Behavior Of Machine Research Articles

High Speed UART Implementation Using VHDL

Abstract: The Universal Asynchronous Receiver and Transmitter (UART) could be a custom designed circuit that allows serial communication between a laptop and a computer peripheral. The complex nature of combined circuit generation has made machine design time consuming at the gate and switch flop levels. As a results of this reality, the style designer made the choice to use hardware description language during the virtual machine layout process. VHDL could be a hardware description language that's accustomed model digital systems. It contains information that you just may find useful. It includes information which will be accustomed explain the virtual machine's behaviour shape, also because the ability to explicitly specify its timing. VHDL could be a difficult and verbose language with many complicated assembles that have complicated semantic meanings and is difficult to grasp initially. The language facilitates hierarchical machine modelling likewise as top don methodologies. It provides an easier method for checking the UART and assisting within the discovery of any discrepancies. It also allows for a more behavioural explanation of the module's characteristics. It makes the look implementation easier to read and understand, and it also provides the flexibility to simply describe dependencies among numerous procedures that arise in complex eventdriven systems. Thus, the layout employs VHDL as a layout language to reap the transmitter module. First, the running version of the transmitter is defined. Then, using VHDL, all of the transmitter's blocks are designed and defined.

An overview of computer programming languages: A fundamental piece of knowledge for humanity

The major goal of this review is to standardise the programming languages that are useful for controlling the behaviour of machines on computers. Syntax and semantic norms are defined in programming languages, just as they are in natural languages. Over a hundred thousand programming languages exist, with new ones appearing every year. Few languages ever gain widespread adoption, yet talented programmers may use dozens of languages over the course of their careers. The authors looked at the most prominent standized programming languages' runtime performance, memory utilisation, applications, and key pros and drawbacks in this review.

Smart connected worker edge platform for smart manufacturing: Part 2—Implementation and on‐site deployment case study

AbstractIn this paper, we describe specific deployments of the Smart Connected Worker (SCW) Edge Platform for Smart Manufacturing through implementation of four instructive real‐world use cases that illustrate the role of people in a Smart Manufacturing paradigm through which affordable, scalable, accessible, and portable (ASAP) information technology (IT) acquires and contextualizes data into information for transmission to operation technologies (OT). For case one, the platform captures the relationships between energy consumption and human workflows for improved energy productivity while workers interact with machines during semiconductor manufacturing. The platform utilizes human cognition to identify anomalous machine behavior for root cause analysis of system faults via neural network (NN) that recognize alarm postures of workers with cameras. For case two, a smart assembly line is demonstrated for state monitoring and fault detection. Machine learning (ML) models are used to recognize system states and identify fault scenarios with human intervention. For case three, the platform monitors human–machine interactions to classify manufacturing machine states for proper operations and energy productivity. Internal energy states of individual or collections of manufacturing equipment are determined via NN based algorithms that disaggregate signals associated with smart metering typically deployed at manufacturing facilities. These methods predict the real time energy profile of each machine from the total energy profile of a manufacturing site. For case four, a software defined sensor system built with scientific workflow engines is demonstrated for contextualizing data from laser surface refraction for characterization, and diagnostics in the processing of additively manufactured titanium alloy.

Influence of grinding parameters on surface roughness and temperature under carbon nanotube assisted MQL

ABSTRACT Conventional flood cooling system has some environmental and economic issues. Minimum quantity lubrication (MQL) has been established as a better strategy to control the machining temperature than conventional flood cooling system. Integrated use of carbon nanotube (CNT) with MQL has been drawing attention in recent times. The purpose was to study the effects of hard grinding with key focus on temperature, surface roughness and chips morphology under the application of CNT-mixed MQL and compressed air. The hardness of work material (AISI 1060 steel) was varied at three values (40, 45 and 50 HRC) with the variation of fluid flow rates and infeed rates to observe the machining behaviour. Experimental plan was established using full factorial design of experiment while mathematical models and optimisation were constructed following response surface methodology (RSM). Optimum output responses were flow rate 75 ml/h, infeed (depth of cut) 10 µm and hardness of material is from 40 to 45 HRC. Fluid flow rate of 75 ml/h resulted in best surface finish while work material of 40 HRC hardness generated lowest temperature while machining. A lower hardness values and a moderate flow rate produced favourable machining outcomes, i.e. lower temperature and improved surface finish.

Towards common ethical and safe ‘behaviour’ standards for automated vehicles

Automated vehicles (AVs) aim to dramatically improve traffic safety by reducing or eliminating human error, which remains the leading cause of road crashes. However, commonly accepted standards for the ‘safe driving behaviour of machines’ are pending and urgently needed. Unless a common understanding of safety as a design value is achieved, different manufacturers’ driving styles may emerge, resulting in inconsistent, unpredictable and potentially unsafe ‘behaviour’ of AVs in certain situations. This paper aims to explore the main gaps and challenges towards establishing shared safety standards for the ‘behaviour’ of AVs, and contribute to their responsible traffic integration, by reviewing the state-of-the-art on AV safety in the core relevant disciplines: ethics of technology, safety science (engineering & human factors), and standardisation. The ethical and safety aspects investigated include the users’ perception of AV safety, the ethical trade-offs in critical decision-making contexts, the pertinence of data-driven approaches for AVs to mimic human behaviour, and the responsibilities of various actors. Moreover, the paper reviews the current safety patterns, metrics (surrogate measures of safety – SMoS) and their thresholds introduced in existing research for three use cases: mixed traffic of AV and conventional vehicles, AV interaction with pedestrians and cyclists, and transition of control from machine to human driver. The results reveal several knowledge gaps within each discipline and highlights the lack of common understanding of safety across disciplines. On the basis of the results, the paper proposes a framework for further research on AV safety, identifying concrete opportunities for interdisciplinary research, with common goals and methodologies, and explicitly indicating the path for transfer of knowledge between sectors.

System Frequency Response Model Considering the Influence of Power System Stabilizers

Frequency stability analysis of large power systems are extremely time consuming, laborious and may even exceed the computational capacity of modern computers. Hence, simplified power system models have being developed in the literature. These models are usually called System Frequency Response (SFR). In SFR models, generators are represented by transfer functions, nonlinearities are generally neglected and the grid is not taken into account. Conventional SFR models only contemplate the mechanical behavior of speed governors, turbines and synchronous machines of generators. This is because, a common simplification is to consider that frequency and voltage can be controlled independently. However, it is demonstrated that there is an interaction between them, so frequency can be affected by the effect of power system stabilizers (PSSs) over excitation system controllers. In this work, a modified SFR model is proposed, considering the influence of generators excitation control on frequency. Simulation results show an improvement of the accuracy in the estimation of frequency response of the power system.

Dynamic Modeling and Control of Pole-Phase Modulation-Based Multiphase Induction Motor Drives

Pole-phase modulation based multiphase induction motor (PPMIM) drives possess the capability of providing an extended range of speed-torque for high power traction application with the available additional degrees of freedom in the multiphase machine. As observed in previous open-loop results of PPMIM drives presented in the literature, the transient currents often exceed twice the rated currents. To address this issue, this paper focuses on the modelling and advanced control of the PPMIM drives during all possible pole-phase combinations. The dynamic mathematical modelling of the PPMIM in actual phase variable domain is presented in detail. Since this model involves time-dependent inductances and torque, the machine model equations are transformed into dq domain using transformation matrices. The transformation matrices, modelling equations in the arbitrary reference frame and multiphase inverter are modelled by considering all parameters of possible pole-phase combinations. Based on the proposed modelling equations, an indirect field oriented control (IFOC) is implemented for PPMIM drives for smoother operation in all possible modes of operation. The modelling equations, as well as IFOC of PPMIM drive, are implemented in MATLAB to illustrate the behaviour of machine during transients as well as different load torques. The proposed concepts are also validated on laboratory setup and the hardware results are demonstrating the smoother transition as well as steady state operation of PPMIM drive in pole changeovers as well as both pole-phase combinations.

Machine learning and speed in high-frequency trading

The creative destruction wrought by high-frequency algorithmic trading has raised increasing concerns about the effect of machine learning behaviors and ultra high-frequency trading on financial markets. By employing a genetic algorithm with a classifier system as an adaptive learning tool, we address some of these concerns by studying a dynamic limit order market model with asymmetric information and varying speeds of high-frequency trading (HFT). We show that HFT benefits uninformed traders, improves information efficiency but reduces market liquidity. We find that there is a trade-off where a competition effect erodes the information and speed advantages of high-frequency traders, increasing trading speeds of HF traders reduces market liquidity but generates a hump-shaped relationship to the profitability of high-frequency traders and information efficiency. This research finds there may be potential benefits to throttling the trading speed arms race to improve market efficiency. We also find that strategic algorithmic trading compensates for diminishments in speed advantages, providing an insight on machine behavior in the FinTech age.

Effect of industrial waste fly ash on the drilling characteristics of banana fiber residue reinforced polymer composites

Natural fiber reinforced composites are becoming popular in various industrial and commercial applications as structural components. Unlike conventional materials, machining of fiber composites is critical due to its heterogeneous structure. Hence, it is necessary to understand the machining behaviour of fiber reinforced composites. Hybrid composites reinforced with banana fiber and fly ash filler in polyester matrix were fabricated and then evaluated for drilling performance. Drilling experiments were performed using the Taguchi L27 orthogonal array. The influence of feed rate (A), cutting speed (B) and filler content (C) on thrust force, surface roughness, and delamination factor entry and exit were using Taguchi and Analysis of variance (ANOVA) techniques. The results revealed that (i) Minimum thrust for attained at filler content (A) 1 vol %, speed (B) 3000 r/min, and feed (C) 100 mm/min, (ii) Minimum surface roughness at filler content A (3 vol %), speed B (2000 r/min), and feed rate C (100 mm/min) and (iii) Minimum peel up and push out delamination at filler content (A) as, 5vol %, speed (B) 2000 r/min, and feed (C) 100 mm/min. This study provides machining insights on agro-industrial waste based polymer composites for structural applications.

Experimental assessment and validation of inertial behaviour of virtual synchronous machines

Increasing integration of converter-interfaced renewable generation has led to significant operational challenges for power systems. Such challenges are mainly caused by the different capabilities and dynamic responses of the converters compared with synchronous machines, for example, converters do not naturally provide inertia to the system. Virtual Synchronous Machine (VSM) is considered as a promising solution to address the challenges associated with reduced system inertia via the provision of emulated inertial response to support the operation of converter-dominated power systems. However, it has been observed that the dynamic behaviour of the VSM could differ significantly from that of a Synchronous Condenser (SC) and a Synchronous Generator (SG) in terms of inertial response provision, even when the VSM is configured with the same inertia constant. Furthermore, effective practical methods for evaluating the damping performance of VSMs are not presently available. To gain a better understanding and achieve a more accurate assessment of the dynamic inertial and damping performance of VSMs, this paper presents an experimental methodology for systematic evaluation of the dynamic response of the VSM in the frequency domain using the Network Frequency Perturbation (NFP) method. Experimental design and implementation of the NFP method are presented to assess VSM system's equivalent inertia and damping constants, where the VSM system under test can be treated as a black box without any knowledge of internal settings and control design. Case studies are conducted, where the proposed experimental design has been applied for testing and assessing the inertial and damping constants of a physical 246 kVA VSM prototype driven by a Battery Energy Storage System with comparison of the SC and SG with equivalent inertia constant. Power-Hardware-in-the-Loop (PHiL) testing is also conducted to demonstrate the VSM's inertia performance. The studies demonstrate that the developed experimental approach based on NFP method provides a valuable tool for network operators and manufacturers for evaluating the inertial and damping performance.

Influence of Fluid Film Bearings with Different Axial Groove Shapes on Automotive Turbochargers: An Experimental Study

Most commercial automotive turbochargers (TC) employ semi-floating ring bearings (SFRB) with axial groove shapes. In order to bring some insights into the role played by the axial groove geometry on the dynamics of TC, this work deals with an experimental study of the rotordynamic behavior of a stock automotive turbocharger operating on SFRB with two different groove shapes, which have the same volume and width, and with the same number of grooves. The rotating machine behavior has been evaluated under different operating conditions using a test bench specially designed to analyze turbochargers. Rotordynamic (RD) characteristics of automotive turbochargers are estimated to evaluate the influence of the axial groove geometry on the machine vibratory behavior. Frequency spectra and orbital plots of the rotor are obtained from accelerometers and proximity probes mounted on the turbocharger. The comparative analysis of the vibrational behavior of automotive turbochargers running on different supporting systems allows the identification of the role played by the axial grooves on the machine rotordynamic performance. The experimental results rendered in this work permit to classify the influence of the axial groove geometry on the turbocharger rotordynamic behavior for several speed and flow conditions.

Masked face recognition: Human versus machine

The recent COVID-19 pandemic has increased the focus on hygienic and contactless identity verification methods. However, the pandemic led to the wide use of face masks, essential to keep the pandemic under control. The effect of wearing a mask on face recognition (FR) in a collaborative environment is a currently sensitive yet understudied issue. Recent reports have tackled this by evaluating the masked probe effect on the performance of automatic FR solutions. However, such solutions can fail in certain processes, leading to the verification task being performed by a human expert. This work provides a joint evaluation and in-depth analyses of the face verification performance of human experts in comparison to state-of-the-art automatic FR solutions. This involves an extensive evaluation by human experts and 4 automatic recognition solutions. The study concludes with a set of take-home messages on different aspects of the correlation between the verification behaviour of humans and machines.

Texturing design of WC-Co through laser parameter selection to improve lubricant retention ability of cutting tools

Laser Surface Texturing (LST) is widely used to modify hard material surfaces improving their physic-chemical and mechanical properties. This technology is particularly relevant for tungsten carbides, a material that requires high complexity methods when other micro-machining processes are used. LST allows innovative cutting tool designs that improve the machining behavior and enlarge the cutting tool lifetime.This research analyses the influence of LST parameters on the track dimensions, roughness, microstructure, hardness, and lubricant retention ability of the modified surfaces. Twelve combinations of energy density of pulse and scanning speed created different geometrical patterns on WC-Co surfaces. LST parameters were related to specific shape and dimensions of the linear grooves. Energy density was proven as the most influential parameter for dimensional characteristics and roughness values. Specific channel morphologies increased the lubricant expansion area up to 50%, leading the lubricant to a linear track direction. Low scanning speed and high energy density also increased the surface hardness up to 20%. The surface composition was also modified. The thermal effect of the laser treatments and the non-protective atmosphere increased the oxygen on the surface and modified the WC-Co microstructure. However, the thermal affected zone is considerably low compared to other texturing processes.

Analysis of the high-efficiency and low-damage abrasive processing mechanism for SiC based on the SPH simulation of single-grain indentation and scratching

During the wafer fabrication procedure, abrasive machining occupies a large proportion in time and economic cost, mainly including grinding, lapping, and polishing. The third-generation semiconductor materials, represented by SiC, have the properties of high hardness, large brittleness, and strong chemical inertness, which make abrasive machining more challenging. To improve the machining efficiency and quality, this paper applied the smoothed particle hydrodynamics (SPH) method to simulate the machining behavior of single abrasive grain for SiC wafer. The micro-mechanical mechanisms of the main influencing parameters, such as velocity, depth, and angle, were studied in abrasive machining for SiC wafer. First, it is proved that using SPH coupled with a finite element to study the mechanical effects of abrasive machining for SiC wafer is a feasible and useful method. Then, the explanations for the beneficial effects of ultrasonic vibration-assisted machining, high-speed machining, and other abrasive machining are provided through the analysis of the simulated results. In addition, the optimization basis for parameters such as scratching velocity, scratching depth, and ultrasonic vibration frequency is obtained. This research provides a good insight into implying the micro-mechanical mechanisms of abrasive machining and achieving the optimization for the abrasive machining of hard-brittle wafer materials, which help avoid the shortcomings of experimental research and produce economic benefits.

Anomaly Detection Using Autoencoder Reconstruction upon Industrial Motors.

Rotary machine breakdown detection systems are outdated and dependent upon routine testing to discover faults. This is costly and often reactive in nature. Real-time monitoring offers a solution for detecting faults without the need for manual observation. However, manual interpretation for threshold anomaly detection is often subjective and varies between industrial experts. This approach is ridged and prone to a large number of false positives. To address this issue, we propose a machine learning (ML) approach to model normal working operations and detect anomalies. The approach extracts key features from signals representing a known normal operation to model machine behaviour and automatically identify anomalies. The ML learns generalisations and generates thresholds based on fault severity. This provides engineers with a traffic light system where green is normal behaviour, amber is worrying and red signifies a machine fault. This scale allows engineers to undertake early intervention measures at the appropriate time. The approach is evaluated on windowed real machine sensor data to observe normal and abnormal behaviour. The results demonstrate that it is possible to detect anomalies within the amber range and raise alarms before machine failure.

Analysis of NVH Behavior of Synchronous Reluctance Machine for EV Applications

In this paper, an analysis of noise and vibration of a synchronous reluctance machine for EV applications is performed. The analyzed machine was designed for electric vehicle application. The noise and vibration of a synchronous reluctance machine were first estimated during simulations; next, the obtained results were validated during laboratory tests. The analyzed model of the machine was simplified and included only stator core as it was assumed to be the main source of the machine vibration and generated noise. To simulate the noise and vibration of the machine, multiphysics modeling of the machine was performed. Laboratory tests proved the correctness of performed simulations. The obtained results allowed us to investigate the influence of the machine’s operating point on the generated noise and vibration. The frequency of the magnetic radial forces were proven to be the dominant factor in noise generation. The influence of the load and current angle on the machine’s noise and vibration was proven to be negligible. It was also proven that considering only the stator structure in numerical analysis of the noise and vibration of the machine leads to valuable results.

Locust Mayfly Optimization-Tuned Neural Network for AI-Based Pruning in Chess Game

The art of mimicking a human’s responses and behavior in a programming machine is called Artificial intelligence (AI). AI has been incorporated in games in such a way to make them interesting, especially in chess games. This paper proposes a hybrid optimization tuned neural network (NN) to establish a winning strategy in the chess game by generating the possible next moves in the game. Initially, the images from Portable Game Notation (PGN) file are used to train the NN classifier. The proposed Locust Mayfly algorithm is utilized to optimally tune the weights of the NN classifier. The proposed Locust Mayfly algorithm inherits the characteristic features of hybrid survival and social interacting search agents. The NN classifier involves in finding all the possible moves in the board, among which the best move is obtained using the mini-max algorithm. At last, the performance of the proposed Locust mayfly-based NN method is evaluated with help of the performance metrics, such as specificity, accuracy, and sensitivity. The proposed Locust mayfly-based NN method attained a specificity of 98%, accuracy of 98%, and a sensitivity of 98%, which demonstrates the productiveness of the proposed mayfly-based NN method in pruning.

Low Vibrations and Improved NVH in Permanent Magnet Synchronous Machines Due to Injection of Flux-Linkage Harmonics

Harmonic currents have a significant influence on the vibration behavior of electrical machines. Nevertheless, it remains a challenge to determine the optimum amplitude and phase position of the harmonic currents. This article concentrates on the injection of flux-linkage harmonics to enable acoustic optimization for permanent magnet synchronous machines (PMSMs). The test bench verification of this method using a commercial drive from a luxury class hybrid car proves that vibrations of the problematic mode zero are almost completely eliminated. The total sound pressure level of the machine is reduced by approximately 10 dB compared to the usual field-oriented control. Besides the advantage on the acoustic behavior, this article evaluates the impact of the harmonic flux-linkage injection on current amplitude, average torque, and efficiency.

Analysis of machining characteristics in electrical discharge machining of SiC and Al2O3 reinforced AA6061 hybrid metal matrix composites using Taguchi and ANOVA techniques

In this study, the machining behaviour of aluminium alloy (AA6061)/10% aluminium oxide (Al2O3)/10% silicon carbide (SiC) hybrid metal matrix composites was analyzed by Electric Discharge Machining (EDM). The stir casting method was used to fabricate the composite in a volume percentage of 80, 10, and 10 for AA6061, Al2O3 and SiC, respectively. The specimen was prepared in the size of 30 mm in diameter and 12 mm in thickness from the fabricated composite by the Wire-cut Electric Discharge Machining (WEDM) process. The fabricated specimens were machined using EDM by varying the input parameters of pulse-ON time (T–ON) in μs, pulse-OFF time (T–OFF) in μs, and gap current (GC) in amps at three different levels. The design of the experiment by the Taguchi method was used with the L9 orthogonal array. The impact of process parameters on output variables such as Surface Roughness (SR), Machining Time (MT) and Material Removal Rate (MRR) was studied, and an Analysis of Variance (ANOVA) was carried out to identify the most influencing parameter. The GC is observed as the significant input parameter on SR and MT as the percentage contribution of 33% and 47%, respectively. T–ON is identified as more significant than other parameters on MRR as the percentage contribution is 47%. The results show that T–ON time of 12 μs, T–OFF time of 4 μs, and GC of 14 amps yield the least value of 3.304 μm for SR, 12 min for MT, and a higher value of 0.037 mm3 min−1 for MRR.

Some Characteristics and Arguments in Favor of a Science of Machine Behavior Analysis

Researchers and practitioners recognize four domains of behavior analysis: radical behaviorism, the experimental analysis of behavior, applied behavior analysis, and the practice of behavior analysis. Given the omnipresence of technology in every sphere of our lives, the purpose of this conceptual article is to describe and argue in favor of a fifth domain: machine behavior analysis. Machine behavior analysis is a science that examines how machines interact with and produce relevant changes in their external environment by relying on replicability, behavioral terminology, and the philosophical assumptions of behavior analysis (e.g., selectionism, determinism, parsimony) to study artificial behavior. Arguments in favor of a science of machine behavior include the omnipresence and impact of machines on human behavior, the inability of engineering alone to explain and control machine behavior, and the need to organize a verbal community of scientists around this common issue. Regardless of whether behavior analysts agree or disagree with this proposal, I argue that the field needs a debate on the topic. As such, the current article aims to encourage and contribute to this debate.