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Machines
Volume 10
Issue 11
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Machines, Volume 10, Issue 11 (November 2022) – 140 articles

Cover Story (view full-size image): The purpose of this research is to study the influence of the processing conditions that affect the final behaviour of specimens made from a 17-4PH composition powder, which is manufactured using the additive technique known as plasma metal deposition. Two walls manufactured from the prealloyed powder were built under two distinct atmospheric conditions (air and argon), with previously optimised manufacturing parameters. The additional effect of two applied thermal treatments was studied by means of property and microstructural analyses of the extracted specimens from each consolidated wall. The two thermal treatments consisted of a heating rate of 10 °C/min to 482 °C and 620 °C, with the temperatures for 1 and 4 h, respectively; the cooling rate was 5 °C/min for both treatments. View this paper
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17 pages, 3700 KiB  
Article
A Deep-Learning-Based Multi-Modal Sensor Fusion Approach for Detection of Equipment Faults
by Omer Kullu and Eyup Cinar
Machines 2022, 10(11), 1105; https://doi.org/10.3390/machines10111105 - 21 Nov 2022
Cited by 8 | Viewed by 3948
Abstract
Condition monitoring is a part of the predictive maintenance approach applied to detect and prevent unexpected equipment failures by monitoring machine conditions. Early detection of equipment failures in industrial systems can greatly reduce scrap and financial losses. Developed sensor data acquisition technologies allow [...] Read more.
Condition monitoring is a part of the predictive maintenance approach applied to detect and prevent unexpected equipment failures by monitoring machine conditions. Early detection of equipment failures in industrial systems can greatly reduce scrap and financial losses. Developed sensor data acquisition technologies allow for digitally generating and storing many types of sensor data. Data-driven computational models allow the extraction of information about the machine’s state from acquired sensor data. The outstanding generalization capabilities of deep learning models have enabled them to play a significant role as a data-driven computational fault model in equipment condition monitoring. A challenge of fault detection applications is that single-sensor data can be insufficient in performance to detect equipment anomalies. Furthermore, data in different domains can reveal more prominent features depending on the fault type, but may not always be obvious. To address this issue, this paper proposes a multi-modal sensor fusion-based deep learning model to detect equipment faults by fusing information not only from different sensors but also from different signal domains. The effectiveness of the model’s fault detection capability is shown by utilizing the most commonly encountered equipment types in the industry, such as electric motors. Two different sensor types’ raw time domain and frequency domain data are utilized. The raw data from the vibration and current sensors are transformed into time-frequency images using short-time Fourier transform (STFT). Then, time-frequency images and raw time series data were supplied to the designed deep learning model to detect failures. The results showed that the fusion of multi-modal sensor data using the proposed model can be advantageous in equipment fault detection. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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19 pages, 8062 KiB  
Article
Research on Phase Current Reconstruction for DPWM2 of Induction Motor Drive System Based on DC-Link Current Sampling
by Pengyun Song, Huazhang Wang, Junyi Zhang, Yanghui Liu and Tao Deng
Machines 2022, 10(11), 1104; https://doi.org/10.3390/machines10111104 - 21 Nov 2022
Cited by 2 | Viewed by 1270
Abstract
In order to reduce the switching loss and cost, as well as improve the reliability of the induction motor (IM) drive system, the technology involving the three-phase current reconstruction of 60° discontinuous pulse width modulation (DPWM2) is studied in this paper. According to [...] Read more.
In order to reduce the switching loss and cost, as well as improve the reliability of the induction motor (IM) drive system, the technology involving the three-phase current reconstruction of 60° discontinuous pulse width modulation (DPWM2) is studied in this paper. According to the analysis of the switching state for DPWM2 in different sectors, the three-phase current can be constructed by sampling the voltage of the DC-link resistor. When the target voltage vector is located near the sector boundary or in the low-modulation area, the duration of the active vector in the sampling period is less than the voltage sampling time, which leads to measurement errors of the DC-link current. Therefore, on the basis of the switching state in different unmeasured areas, a time compensation method combining phase shifting and frequency reduction is proposed, and the expressions for comparing values are derived. Lastly, a simulation model and an experimental platform are established to validate the accuracy of the proposed method. Full article
(This article belongs to the Section Electromechanical Energy Conversion Systems)
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22 pages, 4490 KiB  
Review
Fiber Optic Fiber Bragg Grating Sensing for Monitoring and Testing of Electric Machinery: Current State of the Art and Outlook
by Asep Andi Suryandi, Nur Sarma, Anees Mohammed, Vidyadhar Peesapati and Siniša Djurović
Machines 2022, 10(11), 1103; https://doi.org/10.3390/machines10111103 - 21 Nov 2022
Cited by 10 | Viewed by 2473
Abstract
This paper presents a review of the recent trends and the current state of the art in the application of fiber optic fiber Bragg gratings (FBG) sensing technology to condition the monitoring (CM) and testing of practical electric machinery and the associated power [...] Read more.
This paper presents a review of the recent trends and the current state of the art in the application of fiber optic fiber Bragg gratings (FBG) sensing technology to condition the monitoring (CM) and testing of practical electric machinery and the associated power equipment. FBG technology has received considerable interest in this field in recent years, with research demonstrating that the flexible, multi-physical, and electromagnetic interference (EMI) immune in situ sensing of a multitude of physical measurands of CM interest is possible and cannot be obtained through conventional sensing means. The unique FBG sensing ability has the potential to unlock many of the electric machine CM and design validation restrictions imposed by the limitations of conventional sensing techniques but needs further research to attain wider adoption. This paper first presents the fundamental principles of FBG sensing. This is followed by a description of recent FBG sensing techniques proposed for electric machinery and associated power equipment and a discussion of their individual benefits and limitations. Finally, an outlook for the further application of this technique is presented. The underlying intention is for the review to provide an up-to-date overview of the state of the art in this area and inform future developments in FBG sensing in electric machinery. Full article
(This article belongs to the Special Issue Machine Health Diagnosis & Prognosis by Advanced Sensing and Data Driven Techniques)
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19 pages, 7825 KiB  
Article
Development of a Novel Dynamic Modeling Approach for a Three-Axis Machine Tool in Mechatronic Integration
by De-Shin Liu, Jen-Chang Lu, Meng-Shiun Tsai, Chih-Ta Wu and Zhen-Wei Zhuang
Machines 2022, 10(11), 1102; https://doi.org/10.3390/machines10111102 - 21 Nov 2022
Viewed by 1569
Abstract
This paper proposes a novel, fast, and automatic modeling method to build a virtual model with minimum degrees of freedom (DOFs) without the need for FE models or human judgment. The proposed program uses the iterative closest point (ICP) algorithm to analyze the [...] Read more.
This paper proposes a novel, fast, and automatic modeling method to build a virtual model with minimum degrees of freedom (DOFs) without the need for FE models or human judgment. The proposed program uses the iterative closest point (ICP) algorithm to analyze the mode shape vector of structural dynamic characteristics to define the position and DOFs of the joints between structural components. After the multi-body dynamics model was developed in software, it was converted into an SSM to connect the servo loop model. Then, the mechatronic integration analysis was performed to verify the dynamic characteristics of the tool center point (TCP) and the workbench in the experiment and simulation. The model created by the proposed identification process has a small DOF and can accurately simulate the dynamic characteristics of a machine. This model can be used for dynamic testing and control strategy development in mechatronic integration. Full article
(This article belongs to the Section Advanced Manufacturing)
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32 pages, 2252 KiB  
Review
A Survey on Data-Driven Scenario Generation for Automated Vehicle Testing
by Jinkang Cai, Weiwen Deng, Haoran Guang, Ying Wang, Jiangkun Li and Juan Ding
Machines 2022, 10(11), 1101; https://doi.org/10.3390/machines10111101 - 21 Nov 2022
Cited by 15 | Viewed by 6137
Abstract
Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing [...] Read more.
Automated driving is a promising tool for reducing traffic accidents. While some companies claim that many cutting-edge automated driving functions have been developed, how to evaluate the safety of automated vehicles remains an open question, which has become a crucial bottleneck. Scenario-based testing has been introduced to test automated vehicles, and much progress has been achieved. While data-driven and knowledge-based approaches are hot research topics, this survey is mainly about Data-Driven Scenario Generation (DDSG) for automated vehicle testing. Rather than describe the contributions of every study respectively, in this survey, methodologies from various studies are anatomized as solutions for several significant problems and compared with each other. This way, scholars and engineers can quickly find state-of-the-art approaches to the issues they might encounter. Furthermore, several critical challenges that might hinder DDSG are described, and responding solutions are presented at the end of this survey. Full article
(This article belongs to the Section Vehicle Engineering)
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38 pages, 11315 KiB  
Article
Solving the Flexible Job Shop Scheduling Problem Using a Discrete Improved Grey Wolf Optimization Algorithm
by Xiaohong Kong, Yunhang Yao, Wenqiang Yang, Zhile Yang and Jinzhe Su
Machines 2022, 10(11), 1100; https://doi.org/10.3390/machines10111100 - 21 Nov 2022
Cited by 9 | Viewed by 2104
Abstract
The flexible job shop scheduling problem (FJSP) is of great importance for realistic manufacturing, and the problem has been proven to be NP-hard (non-deterministic polynomial time) because of its high computational complexity. To optimize makespan and critical machine load of FJSP, a discrete [...] Read more.
The flexible job shop scheduling problem (FJSP) is of great importance for realistic manufacturing, and the problem has been proven to be NP-hard (non-deterministic polynomial time) because of its high computational complexity. To optimize makespan and critical machine load of FJSP, a discrete improved grey wolf optimization (DIGWO) algorithm is proposed. Firstly, combined with the random Tent chaotic mapping strategy and heuristic rules, a hybrid initialization strategy is presented to improve the quality of the original population. Secondly, a discrete grey wolf update operator (DGUO) is designed by discretizing the hunting process of grey wolf optimization so that the algorithm can solve FJSP effectively. Finally, an adaptive convergence factor is introduced to improve the global search ability of the algorithm. Thirty-five international benchmark problems as well as twelve large-scale FJSPs are used to test the performance of the proposed DIGWO. Compared with the optimization algorithms proposed in recent literature, DIGWO shows better solution accuracy and convergence performance in FJSPs at different scales. Full article
(This article belongs to the Section Advanced Manufacturing)
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25 pages, 7321 KiB  
Article
Design and Performance Analysis of a Double-Outlet-Rod Magnetorheological Damper for Impact Load
by Chenglong Wang, Jiwei Zhang, Guoming Liu, Huan Shang and Xueqian Wei
Machines 2022, 10(11), 1099; https://doi.org/10.3390/machines10111099 - 20 Nov 2022
Cited by 2 | Viewed by 1431
Abstract
In order to improve the performance of magnetorheological dampers under impact load, a double-rod magnetorheological damper is designed in this paper, and its multi-physical field coupling model is established. The performance of a double-rod magnetorheological damper under impact conditions is characterized from the [...] Read more.
In order to improve the performance of magnetorheological dampers under impact load, a double-rod magnetorheological damper is designed in this paper, and its multi-physical field coupling model is established. The performance of a double-rod magnetorheological damper under impact conditions is characterized from the aspects of viscosity, velocity, peak pressure, impact energy consumption and viscous damping force ratio. The research contents include: a comparison of dynamic characteristics such as the viscosity, velocity and pressure of the magnetorheological damper under impact conditions and low-speed vibration; the influence of temperature on the mechanical performance parameters of the magnetorheological damper, such as peak pressure, impact energy dissipation and viscous damping force ratio, under impact load; and the establishment of a peak sensitivity function to study the influence of three key structural parameters on the magnetic flux density and impact energy dissipation at the damper damping channel. On the basis of the above theoretical research, an impact test of the processed double-rod magnetorheological damper prototype under different excitation currents is carried out. The results show that the viscosity distribution of the damping channel activation region (i.e., the region where the magnetorheological effect occurs after energization) of the magnetorheological damper under impact is disordered, the region of the structural flow in the semi-solid state is small and the influence of the Coulomb damping force is greatly weakened. When the current is 0.5 A, the viscous damping force accounts for 91.2%, and the viscous damping force plays a major role in buffering energy absorption. With an increase in working temperature, the effect of the Coulomb damping force decreases, and the peak pressure and impact energy consumption of the MR damper decrease greatly. With increasing excitation current, this reduction is further increased. The influence of gap height, piston diameter and effective length on magnetic flux density and impact energy dissipation is determined via the peak sensitivity function. When the change in the structural parameters ∆α is 30%, the change in the piston diameter has the greatest influence on the peak sensitivity of the magnetic flux density, and the peak sensitivity index of the magnetic flux density reaches 25%. The change in clearance height has the greatest influence on the impact energy consumption, and the peak sensitivity index of the impact energy consumption reaches 115%. This shows that the magnetic flux density is most affected by the piston diameter, and the impact energy consumption is most affected by the clearance height. The test results show that the test pressure peak-change curve is consistent with the simulation pressure peak-change curve, and the pressure peak error between the two is within 10%. Full article
(This article belongs to the Section Machine Design and Theory)
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19 pages, 8661 KiB  
Article
Research on Dynamic Characteristics of Flap Actuation System Considering Joint Clearance and Flexibility
by Qi Wan, Geng Liu, Qi Liu, Ruiting Tong and Shangjun Ma
Machines 2022, 10(11), 1098; https://doi.org/10.3390/machines10111098 - 20 Nov 2022
Viewed by 1157
Abstract
The flight control effects and flight quality of the aircraft are influenced by the flap actuation system directly. The main motivation of this research is to develop a rigid-flexible model of the flap actuation system with clearance and to explore the coupling effects [...] Read more.
The flight control effects and flight quality of the aircraft are influenced by the flap actuation system directly. The main motivation of this research is to develop a rigid-flexible model of the flap actuation system with clearance and to explore the coupling effects of clearance joints and flexible bodies on the system’s dynamic characteristics. A modified contact force model is used to a joint with small clearance, heavy load, and large contact area. Then, the effectiveness of an embedded modeling method of this modified model is verified. Based on this method, a rigid-flexible coupling model of the flap actuation system with multi clearance joints is established. Moreover, the influence of system parameters, such as clearance size and clearance joint position, and coupling effects of the flexibility and joint clearance on dynamic responses, are analyzed. The results show that: (1) flexible bodies act as a suspension to reduce negative effects of joint clearance on dynamic responses of a clearance-contained system; (2) one flexible body can reduce the oscillation phenomenon of joint clearances, yet the suspension effect will be gradually weaken with the increase of the clearance joint number; (3) coupling effects of the elastic deformation owing to multi flexible bodies and the collision interaction between multi clearance joints make the system tend to chaos, and the system stability is reduced. This study can contribute to predicting the collision characteristics of the aircraft transmission system and improving the transmission accuracy, response speed, and stability of the aircraft. Full article
(This article belongs to the Section Machine Design and Theory)
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10 pages, 2126 KiB  
Article
Uniaxial Load Specification for Vehicle Knuckle Part Using Maximum Stress Similarity in Triaxial Load Case
by Chan-Jung Kim
Machines 2022, 10(11), 1097; https://doi.org/10.3390/machines10111097 - 18 Nov 2022
Viewed by 1283
Abstract
Knuckle parts have a complex relationship with adjacent vehicle parts, making it difficult to determine the proper fatigue evaluation specification when considering vehicle operation. An accelerated triaxial load case for the knuckle part was derived using a combination of four event modules from [...] Read more.
Knuckle parts have a complex relationship with adjacent vehicle parts, making it difficult to determine the proper fatigue evaluation specification when considering vehicle operation. An accelerated triaxial load case for the knuckle part was derived using a combination of four event modules from the test code developed by the Korea Automotive Technology Institute. The fatigue damage analysis of the front and rear knuckle models was conducted with respect to the accelerated triaxial load case, and the maximum stress was measured at hotspots for the magnitude and orientation of the critical plane. The sensitivity analysis of the knuckle models was conducted for six directions of the unit force, and the proper uniaxial force orientations of the two knuckle models were determined for the maximum stress similarity in the triaxial load case in terms of the magnitude and orientation of the critical plane. The final uniaxial load specification was derived by adjusting the magnitude of the candidate uniaxial load and the error analysis showed reliable results through inverse safety factors with 0.02, 0.04 error, and critical plane angles with 10.8, 0.8 degrees error for front and rear knuckles, respectively, verified by comparing the maximum stress similarity between the triaxial and uniaxial load cases. Full article
(This article belongs to the Section Vehicle Engineering)
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22 pages, 9665 KiB  
Article
Influence of Position of Intake Struts on Unsteady Load and Vibration of First-Stage Rotor
by Wei Peng, Xiaodong Ren, Xuesong Li, Chunwei Gu, Hong Wu and Xiaobin Que
Machines 2022, 10(11), 1096; https://doi.org/10.3390/machines10111096 - 18 Nov 2022
Viewed by 1503
Abstract
To investigate the influence law and mechanism of the relative circumferential position of intake struts on the unsteady load and vibration of the first stage rotor, the first 1.5-stage of a heavy-duty gas turbine compressor with intake struts was numerically simulated. The analysis [...] Read more.
To investigate the influence law and mechanism of the relative circumferential position of intake struts on the unsteady load and vibration of the first stage rotor, the first 1.5-stage of a heavy-duty gas turbine compressor with intake struts was numerically simulated. The analysis of the flow field and vibration of the rotor blade reveals that the circumferential position of intake struts has little effect on aerodynamic performance but obviously changes the unsteady load and vibration level of the rotor blade. When strut and inlet guide vane wakes coincide, they are strengthened, resulting in the overall enhancement of the unsteady load and vibration on the rotor blade. The circumferential position of struts changes the combined effect of strut wakes and the first stage stator potential flow on the rotor blade, which has an obvious influence on the unsteady load and vibration in the middle chord length but has little influence on the unsteady load near the leading and trailing edges. Research results provide reference and guidance for the installation of struts. Full article
(This article belongs to the Special Issue Aerodynamic Design and Optimization for Turbomachinery)
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16 pages, 7297 KiB  
Article
Calibration Method for In-Process Identification of Tangential Specific Cutting Force in Milling
by Miroslav Janota, Petr Kolář, Jiří Falta and Tomáš Kozlok
Machines 2022, 10(11), 1095; https://doi.org/10.3390/machines10111095 - 18 Nov 2022
Cited by 1 | Viewed by 1502
Abstract
The cutting forces that occur during machining cause static and dynamic deformations of the machine–tool–workpiece system. In general, the cutting force is spatial. However, knowledge of the tangential component of the cutting force is crucial for the optimum use of the power installed [...] Read more.
The cutting forces that occur during machining cause static and dynamic deformations of the machine–tool–workpiece system. In general, the cutting force is spatial. However, knowledge of the tangential component of the cutting force is crucial for the optimum use of the power installed on the spindle. An important parameter for cutting force modeling is the tangential cutting force coefficient. This paper focuses on the approach of the in-process identification of the cutting and edge components of the tangential cutting force coefficient, using the spindle power signal read directly from the machine tool control system. Such procedures have already been described in the available literature. The key point for the successful implementation of these methods is the identification and avoidance of the passive torque signal. This paper describes the operational calibration of the spindle drive system. The calibration procedure is based on the tap test using modal hammer excitation and the spindle power signal response. The proposed procedure was successfully validated using a machine–machine comparison approach and a machine–dynamometer validation approach. The results are consistent with those in the available literature. With this method, we were able to determine the cutting component of the tangential specific cutting force with a deviation of 1% from the reference system and an edge component of 10%. The modal hammer method only enables the avoidance of dynamometers in the machine calibration procedure. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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12 pages, 1840 KiB  
Article
Active Control Method for Rotor Eccentric Vibration of High-Speed Motor Based on Least Squares Support Vector Machine
by Liheng Wang, Ming Zhuang and Kai Yuan
Machines 2022, 10(11), 1094; https://doi.org/10.3390/machines10111094 - 18 Nov 2022
Cited by 1 | Viewed by 1186
Abstract
Aiming at the problems of large active control errors and long control times in the active control method for high-speed motor rotor eccentric vibration, an active control method for high-speed motor rotor eccentric vibration based on a least squares support vector machine was [...] Read more.
Aiming at the problems of large active control errors and long control times in the active control method for high-speed motor rotor eccentric vibration, an active control method for high-speed motor rotor eccentric vibration based on a least squares support vector machine was proposed. Firstly, the overall structure of the system and its high-speed rotor were designed. Secondly, by calculating the centrifugal force of the eccentric rotor, the vibration of the relative phase of the rotor position, and the width of the air gap between the rotor and the stator, a mathematical model of the eccentric vibration of the high-speed motor rotor was established. Then, the basic principle of the least squares support vector machine was analyzed, and the control parameters of the eccentric vibration of the high-speed motor rotor were set and filtered. Finally, an active control model of high-speed motor rotor eccentric vibration was constructed, and the optimal solution of the model was obtained by regression algorithm. The experimental results show that the method is effective for the active control of high-speed motor rotor eccentric vibration, the control effect is consistent with the ideal effect, and the control time is short—the longest is only 0.13 s. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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10 pages, 1846 KiB  
Article
Digitising a Machine Tool for Smart Factories
by Anton Averyanov, Shohin Aheleroff, Jan Polzer and Xun Xu
Machines 2022, 10(11), 1093; https://doi.org/10.3390/machines10111093 - 18 Nov 2022
Cited by 3 | Viewed by 2136
Abstract
Smart factory development renders an incredible opportunity for the manufacturing industry to join the Fourth Industrial Revolution (Industry 4.0). However, an incredible number of conventional CNC machine tools are populating the world’s factories. Replacing these machines is an expensive process. This task might [...] Read more.
Smart factory development renders an incredible opportunity for the manufacturing industry to join the Fourth Industrial Revolution (Industry 4.0). However, an incredible number of conventional CNC machine tools are populating the world’s factories. Replacing these machines is an expensive process. This task might be considered unliftable by most small businesses. An inexpensive digitalisation of Machine Tool 3.0 to an Industry 4.0-compatible tool might be one way for small businesses to keep up with the progress and stay competitive. The developed framework uses recent advances in the open-source community to transform a conventional CNC machine into Machine Tool 4.0. The suggested approach opens up a way to bypass the proprietary computer numerical control and enable connectivity and efficient data communication with the machine tool. At almost no cost, the provided strategy converts an average CNC machine into a machine tool with the full spectrum of accessibility and interoperability of Machine Tool 4.0. The proposed solution can enable small- and medium-sized enterprises to step up and propel them into the Industry 4.0 era. Full article
(This article belongs to the Special Issue Advances in Smart Manufacturing and Industry 4.0)
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21 pages, 3275 KiB  
Article
A Digital Observer-Based Repetitive Learning Composite Control Method for Large Range Piezo-Driven Nanopositioning Systems
by Cunhuan Liu, Yongchun Fang, Yinan Wu and Zhi Fan
Machines 2022, 10(11), 1092; https://doi.org/10.3390/machines10111092 - 18 Nov 2022
Cited by 1 | Viewed by 1134
Abstract
In this study, a novel digital compound compensation method is proposed to compensate for the hysteresis nonlinearity and the drift disturbance of a piezoelectric nanopositioning system with a large range. The overall hysteresis behaviors can be divided into the static amplitude-dependent behavior and [...] Read more.
In this study, a novel digital compound compensation method is proposed to compensate for the hysteresis nonlinearity and the drift disturbance of a piezoelectric nanopositioning system with a large range. The overall hysteresis behaviors can be divided into the static amplitude-dependent behavior and the dynamic rate-dependent behavior, where the static hysteresis is compensated for by a novel discrete feedforward controller, while the dynamic hysteresis and the drift disturbance are compensated for by a novel discrete composite feedback controller composed of a drift observer-based state feedback controller and a repetitive learning controller. Compared with traditional control strategies, the proposed compound control strategy, including feedforward and feedback components, can eliminate system errors more effectively when tracking large range signals with obvious hysteresis. Moreover, the proposed online drift observer is superior over a traditional offline drift compensator both in response speed and compensation accuracy. Sufficient simulation tests and convincing tracking experiments, with large range periodic signals up to 90 μm, are carried out. And comparisons with the two classical control algorithms are performed. The tracking results show that the mean absolute error of the proposed control method is minor compared with the other two algorithms, which validates that the proposed strategy can efficiently compensate for the hysteresis nonlinearity and the drift disturbance. Full article
(This article belongs to the Special Issue Intelligent Mechatronics: Perception, Optimization, and Control)
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14 pages, 3937 KiB  
Article
A Fast Globally Convergent Particle Swarm Optimization for Defect Profile Inversion Using MFL Detector
by Senxiang Lu, Jinhai Liu, Jing Wu and Xuewei Fu
Machines 2022, 10(11), 1091; https://doi.org/10.3390/machines10111091 - 18 Nov 2022
Cited by 4 | Viewed by 1008
Abstract
For the problem of defect inversion in magnetic flux leakage technology, a fast, globally convergent particle swarm optimization algorithm based on the finite-element forward model is introduced as an inverse iterative algorithm in this paper. Two aspects of the traditional particle swarm optimization [...] Read more.
For the problem of defect inversion in magnetic flux leakage technology, a fast, globally convergent particle swarm optimization algorithm based on the finite-element forward model is introduced as an inverse iterative algorithm in this paper. Two aspects of the traditional particle swarm optimization algorithm have been improved: self-adaptive inertia weight and speed updating strategy. For the inertia weight, it can be adaptively adjusted according to the particle position. The speed update strategy mainly uses the best experience positions of other particles in a randomly selected population to realize the algorithm’s learning. At the same time, the learning factor of the position variable is designed to change with the number of iteration steps. The particle with a good position is added to jump out of the local minimum and accelerate the optimization process. Through the comparison experiment, the improved particle swarm optimization algorithm has a faster convergence speed compared with other traditional particle swarm optimization algorithms. It is more difficult for it to fall into the local minimum value and it is more easily converted to a higher precision. Full article
(This article belongs to the Section Automation and Control Systems)
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19 pages, 1881 KiB  
Article
Research on a Visual Servoing Control Method Based on Perspective Transformation under Spatial Constraint
by Chenguang Cao
Machines 2022, 10(11), 1090; https://doi.org/10.3390/machines10111090 - 18 Nov 2022
Cited by 5 | Viewed by 1648
Abstract
Visual servoing has been widely employed in robotic control to increase the flexibility and precision of a robotic arm. When the end-effector of the robotic arm needs to be moved to a spatial point without a coordinate, the conventional visual servoing control method [...] Read more.
Visual servoing has been widely employed in robotic control to increase the flexibility and precision of a robotic arm. When the end-effector of the robotic arm needs to be moved to a spatial point without a coordinate, the conventional visual servoing control method has difficulty performing the task. The present work describes space constraint challenges in a visual servoing system by introducing an assembly node and then presents a two-stage visual servoing control approach based on perspective transformation. A virtual image plane is constructed using a calibration-derived homography matrix. The assembly node, as well as other objects, are projected into the plane after that. Second, the controller drives the robotic arm by tracking the projections in the virtual image plane and adjusting the position and attitude of the workpiece accordingly. Three simple image features are combined into a composite image feature, and an active disturbance rejection controller (ADRC) is established to improve the robotic arm’s motion sensitivity. Real-time simulations and experiments employing a robotic vision system with an eye-to-hand configuration are used to validate the effectiveness of the presented method. The results show that the robotic arm can move the workpiece to the desired position without using coordinates. Full article
(This article belongs to the Topic Smart Manufacturing and Industry 5.0)
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21 pages, 7449 KiB  
Article
Speed Tracking for IFOC Induction Motor Speed Control Using Hybrid Sensorless Speed Estimator Based on Flux Error for Electric Vehicles Application
by Muhamad Syazmie Sepeeh, Shamsul Aizam Zulkifli, Sy Yi Sim, Huang-Jen Chiu and Mohd Zamri Che Wanik
Machines 2022, 10(11), 1089; https://doi.org/10.3390/machines10111089 - 17 Nov 2022
Cited by 2 | Viewed by 1340
Abstract
This paper presents hybrid sensorless speed tracking by an indirect field-oriented control (IFOC) for an induction motor (IM). The sensorless model is based on an improved virtual estimation topology model to predict the virtual speed and flux of the IM using stator current [...] Read more.
This paper presents hybrid sensorless speed tracking by an indirect field-oriented control (IFOC) for an induction motor (IM). The sensorless model is based on an improved virtual estimation topology model to predict the virtual speed and flux of the IM using stator current components. The hybrid sensorless model, defined as a modification of voltage with a rotor flux-oriented current model, was also implemented with proportional-integral (PI) control for comparison with the conventional voltage model (CVM). The suggested adaptive mechanism for PI control in the hybrid estimator was able to compensate for the back-EMF error from the rotor flux-oriented current model into the voltage model and change the air gap flux of the IM. An accurate rotor flux position was estimated and used to estimate the speed with low speed error. This IFOC model, with various speed change references, was tested in a simulation environment by using the MATLAB/Simulink program. The proposed hybrid estimator was tested in two different EV operations, which were reverse and forward operations. The effectiveness of the proposed estimator was analyzed for its transient and steady-state performances based on settling time, recovery time and the overshoot and speed error percentages. All the results were in good agreement in terms of the stability of the speed and current controller with minimum speed error obtained, where the average errors were 0.08% and 0.16% for high speed and lower speed, respectively. Full article
(This article belongs to the Special Issue Advances in Electrical Machines, Drives and Vehicles)
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24 pages, 1000 KiB  
Article
Fault Tolerant Control of Quadrotor Based on Sensor Fault Diagnosis and Recovery Information
by Sunan Huang, Fang Liao and Rodney Swee Huat Teo
Machines 2022, 10(11), 1088; https://doi.org/10.3390/machines10111088 - 17 Nov 2022
Cited by 4 | Viewed by 1550
Abstract
Drones have been developed for more than two decades. They have become central to the functions of various civil aviation and military services. Commercial usage of drones continues to grow steadily. As the drones have been used widely in different areas, this raises [...] Read more.
Drones have been developed for more than two decades. They have become central to the functions of various civil aviation and military services. Commercial usage of drones continues to grow steadily. As the drones have been used widely in different areas, this raises a safety concern, i.e., all the multi-rotors have an increased risk of motor or sensor faults. This paper considers a fault-tolerant control (FTC) problem against the inertial motion unit (IMU) sensor fault. First, a neural network estimator is built for the purpose of fault diagnosis. Second, a fault detection scheme is designed by comparing the IMU reading with the estimator, where it uses a logic rule to monitor the IMU state. Third, if the IMU sensor is in faulty state, the Euler angle estimator with neural network built is used to recover the IMU information which is fed into the controller designed. Finally, simulation studies are given to illustrate the effectiveness of the proposed FTC. Full article
(This article belongs to the Special Issue Dynamics and Motion Control of Unmanned Aerial/Underwater Vehicles)
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24 pages, 8929 KiB  
Article
Influence of Different Reflux Groove Structures on the Flow Characteristics of the Roots Pump
by Wenshuai Xing, Fan Zhang, Feifei Zhao, Jialong Song, Xiumei Zhu and Xingpeng Tang
Machines 2022, 10(11), 1087; https://doi.org/10.3390/machines10111087 - 17 Nov 2022
Cited by 1 | Viewed by 1327
Abstract
A Roots pump often exhibits the typical characteristics of high gas pressure in the exhaust port, low pressure at a basic volume and large airflow pulsation at the outlet as a result of gas reflux. In light of this, this study employed Pumplinx [...] Read more.
A Roots pump often exhibits the typical characteristics of high gas pressure in the exhaust port, low pressure at a basic volume and large airflow pulsation at the outlet as a result of gas reflux. In light of this, this study employed Pumplinx software for the numerical calculation of the entire flow field of a two-bladed Roots pump. The effects of the rectangular and curved reflux groove structures on the internal flow field of a Roots pump, especially on the outlet pressure pulsation and flow rate, were unveiled separately. The rectangular reflux groove controlled the angle and thickness, while the curved reflux groove regulated the coordinates of the key points on the Bezier curve. It is worth recognizing that different reflux groove structures were not noticeable in enhancing the inlet measurement flow pattern; reduce the exhaust pressure pulsation, flow pulsation and exhaust section vortex. Interestingly, the rectangular return groove far outweighed the curved groove when optimizing the pressure and flow pulsation when registering the higher flow loss compared to the curved return groove. The merits and demerits of the Q criterion and omega criterion in characterizing the vortex structure of the flow field in the Roots pump were compared by Tecplot software. The omega criterion looked more robust, clear and continuous in revealing the strong and weak vortices in the Roots pump. The outcome of this research work could provide a reference for the study of Roots pump airflow pulsation, vortex analysis and casing structure design optimization. Full article
(This article belongs to the Section Machine Design and Theory)
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21 pages, 5259 KiB  
Article
A Connective Framework for Social Collaborative Robotic System
by Syed Osama Bin Islam and Waqas Akbar Lughmani
Machines 2022, 10(11), 1086; https://doi.org/10.3390/machines10111086 - 17 Nov 2022
Cited by 2 | Viewed by 1409
Abstract
Social intelligence in robotics appeared quite recently in the field of artificial intelligence (AI) and robotics. It is becoming increasingly evident that social and interaction skills are essentially required in any application where robots need to interact with humans. While the workspaces have [...] Read more.
Social intelligence in robotics appeared quite recently in the field of artificial intelligence (AI) and robotics. It is becoming increasingly evident that social and interaction skills are essentially required in any application where robots need to interact with humans. While the workspaces have transformed into fully shared spaces for performing collaborative tasks, human–robot collaboration (HRC) poses many challenges to the nature of interactions and social behavior among the collaborators. The complex dynamic environment coupled with uncertainty, anomaly, and threats raises questions about the safety and security of the cyber-physical production system (CPPS) in which HRC is involved. Interactions in the social sphere include both physical and psychological safety issues. In this work, we proposed a connective framework that can quickly respond to changing physical and psychological safety state of a CPPS. The first layer executes the production plan and monitors the changes through sensors. The second layer evaluates the situations in terms of their severity as anxiety by applying a quantification method that obtains support from a knowledge base. The third layer responds to the situations through the optimal allocation of resources. The fourth layer decides on the actions to mitigate the anxiety through the allocated resources suggested by the optimization layer. Experimental validation of the proposed method was performed on industrial case studies involving HRC. The results demonstrated that the proposed method improves the decision-making of a CPPS experiencing complex situations, ensures physical safety, and effectively enhances the productivity of the human–robot team by leveraging psychological comfort. Full article
(This article belongs to the Special Issue Recent Advances in Robotics, Factory Automation and Intelligent Networked Systems)
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24 pages, 988 KiB  
Article
A Novel Optimistic Local Path Planner: Agoraphilic Navigation Algorithm in Dynamic Environment
by Hasitha Hewawasam, Yousef Ibrahim and Gayan Kahandawa
Machines 2022, 10(11), 1085; https://doi.org/10.3390/machines10111085 - 16 Nov 2022
Cited by 1 | Viewed by 1376
Abstract
This paper presents a novel local path planning algorithm developed based on the new free space attraction (Agoraphilic) concept. The proposed algorithm is capable of navigating robots in unknown static, as well as dynamically cluttered environments. Unlike the other navigation algorithms, the proposed [...] Read more.
This paper presents a novel local path planning algorithm developed based on the new free space attraction (Agoraphilic) concept. The proposed algorithm is capable of navigating robots in unknown static, as well as dynamically cluttered environments. Unlike the other navigation algorithms, the proposed algorithm takes the optimistic approach of the navigation problem. It does not look for problems to avoid, but rather for solutions to follow. This human-like decision-making behaviour distinguishes the new algorithm from all the other navigation algorithms. Furthermore, the new algorithm utilises newly developed tracking and prediction algorithms, to safely navigate mobile robots. This is further supported by a fuzzy logic controller designed to efficiently account for the inherent high uncertainties in the robot’s operational environment at a reduced computational cost. This paper also includes physical experimental results combined with bench-marking against other recent methods. The reported results verify the algorithm’s successful advantages in navigating robots in both static and dynamic environments. Full article
(This article belongs to the Special Issue Advances in Mobile Robot Navigation in an Unstructured and Dynamically Cluttered Environment)
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18 pages, 8416 KiB  
Article
An Investigation of the Performance and Internal Flow of Variable Nozzle Turbines with Split Sliding Guide Vanes
by Dengfeng Yang, Kai Wang, Huaiyu Wang, Qian Zhang, Xinguo Lei and Leon Hu
Machines 2022, 10(11), 1084; https://doi.org/10.3390/machines10111084 - 16 Nov 2022
Cited by 2 | Viewed by 2398
Abstract
In order to effectively weaken the leakage flow and shock intensity of traditional “swing” type guide vanes in a variable nozzle turbine, a new flow control device named the “split sliding guide vane” (SSGV) is studied in the present work. Steady and unsteady [...] Read more.
In order to effectively weaken the leakage flow and shock intensity of traditional “swing” type guide vanes in a variable nozzle turbine, a new flow control device named the “split sliding guide vane” (SSGV) is studied in the present work. Steady and unsteady calculations were carried out on both the SSGV and base model at 10%, 40%, and 100% open positions. The shock test was performed to verify the accuracy of the numerical method. The results showed that at 10%, 40%, and 100% open positions, the leakage flow of the SSGV was 43%, 51%, and 40% of that of the base model, respectively. When 10% open, the turbine efficiency increased by 12%, compared with the base model, since the SSGV could effectively inhibit the clearance leakage flow. Due to the increased distance between the rotor and guide vane, the shock intensity of the SSGV was only 52% of that of the base model when 40% was open. The SSGV could reduce the static pressure loss on the guide vane pressure surface, but for the guide vane suction surface, the static pressure distribution appeared in a “W” shape due to the influence of the vane profile. Finally, the flow in the rotor was studied, which showed that the weakening of the shock and reduction of the clearance leakage flow in the guide vane were also beneficial for the strength of downstream rotor blades. Full article
(This article belongs to the Section Turbomachinery)
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15 pages, 29412 KiB  
Article
Swin Transformer Combined with Convolution Neural Network for Surface Defect Detection
by Yinghao Li, Yihao Xiang, Haogong Guo, Panpan Liu and Chengming Liu
Machines 2022, 10(11), 1083; https://doi.org/10.3390/machines10111083 - 16 Nov 2022
Cited by 5 | Viewed by 2254
Abstract
Surface defect detection aims to classify and locate a certain defect that exists in the target surface area. It is an important part of industrial quality inspection. Most of the research on surface defect detection are currently based on convolutional neural networks (CNNs), [...] Read more.
Surface defect detection aims to classify and locate a certain defect that exists in the target surface area. It is an important part of industrial quality inspection. Most of the research on surface defect detection are currently based on convolutional neural networks (CNNs), which are more concerned with local information and lack global perception. Thus, CNNs are unable to effectively extract the defect features. In this paper, a defect detection method based on the Swin transformer is proposed. The structure of the Swin transformer has been fine-tuned so that it has five scales of output, making it more suitable for defect detection tasks with large variations in target size. A bi-directional feature pyramid network is used as the feature fusion part to efficiently fuse to the extracted features. The focal loss is used as a loss function to weight the hard- and easy-to-distinguish samples, potentially making the model fit the surface defect data better. To reduce the number of parameters in the model, a shared detection head was chosen for result prediction. Experiments were conducted on the flange surface defect dataset and the steel surface defect dataset, respectively. Compared with the classical CNNs target detection algorithm, our method improves the mean average precision (mAP) by about 15.4%, while the model volume and detection speed are essentially the same as those of the CNNs-based method. The experimental results show that our proposed method is more competitive compared with CNNs-based methods and has some generality for different types of defects. Full article
(This article belongs to the Section Machines Testing and Maintenance)
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17 pages, 7868 KiB  
Article
Human-Centered Design of a Collaborative Robotic System for the Shoe-Polishing Process
by Giorgia Chiriatti, Marianna Ciccarelli, Matteo Forlini, Melania Franchini, Giacomo Palmieri, Alessandra Papetti and Michele Germani
Machines 2022, 10(11), 1082; https://doi.org/10.3390/machines10111082 - 16 Nov 2022
Cited by 3 | Viewed by 2246
Abstract
Demand for automated processes in the manufacturing industry is now shifting toward flexible, human-centered systems that combine productivity and high product quality, thus combining the advantages of automated and robotic systems with the high-value-added skills of operators and craftsmen. This trend is even [...] Read more.
Demand for automated processes in the manufacturing industry is now shifting toward flexible, human-centered systems that combine productivity and high product quality, thus combining the advantages of automated and robotic systems with the high-value-added skills of operators and craftsmen. This trend is even more crucial for small and medium-sized enterprises operating in the “Made in Italy” fashion industry. The paper presents the study, simulation, and preliminary testing of a collaborative robotic system for shoe polishing that can reduce manual labor by limiting it to the finishing stage of the process, where the aesthetic result is fully achieved, with a benefit also in terms of ergonomics for the operator. The influence of process parameters and design solutions are discussed by presenting preliminary test results and providing hints for future developments. Full article
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16 pages, 4656 KiB  
Article
Machining Quality Prediction of Marine Diesel Engine Block Based on Error Transmission Network
by Li Sun, Xiaodie Ren, Honggen Zhou, Guochao Li, Weibin Yang, Junjie Zhao and Yinfei Liu
Machines 2022, 10(11), 1081; https://doi.org/10.3390/machines10111081 - 16 Nov 2022
Cited by 3 | Viewed by 1202
Abstract
In view of the high precision requirement of the marine diesel engine body and the difficulty of quality control, a quality prediction method of the body, based on a process error transfer network, is proposed. First, according to the processing information of the [...] Read more.
In view of the high precision requirement of the marine diesel engine body and the difficulty of quality control, a quality prediction method of the body, based on a process error transfer network, is proposed. First, according to the processing information of the body, the network nodes and edges are abstracted to establish the process error transfer network of the body. Then, the key quality control points and key quality features of the diesel engine body are determined by the PageRank and node degree. The key quality features obtained from the network analysis are taken as the output, and the corresponding process errors and process parameters are taken as the input. Finally, the quality prediction model of the body is established based on SVR algorithm, and the C, g parameters of SVR algorithm are optimized by the K-fold cross-validation method and grid search method to improve the prediction accuracy of the body processing quality. Full article
(This article belongs to the Special Issue Advances in Diagnostics and Prognostics in the Era of Industry 4.0)
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22 pages, 10354 KiB  
Article
Research on the Power Loss of High-Speed and High-Load Ball Bearing for Cryogenic Turbopump
by Wenhu Zhang, Chaojie Zhang, Xusheng Miao, Liang Li and Sier Deng
Machines 2022, 10(11), 1080; https://doi.org/10.3390/machines10111080 - 16 Nov 2022
Cited by 1 | Viewed by 1808
Abstract
This paper studies the lubrication characteristics of ball bearings for cryogenic turbopumps. First, the frictional coefficients between 440C and a Ag coating, 440C and solid PTFE (polytetrafluoroethylene), and 440C and a PTFE coating in LN2 (liquid nitrogen) are obtained using a ball-on-disk testing [...] Read more.
This paper studies the lubrication characteristics of ball bearings for cryogenic turbopumps. First, the frictional coefficients between 440C and a Ag coating, 440C and solid PTFE (polytetrafluoroethylene), and 440C and a PTFE coating in LN2 (liquid nitrogen) are obtained using a ball-on-disk testing machine under a high sliding speed in the range of 0 to 8 m/s and a high contact stress in the range of 2.5 to 3.5 GPa. Dynamic and power loss models of high-speed and high-load ball bearings are established to study the key factors affecting the heat generation characteristics. In order to verify the correctness of these two theoretical models, a coupled fluid-thermal finite element model is built to evaluate the temperatures of the outer ring under different bearing speeds, which are then proved by experiments with ball bearings for cryogenic turbopumps. The results show that the power loss due to the spinning-sliding of the ball and the churning and drag of LN2 account for more than 80% of the total power loss; the spin-roll ratio of the ball on the inner raceway is a key indicator for this type of ball bearing, and the relatively small radial clearance and contact angle are suggested. Both of the proposed theoretical models have sufficient accuracy and can be used in the performance evaluation and optimization design of bearings. Full article
(This article belongs to the Special Issue Advanced Dynamic Analysis and Vibro-Acoustic Control Methods)
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20 pages, 8493 KiB  
Article
Robust Object Positioning for Visual Robotics in Automatic Assembly Line under Data-Scarce Environments
by Yigong Zhang, Huadong Song, Xiaoting Guo and Chaoqing Tang
Machines 2022, 10(11), 1079; https://doi.org/10.3390/machines10111079 - 16 Nov 2022
Cited by 1 | Viewed by 1364
Abstract
Object positioning is a basic need for visual robotics in automatic assembly lines. An assembly line requires fast transfer to new object positioning tasks with few or no training data for deep learning algorithms, and the captured visual images usually suffer from partial [...] Read more.
Object positioning is a basic need for visual robotics in automatic assembly lines. An assembly line requires fast transfer to new object positioning tasks with few or no training data for deep learning algorithms, and the captured visual images usually suffer from partial missing and cropping and environmental lighting interference. These features call for efficient and robust arbitrary shape positioning algorithms under data-scarce and shape distortion cases. To this end, this paper proposes the Random Verify Generalised Hough Transform (RV-GHT). The RV-GHT builds a much more concise shape dictionary than traditional GHT methods with just a single training image. The location, orientation, and scaling of multiple target objects are given simultaneously during positioning. Experiments were carried out on a dataset in an automatic assembly line with real shape distortions, and the performance was improved greatly compared to the state-of-the art methods. Although the RV-GHT was initially designed for vision robotics in an automatic assembly line, it works for other object positioning mechatronics systems, which can be modelled as shape distortion on a standard reference object. Full article
(This article belongs to the Special Issue Computer/Machine Vision Applications in Automation, Robotic, Mechatronic Systems)
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15 pages, 3410 KiB  
Article
Research on an Adaptive Real-Time Scheduling Method of Dynamic Job-Shop Based on Reinforcement Learning
by Haihua Zhu, Shuai Tao, Yong Gui and Qixiang Cai
Machines 2022, 10(11), 1078; https://doi.org/10.3390/machines10111078 - 15 Nov 2022
Cited by 2 | Viewed by 2027
Abstract
With the rapid development of modern industrialization in our country and the continuous improvement of people’s living standards, the changing market has put forward new requirements for traditional manufacturing enterprises. The order product demand of many manufacturing enterprises is changing from a single [...] Read more.
With the rapid development of modern industrialization in our country and the continuous improvement of people’s living standards, the changing market has put forward new requirements for traditional manufacturing enterprises. The order product demand of many manufacturing enterprises is changing from a single variety, large batch to multiple varieties, small batch. In view of this change, the traditional job-shop scheduling method is far from enough, which greatly affects the efficiency of the production job-shop. In order to solve the above problems, this paper proposes a real-time scheduling method based on reinforcement learning applied in the dynamic job-shop and a new type of neural network is designed at the same time. The neural network is designed with the high-dimensional data in the above problem as input, and a policy-based reinforcement learning algorithm is proposed based on this network. In the process of research, it was found that the reinforcement learning method not only enables the agent to use historical data for learning, but also enables it to explore and learn other possible high-reward actions within a certain range, so as to realize the optimization of production goals under real-time scheduling. The effectiveness of the proposed real-time scheduling method is verified by comparing with other common rule-based scheduling methods in the manufacturing environment. Full article
(This article belongs to the Section Industrial Systems)
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11 pages, 3569 KiB  
Article
A New 2D Displacement Measurement Method Based on an Eddy Current Sensor and Absolute Encoding
by Kunlong Ma, Qianyun Yang, Jin Zhang, Xueming Dang and Penghao Hu
Machines 2022, 10(11), 1077; https://doi.org/10.3390/machines10111077 - 15 Nov 2022
Cited by 3 | Viewed by 1175
Abstract
A new method of two-dimensional (2D) plane displacement measurement based on an eddy current sensor is proposed in this paper. A series of grooves with different widths and depths are graved on the linear displacement table to form 2D absolute coding using the [...] Read more.
A new method of two-dimensional (2D) plane displacement measurement based on an eddy current sensor is proposed in this paper. A series of grooves with different widths and depths are graved on the linear displacement table to form 2D absolute coding using the idea of pseudorandom coding. The eddy current sensor array is arranged above the groove to identify the coding. An artificial neural network is used to establish a measurement model which is the mapping relationship between the output of the eddy current sensor array and the 2D displacement of the workbench. A feasibility experiment showed that in the range of 20 × 20 mm, the root mean square error of measurement in the X- and Y-directions are 83 and 73 μm, respectively. The new method integrates eddy current sensor and artificial neural network modeling to realize 2D displacement measurement, which provides a new solution for displacement and angle measurement. Full article
(This article belongs to the Section Robotics, Mechatronics and Intelligent Machines)
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16 pages, 5391 KiB  
Article
A Neural Network Structure with Attention Mechanism and Additional Feature Fusion Layer for Tomato Flowering Phase Detection in Pollination Robots
by Tongyu Xu, Xiangyu Qi, Sen Lin, Yunhe Zhang, Yuhao Ge, Zuolin Li, Jing Dong and Xin Yang
Machines 2022, 10(11), 1076; https://doi.org/10.3390/machines10111076 - 15 Nov 2022
Cited by 3 | Viewed by 1187
Abstract
In recent years, convolutional neural networks have made many advances in the field of computer vision. In smart greenhouses, using robots based on computer vision technology to pollinate flowers is one of the main methods of pollination. However, due to the complex lighting [...] Read more.
In recent years, convolutional neural networks have made many advances in the field of computer vision. In smart greenhouses, using robots based on computer vision technology to pollinate flowers is one of the main methods of pollination. However, due to the complex lighting environment and the influence of leaf shadow in the greenhouse, it is difficult for the existing object detection algorithms to have high recall rate and accuracy. Based on this problem, from the perspective of application, we proposed a Yolov5s-based tomato flowering stage detection method named FlowerYolov5, which can well identify the bud phase, blooming phase and first fruit phase of tomato flowers. Firstly, in order to reduce the loss of tomato flower feature information in convolution and to strengthen the feature extraction of the target, FlowerYolov5 adds a new feature fusion layer. Then, in order to highlight the information of the object, the Convolutional Block Attention module (CBAM) is added to the backbone layer of FlowerYolov5. In the constructed tomato flower dataset, compared with YOLOv5s, the mAP of FlowerYolov5 increased by 7.8% (94.2%), and the F1 score of FlowerYolov5 increased by 6.6% (89.9%). It was found that the overall parameter of FlowerYolov5 was 23.9 Mbyte, thus achieving a good balance between model parameter size and recognition accuracy. The experimental results show that the FlowerYolov5 has good robustness and more accurate precision. At the same time, the recall rate has also been greatly improved. The prediction results of the proposed algorithm can provide more accurate flower positioning for the pollination robot and improve its economic benefits. Full article
(This article belongs to the Special Issue Design, Modeling and Control of Advanced Mechatronic Systems)
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