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The Competitive Swarm Optimizer (CSO) has emerged as a prominent technique for solving intricate optimization problems by updating only half of the population in each iteration. Despite its effectiveness, the CSO algorithm often exhibits a slow convergence rate and a tendency to become trapped in local optimal solutions, as is common among metaheuristic algorithms. To address these challenges, this paper proposes a hybrid approach combining the CSO with the Salp Swarm algorithm (SSA), CL-SSA, to increase the convergence rate and enhance search space exploration. The proposed approach involves a two-step process. In the first step, a pairwise competition mechanism is introduced to segregate the solutions into winners and losers. The winning population is updated through strong exploitation using the SSA algorithm. In the second step, non-winning solutions learn from the winners, achieving a balance between exploration and exploitation. The performance of the CL-SSA is evaluated on various benchmark functions, including the CEC2017 benchmark with dimensions 50 and 100, the CEC2008lsgo benchmark with dimensions 200, 500 and 1000, as well as a set of seven well-known constrained design challenges in various engineering domains defined in the CEC2020 conference. The CL-SSA is compared to other metaheuristics and advanced algorithms, and its results are analyzed through statistical tests such as the Friedman and Wilcoxon rank-sum tests. The statistical analysis demonstrates that the CL-SSA algorithm exhibits improved exploitation, exploration, and convergence patterns compared to other algorithms, including SSA and CSO, as well as popular algorithms. Furthermore, the proposed hybrid approach performs better in solving most test functions. [ABSTRACT FROM AUTHOR]

ntroduction-- Robotic rehabilitation devices for motor impairment patients are widely used since they significantly impact the rehabilitation process by offering a uniform exercise plan without typical variations due to different human therapists. These devices require force sensors to monitor the patient recovery evolution. Among the rehabilitation robotic devices is the two degree-of-freedom MIT-MANUS model used in patients with upper limbs motor problems. In order to complement the use of a MIT-MANUS system, it is necessary to equip it with planar sensing forces capability. Unfortunately, commercial devices are very expensive and not very accessible in countries like Colombia. Therefore, the research objective is to develop a force sensor for a robotic MIT-MANUS system that is simple, robust, economical and reliable. Objective-- Develop an in-plane sensing device capable of detecting, with good precision and an acceptable measurement error, the forces exerted by girls and boys up to 10 years of age. Methodology-- A Mechanical Engineering methodological design approach was used, based on the phases of conception, design, CAE validation, construction, assembly and experimental tests. Results-- The research main result was the construction of an experimentally validated sensing device capable of measuring force up to 60 N in the plane. Conclusions-- After carrying out the tests and experimental trials, it was possible to verify that the sensor is reliable when measuring a force in the plane. The maximum measurement error of a vertical or horizontal force component is 10%, which is reduced to 6% when the resultant force value is calculated. The source of the errors is diverse, due to the non-homogeneity of the metallic element, the type of gauge that was used, the chemicals used to weld the gauge to the mechanical element, ADC conversion problems, among others, but their acceptable values allow using of the gauge in the application initially planned. [ABSTRACT FROM AUTHOR]

The present paper proposes self-adaptive multi-population elitist (SAMPE) Rao algorithms and chaotic Rao algorithms for design optimization of selected mechanical engineering components. The proposed algorithms are applied to 25 benchmark problems and 15mechanical engineering design optimization problems to examine their performance. The Friedman rank test is utilized to demonstrate the significance of the proposed algorithms and the algorithms are ranked according to their performance. The results obtained using the proposed algorithms are compared with the results obtained using other advanced optimization algorithms to demonstrate the effectiveness of the proposed algorithms. It is observed that the performance of the proposed algorithms is either better or competitive to the basic Rao algorithms and the other advanced optimization algorithms. [ABSTRACT FROM AUTHOR]

The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve continuous mechanical engineering design problems with a considerable balance of the exploration and exploitation phases as a metaheuristic approach. Still, the DMO is restricted in its exploitation phase, somewhat hindering the algorithm's optimal performance. In this paper, we proposed a new hybrid method called the BDMSAO, which combines the binary variants of the DMO (or BDMO) and simulated annealing (SA) algorithm. In the modelling and implementation of the hybrid BDMSAO algorithm, the BDMO is employed and used as the global search method and the simulated annealing (SA) as the local search component to enhance the limited exploitative mechanism of the BDMO. The new hybrid algorithm was evaluated using eighteen (18) UCI machine learning datasets of low and medium dimensions. The BDMSAO was also tested using three high-dimensional medical datasets to assess its robustness. The results showed the efficacy of the BDMSAO in solving challenging feature selection problems on varying datasets dimensions and its outperformance over ten other methods in the study. Specifically, the BDMSAO achieved an overall result of 61.11% in producing the highest classification accuracy possible and getting 100% accuracy on 9 of 18 datasets. It also yielded the maximum accuracy obtainable on the three high-dimensional datasets utilized while achieving competitive performance regarding the number of features selected. [ABSTRACT FROM AUTHOR]

IFMIF-DONES (International Fusion Materials Irradiation Facility – DEMO Oriented Neutron Source) is a fusion materials testing facility that is currently being designed under the framework of a work package of the EUROfusion Consortium. It will use a 125 mA at 40 MeV deuteron beam to generate a high neutron flux through L i (d , x n) stripping nuclear reactions in a liquid lithium target. The High Energy Beam Transport line (HEBT), the most upstream system of the IFMIF-DONES accelerator, is responsible for the guidance and shaping of the beam towards the target. Additionally, during commissioning periods, the HEBT is also responsible for diverting the beam, through the Beam Dump Transport Line, to the Beam Dump for testing purposes. The HEBT is spread along different rooms and zones: the Accelerator Vault, the Radiation Interface Zone (RIZ), and Target Interface Room (TIR). The engineering design of the HEBT components situated within the TIR and RIZ has been updated to satisfy new requirements, with a focus on ensuring the protecting of the Fast Isolation Valve (FIV) from the backscattered radiation from the target. These modifications include relocating the FIV from the TIR to the RIZ, adjusting the building layout to accommodate the new FIV module, configuring an enclosure cabinet for the RIZ, and adding local shielding to extend the lifetime of the FIV seal actuators. This work describes the current status of these TIR and RIZ engineering design, including radioprotection, commissioning and maintenance plan, beam diagnostics devices, beam dynamics and new remote handling approaches, as well as the layout and integration of the required components along the beamline. The TIR and RIZ are critical areas for IFMIF-DONES , and their design and operation must be compliant with functional, reliability and safety requirements. The updated design addresses potential issues and enhances the facility's overall functionality. • Collimator redesigned with new coolant fluid for the near TS safety measures. • TIR layout shift to two LRU frees space for new elements. • TIR-RIZ wall thickness reduced by one meter for space. • Enclosure cabinet in RIZ stores argon atmosphere for the FIV. • FIV lifespan doubled by implementing local shielding strategies. [ABSTRACT FROM AUTHOR]

To address conflicts arising during the innovative product design process, a dynamic evolution of conflicts and their relationships leads to the emergence of a new set of conflicts termed as "conflict flow." Initially, a product elemental network is created by analyzing the relevance of the improved product. The elemental nodes in this network undergo extensible conduction transformation which helps extract elemental conduction paths and identify conflicts. Subsequently, a scale-free conflict network is constructed using graph theory, and key conflicts are determined and resolved using TRIZ tools. Through iterative iterations, the conflict flows in the improvement design process are discovered and resolved. Furthermore, this research proposes a method for elemental conduction transformation and conflict determination rules based on conduction paths. It also describes the processes involved in constructing both the elemental network and the scale-free conflict network. Ultimately, the study establishes an iterative construction process model for conflict flow networks based on Extensible Conduction Transformation. To demonstrate its effectiveness, a case study is conducted involving the improvement design of an engineering mechanical Lead-acid battery plate testing equipment. [ABSTRACT FROM AUTHOR]

[Display omitted] • Heat sinks based on standard and novel, non-standard morphologies were designed using parametric modelling and computational optimization. • To allow their cross-morphological performance comparison, equity was established between them with regard to (thermal) mass. • The heat sinks were compared in a passive cooling application using numerical simulation and experimental methods. • Some heat sink morphologies performed more efficiently than others in the passive cooling application. • The approach to cross-morphological performance comparison is shown to be viable, despite shortcomings in technologies used. Cross-morphological performance comparisons in mechanical engineering design may well resemble comparisons of apples and oranges. They are meaningful only if pertinent properties shared across compared morphologies (so-called tertia comparationis) are identified, and equity with regard to these properties is established. This article demonstrates such an approach with the use of parametric geometry modeling, optimization, and additive manufacturing, enabling a thermal performance comparison of different heat sink morphologies, using both steady-state numerical simulation and experimental evaluation, by establishing equity across the compared heat sinks in terms of (thermal) mass. Limitations in additive manufacturing precision, as well as inconsistencies between numerical simulation and experimental evaluation results, were encountered in this case study. Likely resulting from the heat sinks' thermal properties on the additive manufacturing process and the simulation environment's disproportionate reliance on convection surface area, these limitations and inconsistencies will likely diminish as additive manufacturing and numerical simulation technologies improve. The cross-morphological comparison approach presented here is shown to be viable in principle and may inform decision-making in applied mechanical engineering design research and practice. [ABSTRACT FROM AUTHOR]

Process modularity describes the extent to which processes can be decomposed into modules to be executed in parallel. So far, research has approached process modularity from a static perspective, not accounting for its temporal evolution. As a result, the understanding of process modularity has been limited to inferences drawn from aggregated analyses that disregard process execution. This article introduces and develops the notion of dynamic process modularity considering the evolving activity network structure as executed by people. Drawing on network science, the article quantifies process modularity over time using archival data from an engineering design process of a biomass power plant. This article shows how studying the temporal evolution of process modularity enables a more complete understanding of activity networks, facilitates the comparison of actual process modularity patterns against formal engineering design stages, and provides data-driven decision-support for process planning and interventions. Finally, managerial recommendations for interface management, resource allocation, and process decomposition are proposed, to help practitioners better to understand and manage dynamic processes. [ABSTRACT FROM AUTHOR]

The distributive power of the arithmetic operators: multiplication, division, addition, and subtraction, gives the arithmetic optimization algorithm (AOA) its unique ability to find the global optimum for optimization problems used to test its performance. Several other mathematical operators exist with the same or better distributive properties, which can be exploited to enhance the performance of the newly proposed AOA. In this paper, we propose an improved version of the AOA called nAOA algorithm, which uses the high-density values that the natural logarithm and exponential operators can generate, to enhance the exploratory ability of the AOA. The addition and subtraction operators carry out the exploitation. The candidate solutions are initialized using the beta distribution, and the random variables and adaptations used in the algorithm have beta distribution. We test the performance of the proposed nAOA with 30 benchmark functions (20 classical and 10 composite test functions) and three engineering design benchmarks. The performance of nAOA is compared with the original AOA and nine other state-of-the-art algorithms. The nAOA shows efficient performance for the benchmark functions and was second only to GWO for the welded beam design (WBD), compression spring design (CSD), and pressure vessel design (PVD). [ABSTRACT FROM AUTHOR]

The preservation of historical documents is a task that requires a multidisciplinary team. Mechanical engineering can make valuable contributions. Historical documents made of paper have unique characteristics that must be considered for their preservation and exhibition. Specially designed encasements have emerged as a solution to meet these requirements. In the present research, a comparative design study was carried out. The study comprises identifying the main functions of the encasements. Subsequently, it is analyzed how the capsules that appear in the literature have solved these functions. With the information obtained, three new encasements were designed for historical documents in Mexico. From the results and design experiences, some insights and design principles were obtained; these can be universally applied. [ABSTRACT FROM AUTHOR]

A practical scale mechanical decladder that can slit spent nuclear fuel rod-cuts (hulls + pellets) of several tens of kg HM/batch is being developed to supply UO2 pellets to a voloxidation process. The mechanical decladder is an apparatus for separating and recovering fuel material and cladding tubes by horizontally slitting the cladding tube of a fuel rod and a defective irradiated fuel rod. In this study, we address the engineering design of the mechanical decladder for the pretesting of rod-cut slitting. To obtain the requirements of the mechanical decladder, we first manufactured a slitter for testing based on the decladding and shearing conditions of hulls and pellets. The performance test of the testing device for decladding was carried out using a 2-CUT blade module and a 3-CUT blade module. We evaluated the decladding methods for the mechanical decladder and selected the 3-CUT blade module based on the results. A buckling measurement instrument was used to perform a buckling verification test according to the length of a rod-cut and to determine decladder dimensions. The optimum decladding rod-cut length for buckling prevention was calculated. Furthermore, we analyzed the decladding mechanism for various slitting methods. Design/fabrication and preliminary tests of the practical scale mechanical decladder were also performed. For this purpose, we constructed the main mechanism by utilizing the SolidWorks modeling and analysis program and fabricated a new mechanical decladder. Based on the derived requirements, a mechanical decladder with three main modules was designed and fabricated for testing. Simulated rod-cuts of zircaloy were also manufactured to test the basic performance of the decladder, and a data acquisition system was constructed using RSC 232 to measure decladding force and velocity. In the basic test, the rod-cut was completely sectioned into three evenly spaced locations by the new mechanical decladder. [ABSTRACT FROM AUTHOR]

In the context of mechanical engineering design, the field of machine learning accelerated topology optimization is dominated by image-based models trained in a supervised manner on datasets with a limited diversity of boundary conditions. State-of-the-art methods show poor generalization capabilities and are strongly coupled to finite element mesh resolution, hindering scalability. In this paper, we leverage the explicit topology parameterization of the moving morphable components (MMC) framework to train a deep learning model that directly generates geometric design variables using a model architecture that is independent of the finite element mesh used for structural analysis. The developed model is trained on a large dataset of boundary conditions. Despite achieving state-of-the-art regression loss, evaluations reveal that direct-design approaches generate topologies with poor mechanical performance. Specifically, the model-generated topologies have, on average, a stiffness 11.48% lower than conventional MMC designs, as evidenced by in-distribution and out-of-distribution test samples. We demonstrate that this is due to the incompatibility between the regression loss function typically used in literature and the topology optimization objective of compliance minimization. To address this issue, we propose a novel acceleration approach that leverages the trained model to generate improved initial designs for conventional optimization. Specifically, the deep learning model is used to generate an initial design, which is then refined by conventional optimization to arrive at a final, optimal design. This approach shows a computation time-saving of 36.84% without sacrificing the final mechanical performance of the optimal topology compared to conventional optimization starting from a uniform initial layout. [ABSTRACT FROM AUTHOR]

Previous research has demonstrated the benefit of higher levels of cognitive style diversity and task-specific self-efficacy beliefs on team performance in complex tasks. This study aims to add to this growing body of literature by exploring the effects of both cognitive diversity and the self-efficacy beliefs of design teams on final design solutions. The final designs produced by 55 student design teams in a junior-level mechanical engineering course were analyzed, and measures of team-level cognitive diversity and task-specific self-efficacy beliefs were collected. Results indicate that higher cognitive diversity and aggregate engineering design self-efficacy levels of design teams significantly impact final design characteristics; aggregate creative self-efficacy had no effect on design characteristics. Highlights • Data from 55 student design teams and final designs compared. • Higher levels of cognitive diversity significantly affect final designs. • Aggregate engineering design self-efficacy significantly affect final designs. • Aggregate creative self-efficacy did not significantly affect final designs. [ABSTRACT FROM AUTHOR]

The Young's Double-Slit Experiment (YDSE) optimizer is a newly developed swarm intelligence algorithm inspired by Young's double-slit experiment that reveals the wave nature of light. Due to its simple structure and efficiency, the researchers employed the algorithm to address real-world optimization problems in multiple domains. However, in large-scale mechanical and engineering applications, it still needs to balance exploration and exploitation effectively and easily fall into local optimum. Therefore, in order to ameliorate these shortcomings, a multi-strategy improved YDSE optimizer named IYDSE is developed in this study. In IYDSE, introducing the Bernoulli map strategy ensures a higher quality initial population in the initialization, thus improving the convergence speed. Then, Levy flight is utilized as a local development engine to avoid local search stagnation of the optimal solution during optimization. In addition, mirror reflection learning is designed to enhance group diversity. Finally, the information entanglement strategy is introduced to enhance the information exchange between light and dark fringes, thus balancing the exploration-development capability. The superiority of the proposed IYDSE is comprehensively demonstrated by comparing it with state-of-the-art algorithms on the CEC2019 and CEC2020 test suites, respectively. Meanwhile, the practicability of IYDSE is also verified by solving five specific engineering design cases and 50 different types of engineering optimization suites. Finally, the IYDSE algorithm is applied to three truss topology optimization problems. The experimental results demonstrate the applicability and development potential of the proposed algorithm in practical engineering applications. Therefore, IYDSE is potentially an effective and competitive algorithm for solving applied mechanics and engineering. [ABSTRACT FROM AUTHOR]

The Monte Carlo method is widely used for the estimation of uncertainties in mechanical engineering design. However, while flexible, this method remains impractical in terms of computational time and scalability. To bypass these limitations, other more efficient approaches such as the Galerkin stochastic finite element method (GSFEM) or the collocation method have been proposed. GSFEM provides accurate output statistics, has the advantage of being sampling independent and can be modular in terms of operations, albeit code intrusive. While linear elasticity has been extensively covered in the literature, the application of GSFEM to nonlinear mechanical behaviour remains relatively unexplored, in part due to the difficulty to capture nonlinear effects with the traditional GSFEM interpolants. To this end, we propose a seamless and efficient modular framework avoiding the need to know a priori the material law. In particular, the method makes use of a wavelet based formulation able to capture simultaneously continuous and discontinuous behaviours, and stochastic operators are proposed to straightforwardly adapt any material model. Finally, the flexibility of this approach is illustrated with two problems: (i) a 3D hyperelastic example with nonlinear behaviour arising from buckling uncertainty, and (ii) the evaluation of the displacement of a point within a structure based on how much of the external accessible structural deformation is known. • A stochastic finite element method framework is presented. • A stochastic algebra library is provided for easy integration in FE code. • Polynomial chaos, Haar and wavelet expansions are considered. • Wavelet expansion is shown to capture mixed smooth/non-smooth problems. [ABSTRACT FROM AUTHOR]

This article - paper presents the design of the main rotor of the helicopter supported by the patent documents, as a very useful tool (instrument) to reach the specific ideas and design in order to find the most economical way for the quality solution for a certain technical problem present in the process of design (construction). A large amo-unt of technical information first appears in patent documents and abo-ut two thirds of this information cannot be found on any other site. [ABSTRACT FROM AUTHOR]

Target design methodologies (DfX) were developed to cope with specific engineering design issues such as cost-effectiveness, manufacturability, assemblability, maintainability, among others. However, DfX methodologies are undergoing the lack of real integration with 3D CAD systems. Their principles are currently applied downstream of the 3D modelling by following the well-known rules available from the literature and engineers' know-how (tacit internal knowledge). This paper provides a method to formalize complex DfX engineering knowledge into explicit knowledge that can be reused for Advanced Engineering Informatics to aid designers and engineers in developing mechanical products. This research work wants to define a general method (ontology) able to couple DfX design guidelines (engineering knowledge) with geometrical product features of a product 3D model (engineering parametric data). A common layer for all DfX methods (horizontal) and dedicated layers for each DfX method (vertical) allow creating the suitable ontology for the systematic collection of the DfX rules considering each target. Moreover, the proposed framework is the first step for developing (future work) a software tool to assist engineers and designers during product development (3D CAD modelling). A design for assembly (DfA) case study shows how to collect assembly rules in the given framework. It demonstrates the applicability of the CAD-integrated DfX system in the mechanical design of a jig-crane. Several benefits are recognized: (i) systematic collection of DfA rules for informatics development, (ii) identification of assembly issues in the product development process, and (iii) reduction of effort and time during the design review. [ABSTRACT FROM AUTHOR]

Biofuels represent an energy option to mitigate polluting gases. However, technical problems must be solved, one of them is to improve the combustion process. In this study, the geometry of a piston head for a diesel engine was redesigned. The objective was to improve the combustion process and reduce polluting emissions using biodiesel blends as the fuel. The methodology used was the mechanical engineering design process. A commercial piston (base piston) was selected as a reference model to assess the piston head's redesign. Changes were applied to the profile of the piston head based on previous research and a new model was obtained. Both models were evaluated and analyzed using the finite element method, where the most relevant physical conditions were temperature and pressure. Numerical simulations in the base piston and the new piston redesign proposal presented similar behaviors and results. However, with the proposed piston, it was possible to reduce the effort and the material. The proposed piston profile presents adequate results and behaviors. In future, we suggest continuing conducting simulations and experimental tests to assess its performance. [ABSTRACT FROM AUTHOR]

A DC circuit breaker is piece of core equipment for DC grid construction and can achieve fast isolation of DC faults in the grid. In this paper, based on the fault characteristics and protection requirements of an AC/DC hybrid distribution network, the technical parameters and topology structure of an inductance and capacitance (LC) resonant commutation-type mechanical DC breaker are proposed. Furthermore, an engineering prototype of the mechanical DC circuit breaker is developed, then a small current breaking test and a large current breaking test are carried out. The test results show that a 10 kA fault current can be interrupted within 10 ms by the engineering prototype. Finally, the engineering prototype of the mechanical DC circuit breaker is installed in the AC/DC hybrid distribution network project, and a current breaking test is carried out. The test results show that the mechanical DC circuit breaker has good running conditions, and the function and performance meet the engineering design and operation requirements. [ABSTRACT FROM AUTHOR]