Mechanical Design Optimization Research Articles

Enhanced artificial hummingbird algorithm with chaotic traversal flight

Tackling the shortcomings of slow convergence, imprecision, and entrapment in local optima inherent in traditional meta-heuristic algorithms, this study presents the enhanced artificial hummingbird algorithm with chaotic traversal flight (CEAHA), which employs chaotic ergodicity within the foundational framework of the conventional artificial hummingbird algorithm. This approach implements chaotic motion within local regions of the solution space, ensuring a thorough exploration of potential optima and preventing algorithmic stagnation at local maxima by guaranteeing a non-repetitive traversal of all search states. This study also analyzes the intrinsic mechanisms by which eight different chaotic mappings affect optimization performance, from the perspectives of invariant measures and traversal efficiency of ergodic chaotic motion. In comparative tests with 21 meta-heuristic algorithms on the CEC2014, CEC2019, and CEC2022 benchmark suites across various dimensions, CEAHA demonstrates superior optimization performance. Furthermore, the practicability and robustness of CEAHA have been confirmed in mechanical design optimization problems through 4 engineering instances: pressure vessel, gear trains, speed reducers, and piston levers.

A holistic study of the effect of geometrical and processing conditions on the static mechanical performance of LPBF strut elements

Laser powder bed fusion (LPBF) enables geometrical designs of great complexity, such as metamaterials. These structures are founded on elemental struts printed at various orientations and sizes. Understanding how these design variables affect mechanical properties is crucial for optimizing component performance. This work aims to systematically investigate the impact between these design variables on defects, roughness, geometrical deviations, and microstructure of Ti-6Al-4V elemental struts and correlate them with mechanical properties. The analysis shows that smaller strut diameters present an increased sensitivity to defects, reducing ductility by 45.8% on average as the diameter decreases from 1.5 mm to 0.5 mm. When compared to vertical struts, horizontally printed struts of 1.5 mm, 1 mm, and 0.5 mm present on average a respective reduction in ductility of 57.4%, 59.8%, and 70.9%, and a respective reduction in the ultimate strength of 13.3%, 24.5%, 61.2%. This has been associated with warping and increased roughness caused by dross formation. Finally, the study shows the complex interaction of process parameters' effect with the struts' orientation and size. These findings pose the basis for a more accurate and optimal mechanical design of cellular metamaterials, from the underlying material perspective.

CFD-DEM simulation and experimental investigations on continuous hydraulic sampling of deep-sea polymetallic nodules

The physical sampling to grasp the distribution of deep-sea polymetallic nodules is essential for efficient mining. A hydraulic sampling scheme is proposed that balances low environmental disturbance, high sampling precision, broad coverage, and statistical data viability to address the limitations of existing methods. This paper utilizes a coupled approach of Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) to explore innovative solutions for precise and continuous hydraulic sampling of deep-sea polymetallic nodules. Focusing on the optimization of the double-row nozzle sampling mechanism design, simulations are conducted to analyze the dynamic characteristics of solid-liquid two-phase flow during the hydraulic sampling process under various nozzle configurations and operational water pressures of the pump. Laboratory experiments with the hydraulic sampling mechanism validate the accuracy of the simulation and the effectiveness of the optimization suggestions. The configuration is finalized with a front nozzle diameter of 10 × φ9 mm, a rear nozzle diameter of 10 × φ7 mm, and an initial working water pressure for the pump of 80 kPa. Subsequently, integrated driving-sampling experiments are conducted using a self-propelled tracked vehicle, validating the feasibility of the proposed configuration.

A parallel mechanism-based virtual biomechanical shoulder robot model: Mechanism design optimization and motion planning

This paper presents the design and optimization process of a Virtual Biomechanical Shoulder Robot Model (VBSRM) based on a 6-4 parallel mechanism. To address the challenges posed by parallel manipulators and the specific biomechanical constraints of the shoulder joint, a comprehensive design analysis is conducted. First, a kinematic model of the VBSRM is developed, followed by an investigation of its geometric model. To obtain an optimal VBSRM design, performance objectives such as condition number, norm of actuator force, and stiffness are identified and optimized. Initially, only condition number of the robot mechanism is optimized using Genetic Algorithm and performance objectives from the optimal design are analyzed. Later, the three objectives are grouped to form a single function and a single objective-based optimization is also conducted. However, further investigation revealed the conflicting nature of the objectives and hence these were simultaneously optimized using the Non-dominated Sorting Genetic Algorithm (NSGA II). The results obtained from various optimization routines are compared and it is found that the results from the NSGA II provide a better tradeoff between the performance objectives. The motion trajectories from the optimal design of the VBSRM are later analyzed vis-à-vis human shoulder motions for its intended use as a robotic model of the human shoulder joint in various applications.

Optimal design of a hybrid compliant mechanism considering the dynamic behavior of membrane-loaded

Optimal design of a hybrid compliant mechanism considering the dynamic behavior of membrane-loaded

Physics-informed and data-driven hybrid method for transmission accuracy design optimization of planetary roller screw mechanism

The planetary roller screw mechanism (PRSM) faces an ever-increasing precision transmission demand in current advanced equipment. The relationship between machining errors and transmission accuracy remains elusive due to the over-simplified physical models and small-sample experimental datasets. This work proposes a physics-informed and data-driven hybrid strategy for PRSM transmission accuracy evaluation and tolerance optimization. In the physical model, a PRSM transmission accuracy model is developed to calculate transmission error that considers 16 machining errors in eccentric, nominal diameter, pitch, flank angle, and roller consistency. In the dataset establishment, thread profile measurements and dynamic leadscrew inspections are conducted for the machining error and transmission accuracy data acquisition. A data augmentation approach combining the physical model with the generative adversarial network is utilized to predict travel deviation, variations, and axial backlash and estimate machining error contribution with the small-sample experimental dataset. It is firstly found that the roller consistency of nominal diameter significantly affects PRSM travel variation V2π with a 17.3 % importance value. With the developed framework, the key tolerances for screw, roller, nut, and roller consistency are optimized toward a typical precision transmission requirement using the non-dominated sorting genetic algorithm. It also provides a tolerance grade recommendation table with PRSM transmission accuracy level in engineering practice.

Modular design and structural optimization of CubeSat separation mechanism

In order to achieve the design requirements of light weight and high stiffness of the CubeSat modular separation mechanism, a BPNN-GA-PSO size optimization method is proposed to optimize the design of the separation mechanism. Firstly, the separation mechanism of modularization and standardization is analyzed to select the optimization objects. Then, a hierarchical optimization strategy of topology optimization followed by size optimization is adopted to find the global optimum using a hybrid GA-PSO optimization algorithm. Meanwhile, the BPNN surrogate model is introduced to improve the optimization efficiency. The results show that the mass proportion of the optimized separation mechanism is reduced to 18 %, and the maximum deformation of the separation mechanism is 0.123 mm, which meets the design requirements of light weight and high stiffness of the separation mechanism. It proves the applicability of the optimization method to the optimal design of the separation mechanism. The CubeSat modular separation mechanism designed in this paper has been verified by ground verification with overload, vibration, and shock mechanical tests, and successfully deployed the BY-03 satellite in-orbit, which can provide reference for the design and development of subsequent CubeSat modular separation mechanisms.

Design and parameter optimization of solid-surface antenna parallel mechanism constructed by multi-loop spatial coupling units

The optimization of design and structural parameters of solid-surface antennas is crucial for the development of aerospace satellites. In this paper, the study explores an innovative parallel solid-surface antenna mechanism, inspired by the daisy bloom pattern, which realizes the characteristics of Fewer input-More output (Fi-Mo). A single-DOF deployable/foldable spatial multi-loop coupled mechanism model of the bionic daisy solid-surface antenna is established based on the single-axis tilting hinge, RSSR mechanism, and Bennett mechanism. The motion characteristics of the antenna mechanism are analyzed using the topology, position and orientation characteristic (POC) and inverse screw theory. The error transfer law of multi-loop mechanism is derived by the low-layer-loop sequence. The optimal configuration parameter set of the antenna deployment mechanism is determined using the NSGA-II genetic algorithm. Finally, the optimized antenna mechanism is subjected to physical simulation analysis to verify its stability and the accuracy of its deployment path.

Enhanced Displacement Magnification in Symmetrical Differential Levers: A Compliant Mechanism Design Optimization Study

Enhanced Displacement Magnification in Symmetrical Differential Levers: A Compliant Mechanism Design Optimization Study

Optimization Design of Redundant Parallel Posture Adjustment Mechanism for Solar Wing Docking Based on Response Surface Methodology

The parallel mechanism exhibits high stiffness and excellent dynamic response, making it ideal for high-precision applications. In our early work, a novel 6-DOF redundant parallel posture mechanism with four limbs for solar wing docking has been proposed; each limb consists of three links and four joints. This paper primarily focuses on optimization design of the mechanism. The calculation of workspace volume reveals that factors influencing the range of posture adjustment include dynamic platform parameters, static platform parameters, the drive trajectory of each kinematic pair, and the angles between each kinematic pair. A sensitivity analysis was conducted to examine the impact of each parameter on the range of posture adjustment. To reduce computational complexity and improve analysis efficiency, a combined approach of single-factor analysis and response surface methodology (RSM) is used in the paper. Single-factor analysis is utilized to evaluate the effect of each parameter on the posture adjustment range. Based on these results, RSM is used to establish a regression model for parameters; thereby, the optimal parameter combination for the mechanism is determined. The regression coefficient R2 = 0.9374 attests to the validity of the proposed model. Finally, a comparison of the posture adjustment range before and after optimization is presented, providing a foundation for the practical application of the redundant parallel mechanism. This paper introduces a novel structural design concept aimed at resolving the conflict between heavy loads and compact sizes in redundant parallel mechanisms while providing valuable insights for miniaturized design.

Design and application of a high-precision counterweighted self-calibrating surface thermometer.

In this study, a high-precision counterweight self-calibrating surface thermometer is designed to reduce human and environmental influences on a thermocouple surface thermometer during measuring. A self-weighted spring structure based on a copper substrate is designed to ensure perfect contact between the surface thermometer and the temperature source. In conjunction, a wind guard is coupled with insulating materials to optimize the thermal exchange of the surface thermometer. Subsequently, the maximum error is reduced to ±1.5 °C by system hardware optimization. However, hardware calibration alone is insufficient. Furthermore, a back propagation neural network is employed to calibrate the surface thermometer. Temperature sensor data are collected under various surface source temperatures and airflow velocities to train the neural network. Hence, the effectiveness of the proposed Gaussian function in enhancing the measurement accuracy of the surface temperature sensor is demonstrated. The results show higher stability and repeatability in temperature measurement than thermocouple-based surface thermometers. The proposed thermometer exhibits robustness against environmental and operational variability with a maximum indication error of -0.2 °C. In contrast, the maximum error of the surface thermometer is between -2.8 and -6.8 °C. Regarding repeatability, the standard deviation with the proposed device is 0.2%, highlighting its accuracy and consistency of performance. These results can mostly be attributed to the synergistic effect of clever mechanical design and software optimization, resulting in a surface thermometer with outstanding accuracy and repeatability.

Mechanical Properties-Based Reliability Optimization Design of GFRP Culvert

Mechanical Properties-Based Reliability Optimization Design of GFRP Culvert

Advancements and Current Developments in Integrated System Architectures of Lithium-Ion Batteries for Electric Mobility

Recognizing the challenges faced by power lithium-ion batteries (LIBs), the concept of integrated battery systems emerges as a promising avenue. This offers the potential for higher energy densities and assuaging concerns surrounding electric vehicle range anxiety. Moreover, mechanical design optimization, though previously overlooked, is gaining traction among researchers as a viable alternative to achieve enhanced energy and power densities. This review paper provides a comprehensive overview of recent research and progress in this domain, emphasizing the significance of battery architectures in enabling the widespread adoption of electric mobility. Beginning with an exploration of fundamental principles underlying LIB systems, the paper discusses various architectures involving different cell form factors, like pouch cells, cylindrical cells, and prismatic cells, along with their advantages and limitations. Furthermore, it reviews recent research trends, highlighting innovations aimed at enhancing battery performance, energy density, and safety through advanced battery system architecture. Through case studies and discussions on challenges and future directions, the paper underscores the critical role of advanced battery system architecture in driving the evolution of e-mobility and shaping the sustainable transportation landscape.

A Hindmarsh–Rose neuron model with electromagnetic radiation control for the mechanical optimization design

This paper proposes a Hindmarsh–Rose(HR) neuron model controlled by electromagnetic radiation to solve the mechanical optimization design problem. A novel memristor for simulating electromagnetic radiation is established to control the dynamical behavior of HR neuron models. By analyzing the HR model and using the numerical analysis methods of phase portrait, bifurcation diagram, Lyapunov exponent and spectral entropy (SE) complexity, the HR model is shown to have a hidden attractor and rich chaotic dynamics. Then, a chaotic optimization algorithm based on HR neuron model is designed to solve the mechanical optimization design of tension/compression coil springs and pressure vessels. The chaotic sequence of this algorithm which has high-quality pseudo-random number, has stronger global search ability. Simulation of two mechanical optimization design problems in comparison with some well-known optimization algorithms demonstrates the superiority of the HR in the solution quality.

Optimal Mechanism Design with Referral

This paper establishes the optimal selling mechanism when a seller can incentivize an existing buyer to refer his privately known potential buyer to participate. We identify three optimal channels for providing referral incentives. First, if the existing buyer declares that no potential buyer exists, his virtual value is penalized. Second, if the existing buyer refers the potential buyer to the seller, his virtual value is boosted. Third, in some scenarios where this carrots-and-sticks-via-virtual-value approach is insufficient for creating proper referral incentives, the existing buyer is then given a constant referral bonus for referring the potential buyer. We also provide conditions under which the optimal mechanism can be implemented using simple mechanisms. Finally, we demonstrate that the conventional resale mechanism is suboptimal. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Funding: H. Zhou’s research is supported by the Chenguang Program of Shanghai Education Development Foundation and Shanghai Municipal Education Commission [Grant 22CGA77]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.01540 .

Mechanism design optimization through CAD-based Bayesian optimization and quantified constraints

This study addresses the challenge of mechanism design optimization, particularly focusing on the energy efficiency and design space of reciprocating mechanisms. The research question centers on how to effectively utilize Computer-Aided Design (CAD) simulations alongside Bayesian optimization (BO) and a constrained design space to streamline the design optimization process, overcoming the limitations of traditional kinematic and dynamic analysis methods. The objective is to investigate and develop a novel optimization framework that integrates CAD-based simulations with a BO approach. To achieve this, the study employs a methodological approach. At first, the feasibility of a chosen mechanism design is evaluated through a sequence of CAD-motion simulations to quantify the (in)feasibility of this design. When this design appears to be feasible, a CAD-based design evaluation method is started, in which the objective value is extracted by a sequence of CAD-motion simulations. In this paper, we advocate the use of non-parametric Gaussian processes to build a surrogate model of the objective function and the feasible design space constrained by static and dynamic constraints. The main research results demonstrate that the proposed CAD-based Bayesian optimization framework can effectively identify optimal design parameters that minimize the root mean square (RMS) torque while adhering to specified static and dynamic constraints. This optimization approach significantly reduces the complexity associated with analytic methods, making it scalable to more complex mechanisms and implementable by machine builders. In conclusion, the study successfully develops a novel optimization framework that leverages CAD-based simulations and Bayesian optimization to streamline the design process of mechanisms. The results of an emergency ventilator case study with three design parameters show a reduction of the RMS torque with 71% after 255 CAD-based design evaluations. Moreover, the results demonstrate the effectiveness of incorporating constraints into the design optimization process and the usage of Bayesian optimization (BO), providing insights into the obtained optimum. This approach offers probabilistic insights into the expected improvement of the optimum, highlighting that the full optimization potential is utilized in a computationally efficient manner.

Towards Design Optimization of Compliant Mechanisms: A Hybrid Pseudo-Rigid-Body Model-Finite Element Method Approach and an Accurate Empirical Compliance Equation for Circular Flexure Hinges.

Innovative designs such as morphing wings and terrain adaptive landing systems are examples of biomimicry and innovations inspired by nature, which are actively being investigated by aerospace designers. Morphing wing designs based on Variable Geometry Truss Manipulators (VGTMs) and articulated helicopter robotic landing gear (RLG) have drawn a great deal of attention from industry. Compliant mechanisms have become increasingly popular due to their advantages over conventional rigid-body systems, and the research team led by the second author at Toronto Metropolitan University (TMU) has set their long-term goal to be exploiting these systems in the above aerospace applications. To gain a deeper insight into the design and optimization of compliant mechanisms and their potential application as alternatives to VGTM and RLG systems, this study conducted a thorough analysis of the design of flexible hinges, and single-, four-, and multi-bar configurations as a part of more complex, flexible mechanisms. The investigation highlighted the flexibility and compliance of mechanisms incorporating circular flexure hinges (CFHs), showcasing their capacity to withstand forces and moments. Despite a discrepancy between the results obtained from previously published Pseudo-Rigid-Body Model (PRBM) equations and FEM-based analyses, the mechanisms exhibited predictable linear behavior and acceptable fatigue testing results, affirming their suitability for diverse applications. While including additional linkages perpendicular to the applied force direction in a compliant mechanism with N vertical linkages led to improved factors of safety, the associated increase in system weight necessitates careful consideration. It is shown herein that, in this case, adding one vertical bar increased the safety factor by 100N percent. The present study also addressed solutions for the precise modeling of CFHs through the derivation of an empirical polynomial torsional stiffness/compliance equation related to geometric dimensions and material properties. The effectiveness of the presented empirical polynomial compliance equation was validated against FEA results, revealing a generally accurate prediction with an average error of 1.74%. It is expected that the present investigation will open new avenues to higher precision in the design of CFHs, ensuring reliability and efficiency in various practical applications, and enhancing the optimization design of compliant mechanisms comprised of such hinges. A specific focus was put on ABS plastic and aluminum alloy 7075, as they are the materials of choice for non-load-bearing and load-bearing structural components, respectively.

Performance of Modified Polymer as a Binder with Qarry Dust and Ballast in the Production of Paving Blocks

The city of Nairobi is in need of pedestrian and cyclist infrastructure as an answer to an increasing reliance on automobiles. Collaborative efforts are underway to reduce congestion, although challenges such as sand mining persist. The utilization of plastic waste and Quarry Dust (QD) may provide a sustainable solution. The proposed study aims to assess the mechanical properties and optimal blend design for the production of paving blocks using a modified polymer and a combination of ballast (B) and QD. The gradation curve constituted a pivotal stage in the research process, as it provided vital information regarding the ratio of B and QD required for the optimal mix design. The study methodically explored the optimal ratio of materials for the production of paving blocks incorporating a modified polymer, QD, and B. It was observed that there were minor reductions in strength under acidic conditions, which suggests that the proposed paving blocks may be suitable for use in a variety of outdoor applications. Additionally, it would be advantageous to investigate innovative solutions for Non-Motorized Transport (NMT) infrastructure.

Reliability Optimization Design of Constrained Metamorphic Mechanism Based on the Augmented Assur Groups

Reliability Optimization Design of Constrained Metamorphic Mechanism Based on the Augmented Assur Groups

Cellular gradient algorithm for solving complex mechanical optimization design problems

In mechanical optimization design problems, there are often some non-continuous or non-differentiable objective functions. For these non-continuous and non-differentiable optimization objectives, it is often difficult for existing optimal design algorithms to find the desired optimal solutions. In this paper, we incorporate the idea of gradient descent into cellular automata and propose a Cellular Gradient (CG) method. First, we have given the basic rules and algorithmic framework of CG and designed three kinds of growth and extinction rules respectively. Then, the three evolutionary rules for cellular within a single cycle are analyzed separately for form and ordering. The best expressions for the cellular jealous neighbor rule and the solitary regeneration rule are given, and the most appropriate order in which the rules are run is selected. Finally, the solution results of the cellular gradient algorithm and other classical optimization design algorithms are compared with a multi-objective multi-parameter mechanical optimization design problem as an example. The computational results show that the cellular gradient algorithm has an advantage over other algorithms in solving global and dynamic mechanical optimal design problems. The novelty of CG is to provide a new way of thinking for solving optimization problems with global discontinuities.