Optimal Speed Research Articles

Research on Multi-objective Optimization Model of Power Storage Materials Based on NSGA-II Algorithm

Aiming at the problems of slow convergence speed and low precision probability of multi-objective optimization of energy storage materials, a multi-objective optimization model of energy storage materials based on NSGA-II algorithm was proposed. The association rule set of storage materials in the joint supply chain operation performance management system is extracted, and the rough vector feature distribution set multi-objective optimization method is used to decompose and optimize the characteristics of storage materials in the joint supply chain operation performance management system. Using NSGA-II optimization analysis method, this paper summarizes the power storage materials under the joint supply chain operation performance management system, and summarizes three kinds of inventory control: periodic inventory, inventory coding, and computerized inventory. Combined with the positive regression learning method of organizational operational performance, the multi-objective optimization decision of electric storage materials under the joint supply chain operational performance management system is realized. The simulation results show that under the joint supply chain operation performance management system, the proposed method reaches the optimal convergence after 65 iterations, the convergence speed is fast, and the accuracy probability reaches 1.000 after 80 iterations, which solves the problems of slow convergence speed and low accuracy probability, and has a good scheduling ability of energy storage materials.

Performance Assessment of Two-Wheeler Electric Vehicle Batteries Using Multi-Mode Drive Cycles

This article presents a model-based approach to assess the battery performance of a two-wheeler EV drive train system for various user driving patterns using the selected urban drive cycles. The battery pack is one of the most expensive parts of an EV, and its life is heavily dependent on its usage pattern. The impact of the user’s driving behaviour on the performance parameters of the EV battery pack needs to be investigated. Thus, a two-wheeler EV drive train model was developed in MATLAB with a 5 kW motor, a 4.32 kWh battery, vehicle dynamics, and the power train control algorithms for in-depth analysis of battery performance. The validity of the developed model was tested against various state-of-the-art drive cycles for a duration of 3600 s. Numerous user driving behaviours, such as aggressive, moderate, and slow driving behaviours, were modelled with modified drive cycles, which were used to assess the two-wheeler battery pack performance. An optimum speed range, which ranges from 21 km/h to 34 km/h for different drive cycles, was identified, and these speed ranges minimised the battery energy consumption for the selected drive cycles with the modified drive cycle models.

Design and verification of a new non-contact piezoelectric energy harvester based on a sinusoidal exciting mechanism.

In this paper, a new non-contact rotary piezoelectric energy harvester based on a sinusoidal exciting mechanism has been proposed. The energy transformation is realized in a non-contact form. The sinusoidal orbital rotor can act as a sinusoidal excitation to the contacts, and it can avoid damage to piezoelectric ceramics from direct strikes while bending piezoelectric cantilever beams. After a series of experiments, the prototype demonstrated an excellent output performance. Having explored the influence of the rotation speed on the output voltage, it reaches the peak when the rotation speed is 180rpm and the maximum voltage is 18.6V. The relationship between power and voltage was validated with the rise of resistance at the optimum speed. When the resistance is 10 kΩ, the power that arrives at the peak is 1.35 mW, and the maximum voltage is 12.1V when the resistance is 200 kΩ. Some application experiments have been designed and verify the feasibility of the prototype; it can light up 18 LEDs and power some microelectronic equipment.

Study on Resin-based 3D Printed Product for Spin Casting Mold Making

Spin casting (centrifugal rubber mold casting) is well known process to produce castings by utilizing inertia. A vulcanized rubber mold is typically used by spinning it at an optimum speed along its central axis. There are many factors influence the quality of casting product, including the quality of mold, the temperature of molten material, the speed and duration of rotation, as well as the pressure upon the mold. This research aims to investigate the quality of casting product when a resin-based 3D printing is utilized to make a model for vulcanized rubber mold. The model is designed by using CAD software and printed layer by layer by using Selective Laser Sintering (SLS) method. Curing process is employed in order to harden the model before placed in the disc-shape silicone rubber. The rubber is then vulcanized so that the mold is formed. Standard edge gate and straight runner are then made to let the molten material flow during casting process. Zinc alloy is used as raw material in the casting process with five variations of rotation speed. Based on the experiment results it is observed that the resin-based 3D printed product can be used as a master model in rubber mold making with some limitations. The resin could not maintain its shape due to the high temperature and pressure during vulcanization process. However, for simple design without any need of very small and detail features, the resin-based 3D printed product still can be used as an alternative.

Effects of Confinement on Opposed-Flow Flame Spread over Cellulose and Polymeric Solids in Microgravity

Opposed-flow flame spread over solid materials has been investigated in the past few decades owing to its importance in fundamental understanding of fires. These studies provided insights on the behavior of opposed-flow flames in different environmental conditions (e.g., flow speed, oxygen concentration). However, the effect of confinement on opposed-flow flames remains under-explored. It is known that confinement plays a critical role in concurrent-flow flame spread in normal and microgravity conditions. Hence, for a complete understanding it becomes important to understand the effects of confinement for opposed-flow flames. In this study, microgravity experiments are conducted aboard the International Space Station (ISS) to investigate opposed-flow flame spread in different confined conditions. Two materials, cotton-fiberglass blended textile fabric (SIBAL) and 1 mm thick polymethyl methacrylate (PMMA) slab are burned between a pair of parallel flow baffles in a small flow duct. By varying the sample-baffle distance, various levels of confinement are achieved (H = 1–2 cm). Three types of baffles, transparent, black, and reflective, are used to create different radiative boundary conditions. The purely forced flow speed is also varied (between 2.6 and 10.5 cm/s) to investigate its interplay with the confinement level. For both sample materials, it is observed that the flame spread rate decreases when the confinement level increases (i.e., when H decreases). In addition, flame spread rate is shown to have a positive correlation with flow speed, up to an optimal value. The results also indicate that the optimal flow speed for flame spread can decrease in highly confined conditions. Surface radiation on the confinement boundary is shown to play a key role. For SIBAL fabric, stronger flames are observed when using black baffles compared to transparent. For PMMA, reflective baffles yield stronger flames compared to black baffles. When comparing the results to the concurrent-flow case, it is also noticed that opposed-flow flames spread slower and blow off at larger flow speeds but are not as sensitive to the flow speed. This work provides unique long-duration microgravity experimental data that can inform the design of future opposed-flow experiments in microgravity and the development of theory and numerical models.

Optimal proportional-integral speed control for closed-loop engine timing system

In internal combustion engines, adjusting the air-fuel ratio is essential to control the speed and minimize the burnt fuel. The throttle opening is the actuator to control the air-fuel. A better design for the used/conventional controller can give a better response without additional cost. In this work, the proposed controller gains of the proportional-integral (PI) controller are tuned to enhance the speed in constant and variable drive cycle modes. The tuning process is conducted based on two of the most efficient performance indices used in this field. The performance indices are integral absolute error (IAE) and integral time absolute error (ITAE). The optimization problem is solved using three reliable stochastic optimization algorithms to ensure mature convergence of the solutions, to avoid local optima solutions, and to ensure effective shrinking of the search space. The optimization algorithms are teaching-learning-based optimization (TLBO), particle swarm optimization (PSO), and genetic algorithm (GA). Different simulations are conducted to validate the results. The results are compared with conventional tuning methods regarding the system's time response.

Electrode sharpness and insertion speed reduce tissue damage near high-density penetrating arrays

Objective. Over the past decade, neural electrodes have played a crucial role in bridging biological tissues with electronic and robotic devices. This study focuses on evaluating the optimal tip profile and insertion speed for effectively implanting Paradromics’ high-density fine microwire arrays (FμA) prototypes into the primary visual cortex (V1) of mice and rats, addressing the challenges associated with the ‘bed-of-nails’ effect and tissue dimpling. Approach. Tissue response was assessed by investigating the impact of electrodes on the blood-brain barrier (BBB) and cellular damage, with a specific emphasis on tailored insertion strategies to minimize tissue disruption during electrode implantation. Main results. Electro-sharpened arrays demonstrated a marked reduction in cellular damage within 50 μm of the electrode tip compared to blunt and angled arrays. Histological analysis revealed that slow insertion speeds led to greater BBB compromise than fast and pneumatic methods. Successful single-unit recordings validated the efficacy of the optimized electro-sharpened arrays in capturing neural activity. Significance. These findings underscore the critical role of tailored insertion strategies in minimizing tissue damage during electrode implantation, highlighting the suitability of electro-sharpened arrays for long-term implant applications. This research contributes to a deeper understanding of the complexities associated with high-channel-count microelectrode array implantation, emphasizing the importance of meticulous assessment and optimization of key parameters for effective integration and minimal tissue disruption. By elucidating the interplay between insertion parameters and tissue response, our study lays a strong foundation for the development of advanced implantable devices with a reduction in reactive gliosis and improved performance in neural recording applications.

Kinetic model for polyhydroxybutyrate production by Bacillus aryabhattai MH997667.1 in a batch culture using different agitation speeds

Background Slow degradation of synthetic plastics represents a high environmental hazard; therefore, there is an essential requirement to substitute them with eco-friendly products. Polyhydroxybutyrate (PHB) is a biodegradable biopolymer and also has several industrial, agricultural, and medical applications. Scaling up the production of PHB is still a problem due to the numerous parameters tangled in the fermentation processes. Objective The present work seeks to scale up polyhydroxybutyrate production by Bacillus aryabhattai MH997667.1 from shaken flasks to a 5L-bench top bioreactor with previously optimized media by applying a batch fermentation strategy. Materials and methods Different agitation speeds (200, 250 and 300 rpm) were tested in a 5L-bench-top bioreactor with a working volume of 3L. A kinetic model (logistic and Luedeking–Piret) that describes the microbial biomass and polyhydroxybutyrate (PHB) production is used to expect the performance of batch fermentation of Bacillus aryabhattai MH997667.1. Results and conclusion The optimum PHB yield (2.32 g/l) was expressed at 250 rpm agitation after 28 hours of fermentation. The experimental data were also fitted with the logistic and Luedeking–Piret equations for growth and PHB formation, respectively. The mathematical model proposed for batch fermentation revealed that the simulated data showed a good fit with the experimental results obtained during the first 24 h of PHB production at 250 rpm, where the productivity was 0.095 g L−1h−1. Our data suggest that agitation speed had a significant effect on PHB production and the 250 rpm agitation speed is the optimum speed for PHB production using Bacillus aryabhattai MH997667.1 in batch fermentation.

(187) - Optimized or Not? International Practices for VAD Speed Optimization

(187) - Optimized or Not? International Practices for VAD Speed Optimization

Multi-objective arterial coordination control method based on induction control and vehicle speed guidance

The fixed green wave speed and staged statistical flow used in arterial signal coordination are not adaptable to the fluctuations in vehicle travel speed and traffic flow on roads, resulting in a mismatch between the signal scheme and the optimal green wave speed and traffic flow demand. This discrepancy negatively impacts the efficiency of intersection traffic. In traditional signal control systems, the cycle and green light timing are typically set independently. However, such a setting method poses problems in practical operation. In this paper, we combine vehicle arrival and vehicle location information, and consider the interaction of speed guidance and dynamic signal optimization to construct a model. This study is developed along the following steps: in the vehicle-road coordination environment, based on the MAXBAND model, a global coordination scheme is obtained, incorporating the speed guidance method; then, based on the vehicle saturation of the inlet lane of the arterial intersection, a multi-objective optimization model for arterial signal coordination under vehicle speed guidance is established based on global coordination with the maximum green wave bandwidth and the minimum delay of arterial vehicles, the minimum number of arterial stops and the minimum delay in the minor direction road as the optimization objectives. Based on global coordination, adopting an integrated control mechanism of cycle and green light timing allows for dynamic adjustments according to real-time traffic conditions. The improved multi-objective particle swarm algorithm is chosen to solve the model, and the simulation environment is built based on the COM interface of VISSIM software and C# platform. Three adjacent intersections of Ganjiang Middle Road in Nanchang are selected as case studies, and the methods in this paper are compared with the current timing scheme, the MAXBAND method and the optimization scheme under speed guidance only, respectively. The results show that the model proposed in this paper achieves significant optimization effects on the indicators of arterial delay, arterial stopping times and the delay of minor roads.

Working speed optimisation of the fully automated vegetable seedling transplanter

The purpose of this study was to determine the optimal operating speeds for a modified linkage cum hopper type planting unit that was used in low-speed automated vegetable transplanters. The transplanter utilizes a biodegradable seedling plug-tray feeding mechanism. The movement of the planter unit was simulated at different operating conditions using kinematic simulation software, and the resulting trajectories were compared based on factors such as plant spacing, soil intrusion area, soil intrusion perimeter, and horizontal displacement of the hopper in soil and found optimal result at 200, 250 and 300 mm/s and 40, 50 and 60 rpm combinations. The optimal operating speeds were then tested in a soil bin facility and found to perform well when transplanting pepper seedlings, with measured plant spacing that was close to the theoretical spacing. The planting depth in each case was not significantly different and the planting angle in different speed combinations was found to be significantly different, but within permissible limits. The mulch film damage was low for the selected optimised speed combinations. This study resulted in the determination of the optimal speeds for the transplanter, which can be used as a basis for optimising the other mechanisms within the transplanter.

GSO-Net: Grid Surface Optimization via Learning Geometric Constraints

In the context of surface representations, we find a natural structural similarity between grid surface and image data. Motivated by this inspiration, we propose a novel approach: encoding grid surfaces as geometric images and using image processing methods to address surface optimization-related problems. As a result, we have created the first dataset for grid surface optimization and devised a learning-based grid surface optimization network specifically tailored to geometric images, addressing the surface optimization problem through a data-driven learning of geometric constraints paradigm. We conduct extensive experiments on developable surface optimization, surface flattening, and surface denoising tasks using the designed network and datasets. The results demonstrate that our proposed method not only addresses the surface optimization problem better than traditional numerical optimization methods, especially for complex surfaces, but also boosts the optimization speed by multiple orders of magnitude. This pioneering study successfully applies deep learning methods to the field of surface optimization and provides a new solution paradigm for similar tasks, which will provide inspiration and guidance for future developments in the field of discrete surface optimization. The code and dataset are available at https://github.com/chaoyunwang/GSO-Net.

LERMO: A Novel Web Game for AI-Enhanced Sign Language Recognition

Sign language is a visual and gestural communication system used by deaf and hearing-impaired people. Despite numerous deep learning methods proposed for automatic interpretation, a gap persists in developing applications that effectively utilize these models for assisting sign language studies and inclusion. We introduce LERMO (https://lermo.app/), a web game merging machine learning and gamification to enhance sign language fingerspelling. Inspired by Wordle™, LERMO offers an interactive word-guessing game where users can play using a video camera. We create a new dataset of labeled landmark fingerspelling and design our model to ensure optimal speed and efficiency to run on a web browser. We survey approximately 40 users, which find LERMO user-friendly and innovative. From those, 95% believe LERMO could be used to enhance fingerspelling skills.

A Collaborative Control Framework: Achieving Emergency Vehicle Priority While Minimizing Negative Impact on Ordinary Vehicles at Signalized Intersection

When an emergency vehicle (EV) passes through an isolated signal intersection, it is crucial to ensure the efficient passage of the EV while minimizing the negative impact on ordinary vehicles (OVs), particularly in high-traffic flow scenarios. Given the constraints on temporal and spatial resources within intersection areas, OVs ahead of EV often face challenges in finding safe gaps for giving way, resulting in significant obstructions to OVs. This research introduces a novel collaborative control framework to jointly optimize dynamic emergency lane settings and signal schemes, considering EV priority and OVs benefits for a single signalized intersection. Firstly, we propose a dynamic emergency lane control algorithm to help obstructed EV in roadway segments by extending and reallocating temporal and spatial resources for vehicles. Then, we establish a collaborative control model considering EV priority and OVs benefits. Assigning the highest priority to the emergency priority phase, this model optimizes signal schemes to prevent interphase conflict, taking into account OVs benefits. Finally, our collaborative control framework also employs an Eco-Driving algorithm for the optimization of OV speed to reduce fuel consumption. The case study results reveal that in comparison to other baseline methods, our proposed model significantly reduces EV travel time, simultaneously lowering the travel time and fuel consumption of OVs. Sensitivity analysis of varying traffic flow scenarios reveals that, as vehicle volumes increase, our proposed method demonstrates more pronounced reductions in both EV and OV travel time. In addition, there is a progressive increase in the proportion of dynamic emergency lane utilization, with activation occurring at earlier locations.

In‐Situ Wavefront Correction via Physics‐Informed Neural Network

AbstractWavefront distortions pose a significant limitation in various optical applications, hindering further advancements in optical system performance. In this study, a novel generic calibration model based on Zernike‐fitting neural network (ZFNN) is proposed, which enables insitu wavefront correction with just a single‐shot measurement. The experimental setup follows a standard or equivalent focal‐field imaging optical path, allowing calibration without the need to remove any components from the optical system. The ZFNN, a physics‐informed neural network, offers the advantage of not requiring prior training, eliminating the need for extensive labeled data. With a fully connected network architecture and a modest number of neurons (469), the ZFNN achieves exceptionally fast optimization speed and meets the basic requirements for real‐time calibration. Consequently, this approach holds great potential for applications such as rapid calibration of optical systems, high‐precision light field modulation, and various advanced imaging techniques.

Dynamic Amplification of Railway Bridges under Varying Wagon Pass Frequencies

Train configurations give rise to a primary wagon pass forcing frequency and their multiples. When any one of these frequencies coincides with the natural frequency of vibration of the bridge, a resonant response can occur. This condition can amplify the dynamic response of the bridge, leading to increased levels of displacement, stresses and acceleration. Increased stress levels on critical bridge structural elements increases the rate at which fatigue damage accumulates. Increased bridge acceleration levels can affect passenger comfort, noise levels, and can also compromise train safety. For older bridges the effects of fatigue, and being able to predict the remaining life, has become a primary concern for bridge engineers. Better understanding of the sensitivity of fatigue damage to the characteristics of the passing train will lead to more accurate remaining life predictions and can also help to identify optimal train speeds for a given train–bridge configuration. In this paper, a mathematical model which enables the dynamic response of railway bridges to be assessed for different train configurations is presented. The model is based on the well established closed from solution of the Euler–Bernoulli Beam (EBB) model, for a series of moving loads, using the inverse Laplace–Carson transform. In this work the methodology is adapted to allow different train configurations to be easily implemented into the formulation in a generalised form. A generalised equation, which captures the primary wagon pass frequency for any train configuration, is developed and verified by presenting the results of the bridge response in the frequency domain. The model, and the accuracy of the equation for predicting the primary wagon pass frequency, is verified using independently obtained measured field train–bridge response data. The main emphasis of this work is to enable the practicing engineer, railway operators and bridge asset owners, to easily and efficiently make an initial assessment of dynamic amplification, and the optimal train speeds, for a given bridge and train configuration. This is visually presented in this work using a Campbell diagram, which shows dynamic amplification and compares this with those calculated based on the design code, across a range of train speeds. The diagram is able to identify train speeds at which a resonance response can occur, and the wagon pass frequency, or its multiples, which are causing the increased dynamic amplification. The model is implemented in Matlab and demonstrated by analysing a range of short- to medium-single span simply supported plate girder railway bridges, typically found on the UK railway network, using the standard BS-5400 train configurations. The model does not consider the effects of the train mass and suspension system as this would require a non-closed form numerical solution of the problem which is not practical for the purposes of an initial assessment of the train–bridge interaction problem.

Effect of Pumping Speeds on the Fate of Aniline in Different Soil Layer

Helan Mountain is an important ecological safety barrier in northwest China. In this study, a heterogeneous site polluted by aniline on Helan Mountain was the research object, and the TMVOC (A Simulator For Multiple Volatile Organic Chemicals) model of aniline restoration by pumping was optimized by employing a column experiment. Four typical layers of the soil medium were selected to explore the influence of soil settlement caused by different pumping speeds on the fate of aniline in different zones. The results show that the optimal pumping speed at the site is 3.24 × 106 m3/month and the latest remediation time is the 10th month after the start of the remediation. The larger the pumping speed is, the more obvious the sedimentation effect is. When the remediation is carried out at 5.18 × 106 m3/month, the NAPL (Non-Aqueous-Phase Liquid) phase removal rate decreases by 33.75% and the distribution of aniline to the NAPL phase increases, compared to that without considering the soil settlement. The fate of aniline in the source zone is the least affected by sedimentation, while that in the vadose zone is the most affected. The phase redistribution phenomenon is the most obvious in the water table fluctuation zone, and the NAPL phase aniline changes into gas and liquid phases. In addition, the NAPL phase concentration in the water table fluctuation zone is two orders of magnitude higher than that at 0.2 m below the water table. NAPL is the most sensitive to the relative settlement in the aquifer. The simulation results can provide a technical reference for the future application of P&T (Pump-and-Treat) technology in the remediation of organically contaminated sites to facilitate the sustainable use of soil. It is suggested that more attention should be paid to the water table fluctuation zone during the remediation of contaminated sites.

Model predictive control for real-time microstructure control in laser heat treatment

Metals’ surface microstructure can be altered through controlled heating and cooling of the surface using the laser heat treatment (LHT) method. Lasers have the advantage in this process of being able to heat treat specific regions of the workpiece without affecting the whole product. However, because a laser is being used, this process is extremely sensitive to changes in the process inputs and disturbances, which results in changes in the thermal dynamics and alterations in the peak temperature and cooling rate, in particular. These alterations have a direct effect on the material and its mechanical characteristics, such as hardness and hardening depth. Since these variations are caused by complex metallurgical phenomena, it is important to control the thermal dynamics in real-time. Real-time control systems are usually error-based and do not have the ability to predict the effect of the control action on the system, deal with constraints, or correlate the mechanical and material properties of the processed materials to LHT process parameters. Hence, the development of accurate model-based controllers is required. Therefore, a Model Predictive Control (MPC) algorithm is developed in this paper to control the thermal dynamics during the LHT process, e.g., the peak temperature, in real-time. This controller uses a finite difference thermal model to find the optimal process speed that results in the desired peak temperature. Moreover, consistent hardness and hardening depth will be obtained by closed-loop peak temperature control.

Fuel consumption estimation for jet aircraft: a mathematical model development

PurposeReducing aircraft fuel consumption has become a paramount research area, focusing on optimizing operational parameters like speed and altitude during the cruise phase. However, the existing methods for fuel reduction often rely on complex experimental calculations and data extraction from embedded systems, making practical implementation challenging. To address this, this study aims to devise a simple and accessible approach using available information.Design/methodology/approachIn this paper, a novel analytic method to estimate and optimize fuel consumption for aircraft equipped with jet engines is proposed, with a particular emphasis on speed and altitude parameters. The dynamic variations in weight caused by fuel consumption during flight are also accounted for. The derived fuel consumption equation was rigorously validated by applying it to the Boeing 737–700 and comparing the results against the fuel consumption reference tables provided in the Boeing manual. Remarkably, the equation yielded closely aligned outcomes across various altitudes studied. In the second part of this paper, a pioneering approach is introduced by leveraging the particle swarm optimization algorithm (PSO). This novel application of PSO allows us to explore the equation’s potential in finding the optimal altitude and speed for an actual flight from Algiers to Brussels.FindingsThe results demonstrate that using the main findings of this study, including the innovative equation and the application of PSO, significantly simplifies and expedites the process of determining the ideal parameters, showcasing the practical applicability of the approach.Research limitations/implicationsThe suggested methodology stands out for its simplicity and practicality, particularly when compared to alternative approaches, owing to the ready availability of data for utilization. Nevertheless, its applicability is limited in scenarios where zero wind effects are a prevailing factor.Originality/valueThe research opens up new possibilities for fuel-efficient aviation, with a particular focus on the development of a unique fuel consumption equation and the pioneering use of the PSO algorithm for optimizing flight parameters. This study’s accessible approach can pave the way for more environmentally conscious and economical flight operations.

Design and Experiment of Automatic Transport System for Planting Plate in Plant Factory

In the plant factories using stereoscopic cultivation systems, the cultivation plate transport equipment is an essential component of production. However, there are problems, such as high labor intensity, low levels of automation, and poor versatility of existing solutions, that can affect the efficiency of cultivation plate transport processes. To address these issues, this study designed a cultivation plate transport system that can automatically input and output cultivation plates, and can flexibly adjust its structure to accommodate different cultivation frame heights. We elucidated the working principles of the transport system and carried out structural design and parameter calculation for the lift cart, input actuator, and output actuator. In the input process, we used dynamic simulation technology to obtain an optimum propulsion speed of 0.3 m·s−1. In the output process, we used finite element numerical simulation technology to verify that the deformation of the cultivation plate and the maximum stress suffered by it could meet the operational requirements. Finally, operation and performance experiments showed that, under the condition of satisfying the allowable amount of positioning error in the horizontal and vertical directions, the horizontal operation speed was 0.2 m·s−1, the maximum positioning error was 2.87 mm, the vertical operation speed was 0.3 m·s−1, and the maximum positioning error was 1.34 mm. Accordingly, the success rate of the transport system was 92.5–96.0%, and the operational efficiency was 176–317 plates/h. These results proved that the transport system could meet the operational requirements and provide feasible solutions for the automation of plant factory transport equipment.