcing phases, the milling force is maximized when cutting LPBF Inconel 718 parts. In addition to grain size and dislocation density, the texture also has an effect on the plastic deformation of the subsurface after milling. Furthermore, LDED and forged Inconel 718 parts undergo a continuous dynamic recrystallization process characterized by sub-grain rotation transforming low angle grain boundaries into high angle grain boundaries during milling. This study provides some theoretical guidance for the integration of additive and subtractive processes in the machining of complex structural parts with Inconel 718 alloy.

Impact of radial rake angle and machining constraints on the machining behavior and functionality of additive manufactured inconel 718 alloy

The Additive manufactured (direct metal laser sintering) samples has shown greater surface roughness which leads to reduce the functionality of the components. Additionally, the cutting force requirement and obtainable surface finish show the vigorous role during the slot milling process of many materials. In this study, the slot milling process has been conducted on the Additive Manufactured (AM) Inconel 718 alloy under numerous cutter nomenclature such as −7°, 0° and +7° in radial rake angle and machining circumstances such as a cutting feed (5, 15, and 25 mm min−1) and cutting speed (14.13, 28.26, and 42.39 m min−1) to analyze its impact on the cutting force requirement and surface roughness of AM Inconel 718 alloy through TOPSIS statistical tool. Through the above analyses, it is found that the cutter nomenclature plays a vigorous role in the cutting force requirement and obtainable surface roughness followed by the cutting feed and cutting speed, owed to the establishment of moderate strain strengthening and thermal soothing effect.

PWM Voltage-Based Modeling for PM Machines With Interturn Short Circuit Fault Considering the Effect of Drives

This paper presents a novel analytical fault model based on PWM voltage rather than ideal sinewave phase voltage for permanent magnet (PM) machines with inter-turn short circuit fault. This model is important because PM machines are usually driven by the pulse width modulation (PWM) inverter. In addition, main contributors to the accuracy of the fault model such as (i) the cables between the inverter and the machine, (ii) the precision of the rotor position estimation and (iii) the inverter, in particular the metal-oxide semiconductor field-effect transistors (MOSFETs) have been considered. Therefore, this developed model can fully account for the effects of PWM harmonics and accurately predicts the machine's behaviors (particularly the fault current) under fault conditions considering the effect of the drives. This has rarely been studied in the literature as most existing fault modelling methods adopt sinewave phase voltage as inputs, which cannot account for the PWM harmonics in the fault currents. However, the investigations in this paper showed that the PWM harmonic component could be close to 25% of the fundamental component in the fault current, and hence cannot be neglected. The accuracy of the proposed fault model has been validated by a series of experimental tests.

Experimental Validation of a Mesh-to-Mesh Magnetic Force Projection for e-NVH Simulation

This article discusses the use of simulation for analyzing the vibro-acoustic behavior of radial flux electrical machines under magnetic forces. Numerical simulation enables a systematic investigation of electromagnetic Noise, Vibration and Harshness (e-NVH) issues, but requires accurate modeling and this despite manufacturing uncertainties. Experimental validation is a necessary step to setup a multiphysic virtual prototyping e-NVH workflow. In particular, a key point is the magneto-mechanical coupling. In this study, the Virtual Work Principle (VWP) is used for magnetic force calculation. The goal is to propose an accurate e-NVH model applied to a 12s10p radial flux electrical machine using a mesh-to-mesh projection. This e-NVH model enables to understand the origin of the noisiest orders along the whole speed range of the machine. Differences between simulation and experiments are discussed thanks to the modal participation factor of specific harmonics. Mesh-to-mesh projection results are compared to those obtained by using lumped tooth force, highlighting the contribution of the tooth tip moment to the vibration. Finally, the tooth modulation effect is discussed in the light of these results.

Impact of 13-93 bio-glass inclusion on the machinability, in-vitro degradation, and biological behavior of Y-TZP-based bioceramic composite

Nowadays, bio-ceramic composite material is demanded in the biomedical industry due to its remarkable biological and mechanical properties. But, the shaping of such material challenges the manufacturer. In this work, bioceramic composite materials, having general weight composition [(100-x) (3Y-TZP) – x (13-93 BG)] where x = 0 to 25 wt%, has been prepared, and their machinability in terms of MRR (material removal rate) and SR (surface roughness) is studied as a function of BG composition and machining temperature. It is found that MRR decreases with increasing the concentration of bioactive glass (BG) while it increases with increasing the machining temperature. The SR is found in the opposite trend. The MRR, SR, and material removal mechanics (MRM) are also confirmed by the HR-SEM and AFM (atomic force microscopy) analysis. The effect of BG addition on the physical, mechanical, in-vitro degradation, and cell culture behaviour of the composite materials are also evaluated. The relative density and mechanical properties, such as flexural and compressive strength, are found to increase up to 10 wt% of BG content; afterward, it decreases. The hardness is increased with BG addition in 3Y-TZP ceramics. The in-vitro degradation indicates the dissolution and apatite layer formation after 35 days of immersion in simulated body fluid (SBF) solution. The cell proliferation is examined by MTT assay over MG-63 cell lines. The cell proliferation ability of the composite is increased with increasing the BG concentration and found a maximum of up to 25 wt% of BG. Overall, the best findings were obtained with 10 wt% BG contained 3Y-TZP ceramic composite sintered at 1250 °C. The relative density, flexural strength, and compressive strength are about 98.34%, 397.9 MPa, and 488.7 MPa, respectively. Based on the obtained results, it may be recommended that 10 wt% BG concentration in 3Y-TZP bioceramic composite is the most suitable and potential material for biomedical application.

Stingy bots can improve human welfare in experimental sharing networks

Machines powered by artificial intelligence increasingly permeate social networks with control over resources. However, machine allocation behavior might offer little benefit to human welfare over networks when it ignores the specific network mechanism of social exchange. Here, we perform an online experiment involving simple networks of humans (496 participants in 120 networks) playing a resource-sharing game to which we sometimes add artificial agents (bots). The experiment examines two opposite policies of machine allocation behavior: reciprocal bots, which share all resources reciprocally; and stingy bots, which share no resources at all. We also manipulate the bot’s network position. We show that reciprocal bots make little changes in unequal resource distribution among people. On the other hand, stingy bots balance structural power and improve collective welfare in human groups when placed in a specific network position, although they bestow no wealth on people. Our findings highlight the need to incorporate the human nature of reciprocity and relational interdependence in designing machine behavior in sharing networks. Conscientious machines do not always work for human welfare, depending on the network structure where they interact.

Fault Detection for Point Machines: A Review, Challenges, and Perspectives

Point machines are the actuators for railway switching and crossing systems that guide trains from one track to another. Hence, the safe and reliable behavior of point machines are pivotal for rail transportation. Recently, scholars and researchers have attempted to deploy various kinds of sensors on point machines for anomaly detection and/or incipient fault detection using date-driven algorithms. However, challenges arise when deploying condition monitoring and fault detection to trackside point machines in practical applications. This article begins by reviewing studies on fault and anomaly detection in point machines, encompassing employed methods and evaluation metrics. It subsequently conducts an in-depth analysis of point machines and outlines the envisioned intelligent fault detection system. Finally, it presents eight challenges and promising research directions along with a blueprint for intelligent point machine fault detection.

Hacking Surveillance Cameras, Tricking AI and Disputing Biases: Artistic Critiques of Machine Vision

In the field of AI, troublesome machine behaviour is a recurring problem, and is particularly worrying when the governance of populations is externalised to machines. This article will focus on machine vision and explore whether hacking as a concept, a method and an ethic, as it has been appropriated by artists, makers and designers, offers ways for citizens to resist surveillant vision. By combining distant and close readings of art hacks in the ‘Database of Machine Vision in Art, Games and Narratives’ this article demonstrates a shift in resisting machine vision from hacking sensorial devices to tricking intellectual seeing. I call it the ‘intuition machine shift’ and argue that emergent with this shift is an art hacking strategy which specifically challenges biased machine vision. Drawing from critical making, tactical media and feminist theorisation of hacking, and adopting Mareille Kaufmann’s understanding of hacking as a form of disputing surveillance, this article outlines three artistic approaches to hacking machine vision: hacking surveillance cameras, tricking AI and disputing biases. The conceptual contribution of disputing biases is developed further to offer new nuanced understandings of risks and potentials of art hacks to resist biased machine vision.

Comparison of fatigue behaviour and fracture toughness of cobalt chromium alloy fabricated by casting and selective laser melting methods

Co-Cr alloys are most commonly used base metal alloy in dentistry for the fabrication of many types of metallic substructures. They gained popularity due to their good mechanical properties and low cost. There are different techniques for fabrication of Co-Cr alloys which includes casting and DMLS technique. However, change in the fabrication techniques showed variation in mechanical properties. Forty Co-Cr samples each were made from two different techniques, Casting and DMLS. Fracture toughness was done with charpy’s impact test machine and fatigue behaviour test was done with nano universal testing machine. The samples were later observed under stereo electron microscope to assess the kind of failure. Unpaired t test revealed that there was statistically significant difference between two different groups. The mean difference of fracture toughness in Casting group was higher when compared with DMLS group. The number of fatigue cycles were higher in DMLS group than the casting group. Within the limitations of this in vitro study, casting group showed higher fracture toughness when compared to DMLS group, whereas fatigue limit was higher in DMLS group than casting group.

Study of Microstructural, Machining and Tribological Behaviour of AA-6061/SiC MMC Fabricated Through the Squeeze Casting Method and Optimized the Machining Parameters by Using Standard Deviation-PROMETHEE Technique

Study of Microstructural, Machining and Tribological Behaviour of AA-6061/SiC MMC Fabricated Through the Squeeze Casting Method and Optimized the Machining Parameters by Using Standard Deviation-PROMETHEE Technique

Effect of Bioceramic Reinforcement on Mechanical and Machinability Behaviour of AZ31 Magnesium Alloy Composites

Effect of Bioceramic Reinforcement on Mechanical and Machinability Behaviour of AZ31 Magnesium Alloy Composites

General Method to Foresee the Behavior of Virtual Synchronous Machines Working With Distorted and Unbalanced Voltage Conditions

To make photovoltaic and wind power plants able to provide grid services (i.e., inertial behavior, grid support and harmonic compensation), several control algorithms have been proposed in the literature during the last years. The most promising ones make the power electronic converters behave as conventional alternators, using the concept of Virtual Synchronous Machine (VSM). Several VSM models are available in the literature, some of which can improve the voltage quality at the point of connection with the grid behaving as harmonic and unbalance sinks under non–ideal grid voltage conditions. However, the literature lacks a general method to foresee the behavior of a generic VSM configuration in such conditions along with a well–established definition of the needed features to make VSMs able to work as harmonic or unbalance sinks. Therefore, this paper proposes a simple and general method to foresee the behavior of different VSM configurations under non–ideal grid voltage conditions before any experimental verification. The proposed method accurately foresees the VSMs behavior, as experimentally demonstrated on five VSM models available in the literature, working with fifth harmonic and inverse sequence voltage distortions. Moreover, the method identifies which VSM configuration can feature a beneficial harmonic and unbalance compensation.

Additive manufacturing: Application and validation of machine learning-based process-structure-property linkages in Ti-6Al-4V

In the field of metal additive manufacturing (AM), various processes and heat treatments can yield unique grain morphologies, thereby influencing material properties and machining behavior. In this study, a novel workflow using a machine learning-based approach that combines statistical descriptors of textured AM-process induced microstructure, cutting force model (as a material response), and a data-mining method is established. It is proven to be a valid method for creating process-structure-property linkages for metal AM. This study focuses on two highly varied metal AM processes: Powder bed fusion (PBF, e.g., laser PBF and electron beam PBF) and directed energy deposition (DED, e.g., wire-fed plasma-directed energy deposition). The study also accounted for the effects of post-AM heat treatment and build orientation. It was found that the accuracy of material behavior predictions is highly correlated with AM processing conditions, building orientations, and machining conditions. Specifically, while initially applying PBF training data to DED samples resulted in a 15% root mean square prediction error, this error was subsequently reduced to <1% through cross-training using combined microstructure training data sets. This discrepancy could be attributed to the significantly different thermal cycling conditions in L-PBF and DED, which resulted in highly varied textured microstructures. Residual stresses generated during AM processing and the selection of machining parameters exert the highest impact on the machining behavior. The implications of these findings extend to the use of statistically descriptive microstructures for various AM processing conditions and build orientations in computational methods and other machining learning approaches.

Towards a model of human-cyber–physical automata and a synthesis framework for control policies

Advances in research and increasing applications of Cyber–Physical Systems (CPSs) show the need to consider factors of humans in the loop. This has led to the growing research focus on Human-Cyber–Physical Systems (HCPSs). In general, humans in an HCPS interact with both the cyber and physical systems, as well as among the humans themselves. For a better understanding, correct design, development, operation, and maintenance of HCPSs, a computational theory based on a computational model is required. This paper presents our initial work towards a model of human-cyber–physical automata (HCPA). We consider an HCPS as a combination of a human-physical system (HPS) and a CPS in which the control switches between the humans and the machines. We define an HCPA by connecting the automaton of the HPS and the automaton of the CPS through a switch control automaton. The switch control automaton makes switching decision in some critical states shared by the HPS and the CPS. Our theorem shows that the control switching between the HPS and the CPS increases the probability of satisfying a given property. We model the behaviour of a human in specified applications or even in carry out specific tasks, instead of general human intelligence. Therefore, a human can make mistakes to decision making and thus it is a probabilistic automaton with learning ability. The switching between the human and the machine is modelled by an oracle. The oracle learns about the human behaviour, the machine behaviour, as well as the environment to make the control decisions. To generate the control policies of the human and the oracle, we propose a synthesis framework to maximize the probability of the satisfaction of a property specified in Linear Temporal Logic (LTL) by the HCPA. We present a prototype implementation of the framework by extending the model-free reinforcement learning (RL) algorithm and model-free deep-RL algorithm, and our experiment shows that our synthesis framework is effective in obtaining switch policies.

Let the loss impartial: a hierarchical unbiased loss for small object segmentation in high-resolution remote sensing images

ABSTRACT The progress in optical remote sensing technology presents both a possibility and challenge for small object segmentation task. However, the gap between human vision cognition and machine behavior still poses an inherent constrains to the interpretation of small but key objects in large-scale remote sensing scenes. This paper summarizes this gap as a bias of the machine against small object segmentation task, called scale-induced bias. The scale-induced bias causes the degradation in the performance of conventional remote sensing image segmentation methods. Therefore, this paper applies a straightforward but innovative insight to mitigate the scale-induced bias. Specifically, we propose a universal impartial loss, which leverages the hierarchical approach to alleviate two sub-problems separately. The pixel-level statistical methodology is applied to remove the bias between the background and small objects, and an emendation vector is introduced to alleviate the bias between small object categories. Extensive experiments explicitly manifest that our method is fully compatible with the existing segmentation structures, armed with the hierarchical unbiased loss, these structures will achieve satisfactory improvement. The proposed method is validated on two benchmark remote sensing image datasets, where it achieved a competitive performance and could narrow the gap between the human vision cognition and machine behavior.