Mechanical Design Research Articles

Configuration Design and Size Optimization of a High-Precision Novel Parallel Pointing Mechanism Based on Interference Separation

AbstractPointing mechanism is widely used in aerospace field, and its pointing accuracy and stability have high requirements. The pointing mechanism will be affected by external interference when it works. In order to eliminate the impact of interference forces on the output accuracy of the mechanism, firstly, this paper proposes a design method for high-precision pointing mechanisms based on interference separation, aiming at the high-precision pointing requirements of pointing mechanisms. Based on the screw theory, a synthesis method for inner compensation mechanisms has been proposed. And a new type of double-layer parallel mechanism has been designed to compensate for interference forces. Then, the kinematics and dynamics of the mechanism are carried out. An evaluation index for compensating external interference forces is proposed. The interference compensation analysis is conducted for the pointing mechanism. The correctness of the proposed interference force compensation coefficient is verified. Finally, in order to find the optimal solution for the workspace and interference force compensation coefficient of the pointing mechanism, multi-objective optimization design of the structural parameters of the mechanism was carried out based on the particle swarm optimization algorithm. This provides a theoretical basis for the prototype design of the subsequent double-layer parallel mechanism. This double-layer parallel mechanism combines the advantages of large load-bearing capacity, large workspace, and high output accuracy. It can be better applied in the aerospace field where high-precision pointing and force interference compensation are integrated.

Design and performance evaluation of a spiral bar precision weeding mechanism for corn fields.

This paper focuses on addressing the limitations of existing mechanical weeding methods for corn plants by introducing a spiral tendon-type precision weeding device specifically designed for corn fields. The study encompasses mechanical design and theoretical analysis to determine the overall structure, component parts, application scenarios, operation modes, and working principles of the device. The force applied to the spiral tendon weeding cutter head, a crucial working component of the device, is analyzed, along with its motion characteristics. This analysis allows for the calculation of the force required for the spiral tendon to penetrate the soil, as well as the determination of its trajectory and speed in the soil. The desired motion pattern of the weeding cutter head is considered in determining the contour shape of the inner cylinder track and its cooperation mode with the roller follower. Furthermore, the design and optimization of the circulation groove track are conducted to derive the key parameters of the track. A prototype is then constructed based on the proposed structural design and parameter optimization scheme. Soil bin tests are performed to evaluate the device's performance. The optimal combinations of working parameters are determined as follows: a 70-mm depth of the weeding cutter head into the soil, a movement speed of 80mm/s, and an output thrust of the electric actuator of 180N. Under these conditions, the device achieves a weed removal rate exceeding 95% with a 3% wounding rate, demonstrating stable operational performance.

Design and experimental evaluation of a compact inchworm piezoelectric actuator with bridge-type displacement amplification mechanism

Abstract Miniaturization is an important direction in piezoelectric actuator research, with related efforts focusing on reducing structural complexity and simplifying guiding mechanisms. However, some actuators experience backward movement after miniaturization design. There remains significant room for improvement in mechanism configuration and the design of flexible amplification mechanisms to further reduce actuator size. In this paper, a compact inchworm actuator with bridge-type displacement amplification mechanism (BTDAM) is proposed, which does not require an additional guiding mechanism and adopts a three-dimensional packaging approach with high space utilization and a compact amplification mechanism. Because the output capacity of the BTDAM significantly impacts the operating performance of the actuator, mathematical modeling and simulation analysis are conducted. Compared with the experimental results of the amplification ratio of the BTDAM, the error of the theoretical model was 5.05%, and that of the FEM analysis was 9.1%. The dimensions of the prototype are 23 × 23 × 19 mm, and it weighs 23 g. The test results indicate that the proposed actuator can achieve stable stepping motion without backward movement. Under a voltage of 75 V, the proposed actuator achieves a maximum motion speed of 384 μm/s and a maximum output force of 1.7 N. With its compact structure and light weight, the proposed actuator shows promising application prospects in the aerospace and bioengineering fields.

Mechanical Design, Manufacturing, and Testing of a Soft Pneumatic Actuator with a Reconfigurable Modular Reinforcement

Soft actuators have enabled the growth of soft robotics, overcoming several drawbacks of rigid robotics by providing devices with many degrees of freedom and the ability to grasp, bend, move, jump, and more. The reconfiguration of the workspace is still a limitation of these actuators. Indeed, once the actuator is designed and developed, it is used for a specific task. This work presents a reconfigurable soft pneumatic actuator with a novel reconfigurable modular reinforcement. The latter is wrapped around an inner tube in silicone rubber and is made of components whose assembly can be configured based on the task. A formulation is identified by a hybrid approach based on finite element analysis and response surface methodology for predicting and designing the behavior of the actuator. The prototyping revealed the ease of fabrication and reconfigurability as the strength of this new actuator. The experimental tests demonstrated the feasibility of adopting the actuator as a finger in a gripper for handling and moving objects of different shapes, masses, and stiffness. Furthermore, the evaluated performance shows a good trade-off between mass, developed force, implementation time, easy reconfigurability, and cost-effectiveness.

A Conceptual Design of Deployable Antenna Mechanisms

Over the last decade, large-scale antennas have been developed to enhance precise blue force tracking and improve situational awareness. In general, such large-scale antennas, ranging from 1 to up to 10 m, need a specific mechanism that can reconfigure their shapes and morphologies, resulting in stowing and deploying upon the given environment. In parallel, it must be noted that such deployable mechanisms should accommodate a large aperture diameter while ensuring they are lightweight, robust, and structurally rigid to avoid undesired deformations due to the deployment. With these in mind, this work presents a large frustum-shaped deployable antenna mechanism with a large aperture diameter of 7.5 m. The deployable mechanism is composed of hierarchical bayes the radial direction at 30° intervals. Twelve bayes in total identify the overall morphology of the deployable antenna, which features a dodecagon. Specifically, the bay is composed of three linkage structures: a six-bar linkage mechanism, a V-folding mechanism, and a single pantograph mechanism. As a result of static and dynamic simulations, it is identified that the mechanism achieves an area-to-mass ratio of 5.003 m2/kg and a safety factor of 323.8 upon deployment. Conclusively, this work demonstrates a strong potential of the deployable antenna mechanism, providing high rigidity and large aperture diameter while ensuring high stability in space environments.

An Improved Transformer Model for Sap Flow Prediction that Efficiently Utilizes Environmental Information

AbstractAs an important reference for assessing plant water consumption and estimating plant transpiration, it is of great significance to achieve accurate prediction of plant sap flow. A number of deep learning models were established and compared using approximately 3 years of continuous eucalyptus flow time series data collected from the SAPFLUXNET open dataset and 6 environmental factors, including shortwave solar incident radiation, air temperature, air relative humidity, net radiation, vapor pressure deficit, and photosynthetic photon flux density. The experimental results show that the improved Transformer model, with the introduction of a two-step self-attention mechanism and simplified design, maintains significant predictive performance advantages compared to the original Transformer model, long short-term memory, gated recurrent unit, and temporal convolutional neural network models. In the shorter 1-h forecast, the mean squared error and coefficient of determination (R2) of the improved Transformer model are 0.0191 and 0.965, respectively. Compared to the suboptimal typical Transformer model, the MSE is reduced by 22.9%, and R2 is increased by 1.0%. Additionally, the improved model maintains stable predictive performance advantages in long-term plant flow prediction. In the longest 8-h advance prediction, the MSE is reduced by 14.9% compared to the suboptimal Transformer model, and R2 increases by 3.0% compared to the Transformer model. The comprehensive experimental results show that the improved Transformer model makes more effective use of environmental information to achieve more accurate and long-term plant flow prediction. This study emphasizes the basic principle and validity of the two-step self-attention network structure and provides a valuable basis for developing more effective methods for predicting plant sap flow.

Co‐feeding CO2 for Methylfuran Aromatization over Bifunctional Zeolite‐supported ZnMoO4

Aromatization of biofuran offers promising approaches for sustainable biochemical production. However, this process is often hampered by low yields and severe coking on traditional zeolite catalysts. Herein, we report co‐feeding CO2 for 2‐methylfuran (MF) aromatization (CCMA) over bifunctional ZSM‐5 supported ZnMoO4. This bifunctional catalyst can achieve both MF and CO2 conversions of >97%, generating >85% carbon yield of target arenes and CO with negligible alkenes (0.05%). Meanwhile, coke formation is remarkably suppressed from 22.3% to 8.6%. ZnMoO4/ZSM‐5 is capable of selectively manipulating the reaction intermediates and pathways of the CCMA reactions, favoring the cyclopentenones‐ and alkenes‐based dehydro‐aromatization rather than the benzofurans‐based pathway. This finding challenges the prevailing understanding that MF aromatization follows a hydrocarbon pool mechanism. Moreover, the abundant surface oxygen vacancies of ZnMoO4 facilitate the adsorption of CO2 and its subsequent reaction with coke. These insights into reaction mechanism and catalyst design for co‐conversion of CO2 and biofuran can offer guidelines for process intensification in biomass utilization with a carbon‐negative manner.

Fully Integrated Passive Wireless Sensor with Mechanical-Electrical Double-Gradient for Multifunctional Healthcare Monitoring.

Accurate, effective, and continuous monitoring of pressure, moisture, and temperature is essential for routine health assessments and professional patient care. In this study, we present a fully integrated multiparameter passive wireless sensor (MWS) that employs a mechanical-electrical dual-gradient structure design. The unique gradient porous structure endows the MWS with significant advantages in terms of detection dimensions (pressure, moisture, and temperature), sensitivity, and stability. Compared to single mechanical gradient designs, the sensor demonstrates 2.6 times higher pressure sensitivity and a 5-tier moisture detection capability. By bridging the technology gap between high-precision multiparameter sensing, wireless communication, and energy management, the MWS is capable of measuring multiple physiological parameters, including breath, ballistocardiograph, moisture, and temperature at multiple points, providing real-time assessments of the physiological state of the subjects. This work offers valuable quantitative insights for caregivers and paves the way for significant advancements in personal healthcare management.

Co-feeding CO2 for Methylfuran Aromatization over Bifunctional Zeolite-supported ZnMoO4.

Aromatization of biofuran offers promising approaches for sustainable biochemical production. However, this process is often hampered by low yields and severe coking on traditional zeolite catalysts. Herein, we reportco-feeding CO2 for 2-methylfuran (MF) aromatization (CCMA) over bifunctional ZSM-5 supported ZnMoO4. This bifunctional catalyst can achieve both MF and CO2 conversions of >97%, generating >85% carbon yield of target arenes and CO with negligible alkenes (0.05%). Meanwhile, coke formation is remarkably suppressed from 22.3% to 8.6%. ZnMoO4/ZSM-5 is capable of selectively manipulating the reaction intermediates and pathways of the CCMA reactions, favoring the cyclopentenones- and alkenes-based dehydro-aromatization rather than the benzofurans-based pathway. This finding challenges the prevailing understanding that MF aromatization follows a hydrocarbon pool mechanism. Moreover, the abundant surface oxygen vacancies of ZnMoO4 facilitate the adsorption of CO2 and its subsequent reaction with coke. These insights into reaction mechanism and catalyst design for co-conversion of CO2 and biofuran can offer guidelines for process intensification in biomass utilization with a carbon-negative manner.

Medi-Cliq (A Drug Dispenser) - A Survey Paper

In this paper, the concept of automated drug dispensing is summarized, and its significance in the healthcare sector is illustrated. MediCliq is designed to efficiently dispense prescribed medication, is researched in detail, focusing on the integration of mechanical design with IT-based control systems. Key technologies, including prescription scanning, prescription validation, and user interac- tion interfaces, are explored to enable seamless dispensing and user authentication. This paper also conducts a formal review of interdisciplinary methodologies, combining mechanical engineering with IT for developing an effective, secure, and user-friendly automated drug dispensing solution. Keywords: Automated drug dispensing, Prescription scanning, Secure dispensing solution, Prescription validation

Application of Reinforcement Learning in Controlling Quadrotor UAV Flight Actions

Most literature has extensively discussed reinforcement learning (RL) for controlling rotorcraft drones during flight for traversal tasks. However, most studies lack adequate details regarding the design of reward and punishment mechanisms, and there is a limited exploration of the feasibility of applying reinforcement learning in actual flight control following simulation experiments. Consequently, this study focuses on the exploration of reward and punishment design and state input for RL. The simulation environment is constructed using AirSim and Unreal Engine, with onboard camera footage serving as the state input for reinforcement learning. The research investigates three RL algorithms suitable for discrete action training. The Deep Q Network (DQN), Advantage Actor–Critic (A2C), and Proximal Policy Optimization (PPO) were combined with three different reward and punishment design mechanisms for training and testing. The results indicate that employing the PPO algorithm along with a continuous return method as the reward mechanism allows for effective convergence during the training process, achieving a target traversal rate of 71% in the testing environment. Furthermore, this study proposes integrating the YOLOv7-tiny object detection (OD) system to assess the applicability of reinforcement learning in real-world settings. Unifying the state inputs of simulated and OD environments and replacing the original simulated image inputs with a maximum dual-target approach, the experimental simulation achieved a target traversal rate of 52% ultimately. In summary, this research formulates a set of logical frameworks for an RL reward and punishment design deployed with real-time Yolo’s OD implementation synergized as a useful aid for related RL studies.

Research on adversarial attack and defense of large language models

Abstract. Large language models (LLMs) have made excellent progress in text and image understanding and generation. However, with the wide range of applications of these models in various industries, the issue of their security, especially the defense against adversarial attacks, has become a focus of research. This study focuses on exploring the adversarial attacks faced by LLMs and their defense strategies, especially the design and optimization of defense mechanisms. Through literature review and case studies, this paper analyzes in detail the white-box and black-box attack patterns against LLMs, including model inversion, backdoor attacks, and token-based strategies. In response to these attacks, this paper proposes a series of defense strategies, including preventive measures such as data augmentation, adversarial training and model regularization, as well as real-time attack detection and response strategies such as anomaly detection and adversarial sample detection techniques. The core of this research is to improve the robustness and trustworthiness of LLMs, providing the necessary guarantees for their integration and sustainability in multiple industrial applications. In addition, this paper proposes future research directions, highlighting the importance of developing advanced defense systems, promoting interdisciplinary research and exploring new applications for LLMs. This research provides valuable insights into understanding and improving the security defense mechanisms of LLMs, which is essential for maintaining the security and user trust of these models.

Design of a Novel Macro-Micro Integrated Brain Surgery Robot Based on Modular Parallel Mechanisms

The advancement and development of medical surgical robots have provided new technological support for brain surgery and neurosurgical procedures, improving the reliability of highly complex and precise surgeries. In turn, this urges the design and development of novel surgical robots to possess higher precision, stability, and enhanced motion capabilities. In response to this practical demand, this paper introduces a macro-micro integrated medical brain surgery robot system based on the concept of modular PMs (parallel mechanisms), which have a total of 13 active DOFs (degrees of freedom). This system divides the motion process of brain surgery into a large-scale macro-motion space and a small-scale high-precision motion space for design and planning control. The introduction of the design concept that combines multiple modular parallel sub-mechanisms has brought a significant level of decoupling characteristics to the mechanism itself. A comprehensive introduction and analysis of the surgical robot are provided, covering aspects such as design, kinematics, motion planning, and performance indicators. To address the pose allocation and coordination of motion between the macro platform and the micro platform, a pose allocation algorithm based on the decoupling and non-decoupling characteristics in various dimensions of the macro-micro platform is proposed. The designed measurement experiments have demonstrated that the repeatability in positioning accuracy of the macro and micro platform reaches the level of micron and submicron respectively. Practical experiments of motion control and simulated brain electrode implantation validate the excellent performance and stability of the entire surgical robot system. This research contributes innovative insights to the development of medical surgical robot systems, particularly in the domain of mechanism design.

Reconfigurable cable-driven parallel mechanism design: physical constraints and control

Abstract The cable-driven parallel mechanism (CDPM) is known as an interesting application in industry to pick and place objects owing to its advantages such as large workspaces. In addition to the advantages of this mechanism, there are some challenges to improving performance by considering constraints in different components, such as the behavior of cables, shape, size of the end effector and base, and model of pulleys and actuators. Moreover, the impact of online geometry reconfiguration must be analyzed. This paper demonstrates the impact of these constraints on the performance of reconfigurable CDPM. The methodology is based on the systematic review and meta-analysis guidelines to report the results. The databases used to find the papers are extracted from Scopus and Google Scholar, using related keywords. As a result, the impact of physical constraints on system performance is discussed. A total of 90 and 37 articles are selected, respectively. After removing duplicates and unrelated papers, 88 studies that met the inclusion criteria are selected for review. Even when considering the physical constraints in modeling the mechanism, simplifications in designing a model for the reconfigurable CDPM generate errors. There is a gap in designing high-performance controllers to track desired trajectories while reconfiguring the geometry, and the satisfaction of physical constraints needs to be satisfied. In conclusion, this review presents several constraints in designing a controller to track desired trajectories and improve performance in future work. This paper presents an integrated controller architecture that includes physical constraints and predictive control.

Application and Practice of Mathematical Optimization Method in Structural Mechanics Design

Application and Practice of Mathematical Optimization Method in Structural Mechanics Design

Edge Manipulations for the Maximum Vertex-Weighted Bipartite \(b\) -matching

In this paper, we explore the Mechanism Design aspects of the Maximum Vertex-weighted \(b\) -Matching (MVbM) problem on bipartite graphs \((A\cup T,E)\) . The set \(A\) comprises agents, while \(T\) represents tasks. The set \(E\) , which connects \(A\) and \(T\) , is the private information of either agents or tasks. In this framework, we investigate three mechanisms – \(\mathbb{M}_{BFS}\) , \(\mathbb{M}_{DFS}\) , and \(\mathbb{M}_{G}\) . We examine scenarios in which either agents or tasks are strategic and report their adjacent edges to one of the three mechanisms. In both cases, we assume that the strategic entities are bounded by their statements: they can hide edges, but they cannot report edges that do not exist. First, we consider the case in which agents can manipulate. In this framework, \(\mathbb{M}_{BFS}\) and \(\mathbb{M}_{DFS}\) are optimal but not truthful. By characterizing the Nash Equilibria induced by \(\mathbb{M}_{BFS}\) and \(\mathbb{M}_{DFS}\) , we reveal that both mechanisms have a Price of Anarchy ( \(PoA\) ) and Price of Stability ( \(PoS\) ) of \(2\) . These efficiency guarantees are tight; no deterministic mechanism can achieve a lower \(PoA\) or \(PoS\) . In contrast, the third mechanism, \(\mathbb{M}_{G}\) , is not optimal, but truthful and its approximation ratio is \(2\) . We demonstrate that this ratio is optimal; no deterministic and truthful mechanism can outperform it. We then shift our focus to scenarios where tasks can exhibit strategic behaviour. In this case, \(\mathbb{M}_{BFS}\) , \(\mathbb{M}_{DFS}\) , and \(\mathbb{M}_{G}\) all maintain truthfulness, making \(\mathbb{M}_{BFS}\) and \(\mathbb{M}_{DFS}\) truthful and optimal mechanisms. In conclusion, we investigate the manipulability of \(\mathbb{M}_{BFS}\) and \(\mathbb{M}_{DFS}\) through experiments on randomly generated graphs. We observe that (i) \(\mathbb{M}_{BFS}\) is less prone to be manipulated by the first agent than \(\mathbb{M}_{DFS}\) , and (ii) \(\mathbb{M}_{BFS}\) is more manipulable on instances in which the total capacity of the agents is equal to the number of tasks. 1

HAZOP ANALYSIS OF AN ATMOSPHERIC PETROLEUM DISTILLATION UNIT AND SIMILARITY IN SUSTAINABLE AVIATION FUELS (SAFs) PRODUCTION PROCESSES

The production of petroleum derivatives stands out worldwide among the non-renewable energy production processes, as the most well-known worldwide. In this study, an atmospheric distillation column that processed 100,000 barrels/day (662.5 m3/h) of crude oil was simulated using CHEMCAD® software. From this procedure, the Process Flow Diagram (PFD) and the Piping and Instrumentation Diagram (PI&D) for the unit, corresponding to its automation, were obtained. Subsequently, a HAZOP (Hazard and Operability Study) analysis was conducted in order to identify risks in the unit that, although not dangerous, could compromise its ability to achieve its productivity. The main risks of the process were related to fires, explosions and environmental contamination from leaks and ruptures in pipes, pumps, heat exchangers, the column itself, among other equipment. The HAZOP analysis was able, through the use of guide words, to identify possible operational risks arising from deviations in operating intentions, such as the possibility of fires, explosions, environmental contamination and their consequences. It was possible to identify the risks associated with the selection of materials, the mechanical design of the equipment and the specifications of the accessories. Therefore, the study carried out is an important survey for reducing possible failures in the oil industry. And, since this study is an in-depth study on the distillation processes for petroleum-derived products. It is understood that their in-depth study, as a result of the development of new renewable products, especially sustainable aviation fuels (SAFs), because they are similar purification processes, employ the same methodology to identify possible failures in production.

Exploring the evolution and collaboration in two-sided matching: a comprehensive bibliometric and topic modeling analysis

PurposeThis study aims to provide a comprehensive analysis of two-sided matching (TSM) research, an interdisciplinary field that integrates both theoretical and practical perspectives. By examining 756 research articles from the Web of Science database, this paper seeks to identify key trends, collaboration patterns and emerging research topics within the TSM domain.Design/methodology/approachThe research utilizes bibliometric analysis combined with a structural topic model to analyze TSM-related articles published between January 1, 2000, and September 30, 2022. The study identifies leading subfields, journals, countries/regions and institutions based on publication volume, total citations and average citations per article. Interaction and collaboration patterns among these entities are examined through co-occurrence and coupling networks. Additionally, five major research topics are identified and explored using topic modeling and co-word networks. This hybrid knowledge mining approach better reveals the inherent structural changes in topic clusters. Topic distribution and network analysis are beneficial in capturing the attention allocation of different entities to knowledge.FindingsThe analysis reveals five prominent research topics in TSM: communication resource allocation, stable matching research, computing task assignment, TSM decision-making and market matching mechanism design. These topics represent the main directions of TSM research. The study also uncovers a shift in research focus from theoretical aspects to practical applications. Furthermore, the distribution of knowledge and interaction patterns among key entities align with the identified research trends.Originality/valueThis study offers a novel and detailed overview of TSM research highlighting significant trends and collaboration patterns within the field. By integrating bibliometric methods with structural topic modeling the study provides unique insights into the evolution of TSM research making it a valuable resource for both academic and professional communities.

Reliability Analysis of Complex Structures Under Multi-Failure Mode Utilizing an Adaptive AdaBoost Algorithm

A reliability analysis can become intricate when addressing issues related to nonlinear implicit models of complex structures. To improve the accuracy and efficiency of such reliability analyses, this paper presents a surrogate model based on an adaptive AdaBoost algorithm. This model employs an adaptive method to determine the optimal training sample set, ensuring it is as evenly distributed as possible on both sides of the failure curve and fully contains the information it represents. Subsequently, with the integration and iterative characteristics of the AdaBoost algorithm, a simple binary classifier is iteratively applied to build a high-precision alternative model for complex structural fault diagnosis to cope with multiple failure modes. Then, the Monte Carlo simulation technique is employed to meticulously assess the failure probability. The accuracy and stability of the proposed method’s iterative convergence process are validated through three numerical examples. The findings of the study illuminate that the proposed method is not only remarkably precise but also exceptionally efficient, capable of addressing the challenges related to the reliability evaluation of complex structures under multi-failure mode. The method proposed in this paper enhances the application of mechanical structures and facilitates the utilization of complex mechanical designs.

Adaptive fault-tolerant containment control for heterogeneous uncertain nonlinear multi-agent systems under actuator faults

As modern control systems involve a growing number of actuators, the consequent number of faults occurrence, which may deteriorate the closed-loop system performance, is increasing. Focusing on the containment control problem of generic nonlinear high-order heterogeneous uncertain Multi-Agent Systems, this study proposes a novel distributed adaptive fault-tolerant Proportional-Integral-Derivative control protocol to improve the reliability and the resilience of the whole networked systems against actuator failures. The Lyapunov theory, combined with the Barbalat lemma, provides the design of suitable adaptive mechanisms that adjust the PID control actions to ensure the asymptotic convergence of the closed-loop containment errors for the overall network, hence implying the convergence of each agent state toward the convex region shaped by the multiple leaders, despite the occurrence of actuators faults. The fulfillment of the fault-tolerant containment objective is ensured by requiring a lower computational burden with respect to alternative solutions, hence resulting in more attractive for wider practical engineering applications. Numerical simulation results, also including a comparison analysis with a state-of-the-art controller, corroborate the efficiency and the advantages of the proposed distributed PID controller.