Showing papers in "Machines in 2023"

Active Disturbance Rejection Control for Piezoelectric Smart Structures: A Review

Abstract: The piezoelectric smart structures, which can be labeled as the cream of the crop of smart structures without overstatement, are strongly impacted by a large number of uncertainties and disturbances during operation. The present paper reviews active disturbance rejection control (ADRC) technologies developed for application in piezoelectric smart structures, focusing on measurement, analysis, estimation, and attenuation of uncertainties/disturbances in systems. It first explained vast categories of uncertainties/disturbances with their adverse influences. Then, after a brief introduction to the application of basic ADRC in smart structures, a thorough review of recently modified forms of ADRC is analyzed and classified in terms of their improvement objectives and structural characteristics. The universal advantages of ADRC in dealing with uncertainties and its improvement on the particularity of smart structures show its broad application prospects. These improved ADRC methods are reviewed by classifying them as modified ADRC for specific problems, modified ADRC by nonlinear functions, composite control based on ADRC, and ADRC based on other models. In addition, the application of other types of active anti-disturbances technologies in smart structures is reviewed to expand horizons. The main features of this review paper are summarized as follows: 1) it can provide profound understanding and flexible approaches for researchers and practitioners in designing ADRC in the field and 2) light up future directions and unsolved problems.

Recent Advancements in Agriculture Robots: Benefits and Challenges

Abstract: In the development of digital agriculture, agricultural robots play a unique role and confer numerous advantages in farming production. From the invention of the first industrial robots in the 1950s, robots have begun to capture the attention of both research and industry. Thanks to the recent advancements in computer science, sensing, and control approaches, agricultural robots have experienced a rapid evolution, relying on various cutting-edge technologies for different application scenarios. Indeed, significant refinements have been achieved by integrating perception, decision-making, control, and execution techniques. However, most agricultural robots continue to require intelligence solutions, limiting them to small-scale applications without quantity production because of their lack of integration with artificial intelligence. Therefore, to help researchers and engineers grasp the prevalent research status of agricultural robots, in this review we refer to more than 100 pieces of literature according to the category of agricultural robots under discussion. In this context, we bring together diverse agricultural robot research statuses and applications and discuss the benefits and challenges involved in further applications. Finally, directional indications are put forward with respect to the research trends relating to agricultural robots.

Fault Location in Distribution Network by Solving the Optimization Problem Based on Power System Status Estimation Using the PMU

Abstract: Fault location is one of the main challenges in the distribution network due to its expanse and complexity. Today, with the advent of phasor measurement units (PMU), various techniques for fault location using these devices have been proposed. In this research, distribution network fault location is defined as an optimization problem, and the network fault location is determined by solving it. This is done by combining PMU data before and after the fault with the power system status estimation (PSSE) problem. Two new objective functions are designed to identify the faulty section and fault location based on calculating the voltage difference between the two ends of the grid lines. In the proposed algorithm, the purpose of combining the PMU in the PSSE problem is to estimate the voltage and current quantities at the branch point and the total network nodes after the fault occurs. Branch point quantities are calculated using the PMU and the governing equations of the π line model for each network section, and the faulty section is identified based on a comparison of the resulting values. The advantages of the proposed algorithm include simplicity, step-by-step implementation, efficiency in conditions of different branch specifications, application for various types of faults including short-circuit and series, and its optimal accuracy compared to other methods. Finally, the proposed algorithm has been implemented on the IEEE 123-node distribution feeder and its performance has been evaluated for changes in various factors including fault resistance, type of fault, angle of occurrence of a fault, uncertainty in loading states, and PMU measurement error. The results show the appropriate accuracy of the proposed algorithm showing that it was able to determine the location of the fault with a maximum error of 1.21% at a maximum time of 23.87 s.

Flow and Performance of the Disk Cavity of a Marine Gas Turbine at Varying Nozzle Pressure and Low Rotation Speeds: A Numerical Investigation

Abstract: In marine gas turbines, variations in rotational speed occur all the time. To ensure adequate cooling effects on the turbine blades, the valves need to be adjusted to change the pressure upstream of the pre-swirl nozzle. Changing such pressure will have significant effects on the local or overall parameters, such as core swirl ratio, temperature, flow rate coefficient, moment coefficient, axial thrust coefficient, etc. In this paper, we studied the flow characteristics within the pre-swirl system of a marine gas turbine at low rotational speed by varying the pressure at the pre-swirl nozzle. The corresponding global Reynolds number ranged from Re = 2.3793 × 105 to 9.5172 × 105. The flow in the rotor-stator cavities was analyzed to find the effects of nozzle pressure on the radial velocity, core swirl ratio, and pressure. According to the simulation results, we introduced a new leakage flow term into the formulary in the references to calculate the values of K between the inner seal and the pre-swirl nozzle. The matching characteristics between the pre-swirl nozzle and the inclined receiving hole was predicted. Performance of the pre-swirl system was also analyzed, such as the pressure drop, through-flow capacity, and cooling effects. After that, the moment coefficient and the axial thrust coefficient were predicted. This study provides some reference for designers to better design the pre-swirl system.

LQR Trajectory Tracking Control of Unmanned Wheeled Tractor Based on Improved Quantum Genetic Algorithm

Abstract: In the process of trajectory tracking using the linear quadratic regulator (LQR) for driverless wheeled tractors, a weighting matrix optimization method based on an improved quantum genetic algorithm (IQGA) is proposed to solve the problem of weight selection. Firstly, the kinematic model of the wheeled tractor is established according to the Ackermann steering model, and the established model is linearized and discretized. Then, the quantum gate rotation angle adaptive strategy is optimized to adjust the rotation angle required for individual evolution to ensure a timely jumping out of the local optimum. Secondly, the populations were perturbed by the chaotic perturbation strategy and Hadamard gate variation according to their dispersion degree in order to increase their diversity and search accuracy, respectively. Thirdly, the state weighting matrix Q and the control weighting matrix R in LQR were optimized using IQGA to obtain control increments for the trajectory tracking control of the driverless wheeled tractor with circular and double-shifted orbits. Finally, the tracking simulation of circular and double-shifted orbits based on the combination of Carsim and Matlab was carried out to compare the performance of LQR optimized by five algorithms, including traditional LQR, genetic algorithm (GA), particle swarm algorithm (PSO), quantum genetic algorithm (QGA), and IQGA. The simulation results show that the proposed IQGA speeds up the algorithm’s convergence, increases the population’s diversity, improves the global search ability, preserves the excellent information of the population, and has substantial advantages over other algorithms in terms of performance. When the tractor tracked the circular trajectory at 5 m/s, the root mean square error (RMSE) of four parameters, including speed, lateral displacement, longitudinal displacement, and heading angle, was reduced by about 30%, 1%, 55%, and 3%, respectively. When the tractor tracked the double-shifted trajectory at 5 m/s, the RMSE of the four parameters, such as speed, lateral displacement error, longitudinal displacement error, and heading angle, was reduced by about 32%, 25%, 37%, and 1%, respectively.

Intelligent-PID with PD Feedforward Trajectory Tracking Control of an Autonomous Underwater Vehicle

Abstract: This paper investigates the model-free trajectory tracking control problem for an autonomous underwater vehicle (AUV) subject to the ocean currents, external disturbances, measurement noise, model parameter uncertainty, initial tracking errors, and thruster malfunction. A novel control architecture based on model-free control principles is presented to guarantee stable and precise trajectory tracking performance in the complex underwater environment for AUVs. In the proposed hybrid controller, intelligent-PID (i-PID) and PD feedforward controllers are combined to achieve better disturbance rejections and initial tracking error compensations while keeping the trajectory tracking precision. A mathematical model of an AUV is derived, and ocean current dynamics are included to obtain better fidelity when examining ocean current effects. In order to evaluate the trajectory tracking control performance of the proposed controller, computer simulations are conducted on the LIVA AUV with a compelling trajectory under various disturbances. The results are compared with the two degrees-of-freedom (DOF) i-PID, i-PID, and PID controllers to examine control performance improvements with the guaranteed trajectory tracking stability. The comparative results revealed that the i-PID with PD feedforward controller provides an effective trajectory tracking control performance and excellent disturbance rejections for the entire trajectory of the AUV.

Enhanced Reaching-Law-Based Discrete-Time Terminal Sliding Mode Current Control of a Six-Phase Induction Motor

Abstract: This paper develops an inner stator current controller based on an enhanced reaching-law-based discrete-time terminal sliding mode. The problem of tracking stator currents with high accuracy while ensuring the robustness of a six-phase induction motor in the presence of uncertain electrical parameters and unmeasurable states is tackled. The unknown dynamics are approximated by using a time delay estimation method. Then, an enhanced power-reaching law is used to make each stage of the convergence faster. A stability analysis and the system controller’s finite-time convergence are demonstrated in detail. Practical work was conducted on an asymmetrical six-phase induction machine to illustrate the developed discrete approach’s robustness and effectiveness.

Deep PCA-Based Incipient Fault Diagnosis and Diagnosability Analysis of High-Speed Railway Traction System via FNR Enhancement

Abstract: In recent years, the data-driven based FDD (Fault Detection and Diagnosis) of high-speed train electric traction systems has made rapid progress, as the safe operation of traction system is closely related to the reliability and stability of high-speed trains. The internal complexity and external complexity of the environment mean that fault diagnosis of high-speed train traction system faces great challenges. In this paper, a wavelet transform-based FNR (Fault to Noise Ratio) enhancement is realised to highlight incipient fault information and a Deep PCA (Principal Component Analysis)-based diagnosability analysis framework is proposed. First, a scheme for FNR enhancement-based fault data preprocessing with selection of the intelligent decomposition levels and optimal noise threshold is proposed. Second, fault information enhancement technology based on continuous wavelet transform is proposed from the perspective of energy. Further, a Deep-PCA based incipient fault detectability and isolatability analysis are provided via geometric descriptions. Finally, experiments on the TDCS-FIB (Traction Drive Control System–Fault Injection Benchmark) platform fully demonstrate the effectiveness of the method proposed in this paper.

Assessment of a Fully Renewable Generation System with Storage to Cost-Effectively Cover the Electricity Demand of Standalone Grids: The Case of the Canary Archipelago by 2040

Abstract: The change towards a clean electric generation system is essential to achieve the economy decarbonization goal. The Canary Islands Archipelago confronts social, environmental, and economic challenges to overcome the profound change from a fossil fuel-dependent economy to a fully sustainable renewable economy. This document analyzes a scenario with a totally renewable generation system and with total electrification of the economy for the Canary Islands by 2040. In addition, it also shows the significant reduction in this fully renewable system when an optimized interconnection among islands is considered. This scenario consists of a solar PV system of 11 GWp, a wind system of only 0.39 GWp, a pumped storage system of 16.64 GWh (2065 MW), and a lithium-ion battery system of 34.672 GWh (3500 MW), having a system LCOE of 10.1 cEUR/kWh. These results show the certainty of being able to use an autonomous, reliable, and fully renewable system to generate and store the energy needed to dispense with fossil fuels, thus, resulting in a system free of greenhouse gas emissions in the electricity market. In addition, the proposed system has low energy wastage (less than 20%) for a fully renewable, stand-alone, and off-grid system.

The Expanding Role of Artificial Intelligence in Collaborative Robots for Industrial Applications: A Systematic Review of Recent Works

Abstract: A collaborative robot, or cobot, enables users to work closely with it through direct communication without the use of traditional barricades. Cobots eliminate the gap that has historically existed between industrial robots and humans while they work within fences. Cobots can be used for a variety of tasks, from communication robots in public areas and logistic or supply chain robots that move materials inside a building, to articulated or industrial robots that assist in automating tasks which are not ergonomically sound, such as assisting individuals in carrying large parts, or assembly lines. Human faith in collaboration has increased through human–robot collaboration applications built with dependability and safety in mind, which also enhances employee performance and working circumstances. Artificial intelligence and cobots are becoming more accessible due to advanced technology and new processor generations. Cobots are now being changed from science fiction to science through machine learning. They can quickly respond to change, decrease expenses, and enhance user experience. In order to identify the existing and potential expanding role of artificial intelligence in cobots for industrial applications, this paper provides a systematic literature review of the latest research publications between 2018 and 2022. It concludes by discussing various difficulties in current industrial collaborative robots and provides direction for future research.

Human–Machine Relationship—Perspective and Future Roadmap for Industry 5.0 Solutions

Abstract: The human–machine relationship was dictated by human needs and what technology was available at the time. Changes within this relationship are illustrated by successive industrial revolutions as well as changes in manufacturing paradigms. The change in the relationship occurred in line with advances in technology. Machines in each successive century have gained new functions, capabilities, and even abilities that are only appropriate for humans—vision, inference, or classification. Therefore, the human–machine relationship is evolving, but the question is what the perspective of these changes is and what developmental path accompanies them. This question represents a research gap that the following article aims to fill. The article aims to identify the status of change and to indicate the direction of change in the human–machine relationship. Within the framework of the article, a literature review has been carried out on the issue of the human–machine relationship from the perspective of Industry 5.0. The fifth industrial revolution is restoring the importance of the human aspect in production, and this is in addition to the developments in the field of technology developed within Industry 4.0. Therefore, a broad spectrum of publications has been analyzed within the framework of this paper, considering both specialist articles and review articles presenting the overall issue under consideration. To demonstrate the relationships between the issues that formed the basis for the formulation of the development path.

Internal Flow Characteristics of High-Specific-Speed Centrifugal Pumps with Different Number of Impeller Blades under Large Flow Conditions

Abstract: As compared with a conventional centrifugal pump, a high-specific-speed centrifugal pump mostly operates under large flow conditions. In this paper, a typical high-specific-speed centrifugal pump is examined, and the effect of the blade number on the internal flow condition is investigated numerically. The numerical predictions have been verified through measurement. It was found that the predictions and the measurements are in good agreement of discrepancy. Serious cavitation could be observed within the pump when the flow rate reached 1300 m3/h. Meanwhile, the effect of the blade number on the cavitation intensity was extremely obvious. The cavitation area at the inlet edge of the blades significantly reduced when the blade number increased from three to six. In addition, the turbulent kinetic energy within the pump was more uniformly distributed. This demonstrates that the blade number can be reasonably chosen to improve the internal flow pattern within the pump, which could provide a theoretical basis for the practical application of high-specific-speed centrifugal pumps.

Process Simulation and Optimization of Arc Welding Robot Workstation Based on Digital Twin

Abstract: For the welding cell in the manufacturing process of large excavation motor arm workpieces, a system framework, based on a digital twin welding robot cell, is proposed and constructed in order to optimize the robotic collaboration process of the welding workstation with digital twin technology. For the automated welding cell, combined with the actual robotic welding process, the physical entity was digitally modeled in 3D, and the twin welding robot operating posture process beats and other data were updated in real time, through real-time interactive data drive, to achieve real-time synchronization and faithful mapping of the virtual twin as well as 3D visualization and monitoring of the system. For the robot welding process in the arc welding operation process, a mathematical model of the kinematics of the welding robot was established, and an optimization method for the placement planning of the initial welding position of the robot base was proposed, with the goal of smooth operation of the robot arm joints, which assist in the process simulation verification of the welding process through the virtual twin scenario. The implementation and validation process of welding process optimization, based on this digital twin framework, is introduced with a moving arm robot welding example.

Effects of Tool Edge Geometry and Cutting Conditions on the Performance Indicators in Dry Turning AISI 1045 Steel

Abstract: This article presents an experimental investigation and statistical analysis of the effects of cutting conditions on the machining performance of AISI 1045 steel using a wiper-shaped insert. Experimental findings are used to compare the machining performance obtained using wiper inserts with those obtained using conventional round-nose inserts as recently reported in the literature. In addition, the effects of process conditions, namely cutting speed, feed rate, and depth of cut, are analyzed in order to obtain optimum conditions for both types of inserts. The goal is to achieve the optimal machining outcomes: minimum surface roughness, resultant cutting force, and cutting temperature, but maximum material removal rate. A full factorial design was followed to conduct the experimental trials, while ANOVA was utilized to estimate the effect of each factor on the process responses. A desirability function optimization tool was used to optimize the studied responses. The results reveal that the optimum material removal rate for wiper-shaped inserts is 67% more than that of conventional inserts, while maintaining a 0.7 µm surface roughness value. The superior results obtained with wiper-shaped inserts allow wiper tools to use higher feed rates, resulting in larger material removal rates while obtaining the same surface quality.

Effects of the Magnetic Model of Interior Permanent Magnet Machine on MTPA, Flux Weakening and MTPV Evaluation

Abstract: Interior permanent-magnet synchronous machines are widely spreading in automotive and vehicle traction applications, because of their high efficiency over a wide speed range. This capability can be achieved by appropriated control strategies: Maximum Torque per Ampere (MTPA), Flux Weakening (FW) and Maximum Torque per Volt (MTPV). However, these control trajectories are often based on an simplified magnetic model of the electrical machine. In order to improve the evaluation of machine output capabilities, nonlinear magnetic behavior must be modeled. This is not only related to the final application with a given drive and control structure, but also during the design process of the electric machine. In the design process, the output torque Vs. speed characteristic must be calculated following MTPA, MTPV and FW in the most accurate way to avoid significant error. This paper proposes a set of algorithms to compute MTPA, FW and MTPV curves for interior permanent-magnet synchronous machines taking into account the machines’ nonlinearities caused by iron saturation and compares differed approaches to highlight the torque–speed capabilities for the same machine following different methods. The algorithms are based on the maps of the equivalent inductances of a reference interior permanent-magnet synchronous machine and inductances maps were obtained via 2-D Finite Element Analysis over the machine’s operating points in id−iq reference plane. The effects of different 2-D finite element methods are also computed by both standard nonlinear magnetostatic simulations and Frozen Permeability simulations. Results show that the nonlinear model computed via frozen permeability is more accurate than the conventional linear and nonlinear models computed via standard magnetostatic simulations; for this reason, during the electrical machine design, it is important to check the expected performance employing a complete inductance map and frozen permeability.

Balanced K-Star: An Explainable Machine Learning Method for Internet-of-Things-Enabled Predictive Maintenance in Manufacturing

Abstract: Predictive maintenance (PdM) combines the Internet of Things (IoT) technologies with machine learning (ML) to predict probable failures, which leads to the necessity of maintenance for manufacturing equipment, providing the opportunity to solve the related problems and thus make adaptive decisions in a timely manner. However, a standard ML algorithm cannot be directly applied to a PdM dataset, which is highly imbalanced since, in most cases, signals correspond to normal rather than critical conditions. To deal with data imbalance, in this paper, a novel explainable ML method entitled “Balanced K-Star” based on the K-Star classification algorithm is proposed for PdM in an IoT-based manufacturing environment. Experiments conducted on a PdM dataset showed that the proposed Balanced K-Star method outperformed the standard K-Star method in terms of classification accuracy. The results also showed that the proposed method (98.75%) achieved higher accuracy than the state-of-the-art methods (91.74%) on the same data.

Research and Analysis of the Characteristics of the Brushless Doubly-Fed Machine with High-Performance Decoupling Control

Abstract: The paper presents the state-space (SS) model of the brushless double-fed machine (BDFM) by taking the negative conjugate (NC) transformation of the power machine’s correlation variable when the current source of the control machine is supplied in the m-t reference frame. Based on this, the testing method of machine parameters is given, and the SS model described by five parameters is obtained. The derivation process and realization method of the slip-frequency vector feedback linearization control (SFV-FLC) strategy are given. Then, researching the SS equation in the m-t reference frame by the control machine rotor field-oriented, the relationship between the maximum and minimum output torque and the control machine rotor flux amplitude and slip velocity, the machine parameters are given considering power supply constraints. Finally, the validity of the theoretical analysis is verified by simulation and experiment, and the feasibility of the decoupling control method are also demonstrated.

A Hybrid Spiking Neural Network Reinforcement Learning Agent for Energy-Efficient Object Manipulation

Abstract: Due to the wide spread of robotics technologies in everyday activities, from industrial automation to domestic assisted living applications, cutting-edge techniques such as deep reinforcement learning are intensively investigated with the aim to advance the technological robotics front. The mandatory limitation of power consumption remains an open challenge in contemporary robotics, especially in real-case applications. Spiking neural networks (SNN) constitute an ideal compromise as a strong computational tool with low-power capacities. This paper introduces a spiking neural network actor for a baseline robotic manipulation task using a dual-finger gripper. To achieve that, we used a hybrid deep deterministic policy gradient (DDPG) algorithm designed with a spiking actor and a deep critic network to train the robotic agent. Thus, the agent learns to obtain the optimal policies for the three main tasks of the robotic manipulation approach: target-object reach, grasp, and transfer. The proposed method has one of the main advantages that an SNN possesses, namely, its neuromorphic hardware implementation capacity that results in energy-efficient implementations. The latter accomplishment is highly demonstrated in the evaluation results of the SNN actor since the deep critic network was exploited only during training. Aiming to further display the capabilities of the introduced approach, we compare our model with the well-established DDPG algorithm.

Influence of Preparation Characteristics on Stability, Properties, and Performance of Mono- and Hybrid Nanofluids: Current and Future Perspective

Abstract: Nanofluids (NFs) synthesized via the suspension of diverse nanoparticles into conventional thermal fluids are known to exhibit better thermal, optical, tribological, and convective properties, photothermal conversion, and heat transfer performance in comparison with traditional thermal fluids. Stability is pivotal to NF preparation, properties, performance, and application. NF preparation is not as easy as it appears, but complex in that obtaining a stable NF comes with the harnessing of different preparation parameters. These parameters include stirring duration and speed, volume, density, base fluid type, weight/volume concentration, density, nano-size, type of mono or hybrid nanoparticles used, type and quantity of surfactant used, and sonication time, temperature, mode, frequency, and amplitude. The effect of these preparation parameters on the stability of mono and hybrid NFs consequently affects the thermal, optical, rheological, and convective properties, and photothermal conversion and heat transfer performances of NFs in various applications. A comprehensive overview of the influence of these preparation characteristics on the thermal, optical, rheological, and properties, photothermal conversion, and heat transfer performance is presented in this paper. This is imperative due to the extensive study on mono and hybrid NFs and their acceptance as advanced thermal fluids along with the critical importance of stability to their properties and performance. The various preparation, characterization, and stability methods deployed in NF studies have been compiled and discussed herein. In addition, the effect of the various preparation characteristics on the properties (thermal, optical, rheological, and convective), photothermal conversion, and heat transfer performances of mono and hybrid NFs have been reviewed. The need to achieve optimum stability of NFs by optimizing the preparation characteristics is observed to be critical to the obtained results for the properties, photothermal conversion, and heat transfer performance studies. As noticed that the preparation characteristics data are not detailed in most of the published works and thus making it mostly impossible to reproduce NF experimental studies, stability, and results; future research is expected to address this gap. In addition, the research community should be concerned about the aging and reusability of NFs (mono and hybrid) in the nearest future.

Multi-Scale Recursive Semi-Supervised Deep Learning Fault Diagnosis Method with Attention Gate

Abstract: The efficiency of deep learning-based fault diagnosis methods for bearings is affected by the sample size of the labeled data, which might be insufficient in the engineering field. Self-training is a commonly used semi-supervised method, which is usually limited by the accuracy of features for unlabeled data screening. It is significant to design an efficient training mechanism to extract accurate features and a novel feature fusion mechanism to ensure that the fused feature is capable of screening. A novel training mechanism of multi-scale recursion (MRAE) is designed for Autoencoder in this article, which can be used for accurate feature extraction with a small amount of labeled data. An attention gate-based fusion mechanism was constructed to make full use of all useful features in the sense that it can incorporate distinguishing features on different scales. Utilizing large numbers of unlabeled data, the proposed multi-scale recursive semi-supervised deep learning fault diagnosis method with attention gate (MRAE-AG) can efficiently improve the fault diagnosis performance of DNNs trained by a small number of labeled data. A benchmark dataset from the Case Western Reserve University bearing data center was used to validate this novel method which shows that 7.76% accuracy improvement can be achieved in the case when only 10 labeled samples was available for supervised training of the DNN-based fault diagnosis model.

Frequency Response Analysis for Three-Phase Star and Delta Induction Motors: Pattern Recognition and Fault Analysis Using Statistical Indicators

Abstract: This paper presents a new investigation to detect various faults within the three-phase star and delta induction motors (IMs) using a frequency response analysis (FRA). In this regard, experimental measurements using FRA are performed on three IMs of ratings 1 HP, 3 HP and 5.5 HP in normal conditions, short-circuit fault (SC) and open-circuit fault (OC) conditions. The SC and OC faults are applied artificially between the turns (Turn-to-Turn), between the coils (Coil-to-Coil) and between the phases (Phase-to-Phase). The obtained measurements show that the star and delta IMs result in dissimilar FRA signatures for the normal and faulty windings. Various statistical indicators are used to quantify the deviations between the normal and faulty FRA signatures. The calculation is performed in three frequency ranges: low, middle and high ones, as the winding parameters including resistive, inductive and capacitive components dominate the frequency characteristics at different frequency ranges. Consequently, it is proposed that the boundaries for the used indicators facilitate fault identification and quantification.

Optimization of Selective Laser Sintering/Melting Operations by Using a Virus-Evolutionary Genetic Algorithm

Abstract: This work presents the multi-objective optimization results of three experimental cases involving the laser sintering/melting operation and obtained by a virus evolutionary genetic algorithm. From these three experimental cases, the first one is formulated as a single-objective optimization problem aimed at maximizing the density of Ti6Al4V specimens, with layer thickness, linear energy density, hatching space and scanning strategy as the independent process parameters. The second one refers to the formulation of a two-objective optimization problem aimed at maximizing both the hardness and tensile strength of Ti6Al4V samples, with laser power, scanning speed, hatch spacing, scan pattern angle and heat treatment temperature as the independent process parameters. Finally, the third case deals with the formulation of a three-objective optimization problem aimed at minimizing mean surface roughness, while maximizing the density and hardness of laser-melted L316 stainless steel powder. The results obtained by the proposed algorithm are statistically compared to those obtained by the Greywolf (GWO), Multi-verse (MVO), Antlion (ALO), and dragonfly (DA) algorithms. Algorithm-specific parameters for all the algorithms including those of the virus-evolutionary genetic algorithm were examined by performing systematic response surface experiments to find the beneficial settings and perform comparisons under equal terms. The results have shown that the virus-evolutionary genetic algorithm is superior to the heuristics that were tested, at least on the basis of evaluating regression models as fitness functions.

Fault Detection, Diagnostics, and Treatment in Automated Manufacturing Systems Using Internet of Things and Colored Petri Nets

Abstract: Internet of things (IoT) applications, which include environmental sensors and control of automated manufacturing systems (AMS), are growing at a rapid rate. In terms of hardware and software designs, communication protocols, and/or manufacturers, IoT devices can be extremely heterogeneous. Therefore, when these devices are interconnected to create a complicated system, it can be very difficult to detect and fix any failures. This paper proposes a new reliability design methodology using “colored resource-oriented Petri nets” (CROPNs) and IoT to identify significant reliability metrics in AMS, which can assist in accurate diagnosis, prognosis, and resulting automated repair to enhance the adaptability of IoT devices within complicated cyber-physical systems (CPSs). First, a CROPN is constructed to state “sufficient and necessary conditions” for the liveness of the CROPN under resource failures and deadlocks. Then, a “fault diagnosis and treatment” technique is presented, which combines the resulting network with IoT to guarantee the reliability of the CROPN. In addition, a GPenSIM tool is used to verify, validate, and analyze the reliability of the IoT-based CROPN. Comparing the results to those found in the literature shows that they are structurally simpler and more effective in solving the deadlock issue and modeling AMS reliability.

Switched Reluctance Motor Design for a Light Sport Aircraft Application

Abstract: This paper presents the design of a switched reluctance motor (SRM) for a direct-drive propulsion application for a light sport aircraft. The SRM is designed to replace a 70 kW permanent magnet synchronous motor used in aerospace application with similar dimensional constraints. As a means of achieving high torque density and efficiency, a multi-objective design framework is used to optimize the geometry parameters of the motor. In order to further reduce the weight, rotor cutouts are implemented. The conduction angles for the asymmetric bridge converter are selected by employing a multi-objective genetic algorithm to map the torque speed characteristics of the motor. The core losses are evaluated with the modified Bertotti method to calculate the motor efficiency and determine the steady-state and transient thermal performance at the base speed. The designed coil winding is wound on a spindle winder, and the coil fitting, fill factor, and the coil retention are validated experimentally.

Co-Simulation Modeling and Multi-Objective Optimization of Dynamic Characteristics of Flow Balancing Valve

Abstract: The poor dynamic characteristics of the flow balance valve used in a ship’s HVAC system are the main reasons for the hydraulic imbalance and high energy consumption of the system. A new adjustable dynamic flow balance valve structure is designed, which is composed of a self-operated pressure regulator and an electric V-shaped ball valve in series. When the V-shaped ball valve is fully opened at the 20 t/h flow level, the dynamic characteristics of the flow balance valve cannot meet the requirements. A new co-simulation method that combines MATLAB/Simulink and the UDF dynamic grid is proposed to study the dynamic characteristics of a flow balance valve with a 20 t/h flow rate under different pressure drop step signal interference. When the calculation of each micro-element time converges, the valve core motion parameters, the pressure boundary conditions, the valve core axial medium force, and the valve outlet flow are interactively transmitted in the two simulation environments. The discrepancy between the co-simulation and test results is less than 5%, which verifies the accuracy of the co-simulation model. Aiming at the most severe dynamic characteristic working condition where the pressure drop is stepped from 30 to 300 kPa, the influence of different structural parameters on the dynamic characteristics of the balance valve is analyzed. A new surrogate model combining RSM and RBF with the co-simulation method improves the optimization efficiency and fitting accuracy. To improve the convergence of the traditional NSGA-II algorithm, key structural parameters are optimized by combining the NSGA-II algorithm and SDR. The test results show that the dynamic characteristics of the optimized valve are improved, the discrepancy between the stabilized flow rate and 20 t/h does not exceed 4.5%, and the flow is relatively constant. Therefore, the proposed co-simulation and optimization method can be applied to the dynamic characteristic prediction of self-operated valves, such as dynamic flow balance valves, to provide guidance for developing high-precision self-operated valves.

A Deep-Learning-Based Approach for Aircraft Engine Defect Detection

Abstract: Borescope inspection is a labour-intensive process used to find defects in aircraft engines that contain areas not visible during a general visual inspection. The outcome of the process largely depends on the judgment of the maintenance professionals who perform it. This research develops a novel deep learning framework for automated borescope inspection. In the framework, a customised U-Net architecture is developed to detect the defects on high-pressure compressor blades. Since motion blur is introduced in some images while the blades are rotated during the inspection, a hybrid motion deblurring method for image sharpening and denoising is applied to remove the effect based on classic computer vision techniques in combination with a customised GAN model. The framework also addresses the data imbalance, small size of the defects and data availability issues in part by testing different loss functions and generating synthetic images using a customised generative adversarial net (GAN) model, respectively. The results obtained from the implementation of the deep learning framework achieve precisions and recalls of over 90%. The hybrid model for motion deblurring results in a 10× improvement in image quality. However, the framework only achieves modest success with particular loss functions for very small sizes of defects. The future study will focus on very small defects detection and extend the deep learning framework to general borescope inspection.

A State-of-the-Art Review on Chatter Stability in Machining Thin−Walled Parts

Abstract: Thin−walled parts are widely used in many important fields because of performance and structural lightweight requirements. They are critical parts because they usually carry the core functions of high−end equipment. However, their high−performance machining has been facing severe challenges, among which the dynamics problem is one of the most important obstacles. The machining system is easily subjected to chatter due to the weak rigidity of the thin−walled structure and slender cutting tool, which significantly deteriorates the surface quality and reduces the machining efficiency. Extensive studies aiming at eliminating machining chatter have been carried out in the recent decades. This paper systematically reviews previous studies on the identification of system dynamic characteristics, modeling and prediction of chatter stability, and chatter elimination/suppression methods and devices. Finally, existing problems are summarized, and future research is concluded.

Flow Capacity Optimization of a Squirrel Cage Fan with a New Rounded Rectangle Volute under Size Limitation

Abstract: Squirrel cage fans (SCFs) are widely used in a variety of household appliances. Due to the restriction on installation size, the design of high-efficiency SCFs with high flow capacities is an important topic. In this study, we proposed a novel rounded rectangle volute profile (RRVP) for the design of compact high-flow SCFs. At first, we used computational fluid dynamics (CFD) to simulate the aerodynamic performances of three SCFs having the same impeller but different volutes, which were the common logarithmic-spiral volute profile, the cutting volute profile, and the RRVP volute at the maximum flow rate working condition. The CFD simulations indicate that the fan with RRVP volute has the highest flow rate at the maximum flow rate working condition. Then, we proposed a parameterization method for the RRVP with 16 control variables. The multiobjective evolutionary algorithm based on decomposition (MOEA/D) and Kriging model was used to optimize the aerodynamic shape of the compact SCF with an RRVP volute. Twenty-three control variables were used in the multiobjective optimization process, including the optimization of the blade angles and the impeller position. Optimization results show that the maximum volumetric flow rate of the optimal SCF with an RRVP volute increases from 147.1 cubic feet per minute (CFM) to 191.1 CFM, and the fan efficiency also increases from 32.21% to 33.5%, compared with the original SCF with the common logarithmic-spiral volute. Two main factors were found to increase the flow capacity and efficiency of the optimal SCF under strict size constrains. First, the RRVP became smooth and large, which reduced the flow loss and increased the flow cross-section; second, the eccentrically mounted impeller of the optimal fan enlarged the flow section near the outlet of the volute.

A Hybrid Fuzzy Multi-Criteria Decision-Making Model for Evaluating the Influence of Industry 4.0 Technologies on Manufacturing Strategies

Abstract: Manufacturing is transitioning from traditional and mass manufacturing to mass personalization, fast, and intelligent production. Through full automation in various fields and data sharing, Industry 4.0 (I4.0) contributes to the digitalization of manufacturing by enhancing industrial flexibility and product customization. I4.0 is being utilized as a strategy for advanced manufacturing to counter global competitiveness. A company’s manufacturing strategy outputs (MSOs) are critical to its ability to move forward and remain competitive. Despite their importance, I4.0 technologies have received less attention in the literature, and it is unclear how they influence MSOs. Thus, this study aims to build a powerful hybrid MCDM method for ranking the influence of I4.0 technologies on MSOs by adopting a combination of AHP and fuzzy TOPSIS. The application of fuzzy set theory has addressed the ambiguity in comparing various I4.0 technologies. The AHP was used to calculate the weights of criteria and sub-criteria, and the fuzzy-TOPSIS method was utilized to rank the I4.0 technologies. The results revealed that the cost criterion is the most critical factor when implementing I4.0 technologies. In contrast, additive manufacturing (AM) is the most suitable I4.0 technology for countering global competition.

Research Regarding the Development of the Combustion Chamber of Internal Combustion Engines with Opposite Pistons

Abstract: The reduction in environment pollutant emissions is one of the main challenges regarding ground transportation. Internal combustion engines, used especially in hybrid propulsion systems, may be a solution in the transition to fully electric cars. Therefore, more efficient engines in terms of fuel consumption, emission generation and power density must be developed. This paper presents research regarding the architecture of the combustion chamber of an internal combustion engine with opposed pistons. The aim of this research was to find a combustion chamber architecture that would enable the engine to perform close to the program target of: NOx < 3.5 g/kWh, smoke (FSN) < 1, specific fuel consumption (bsfc) < 198 g/kWh. Three variants of the combustion chamber’s architecture have been studied. After the experimental research, the conclusion was that none of them fully reached the target; however, significant improvements have been achieved compared with the starting point. As a result, further research needs to be carried out in order to reach and even exceed the target.