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Engineering design often involves representation in at least two levels of abstraction: the system-level, represented by lumped parameter models (LPMs), and the geometric-level, represented by distributed parameter models (DPMs). Functional design innovation commonly occurs at the system-level, followed by a geometric-level realization of functional LPM components. However, comparing these two levels in terms of behavioral outcomes can be challenging and time-consuming, leading to delays in design translations between system and mechanical engineers. In this paper, we propose a simulation-free scheme that compares LPMs and spatially discretized DPMs based on their model specifications and behaviors of interest, regardless of modeling languages and numerical methods. We adopt a model order reduction (MOR) technique that a priori guarantees accuracy, stability, and convergence to improve the computational efficiency of large-scale models. Our approach is demonstrated through the model consistency analysis of several mechanical designs, showing its validity, efficiency, and generality. Our method provides a systematic way to compare system-level and geometric-level designs, improving reliability and facilitating design translation. [ABSTRACT FROM AUTHOR]

Design processes can be decomposed into several steps, including information acquisition and application. Retrieving prior knowledge and experience is an important method of acquiring design information, thus designers' precedents retrieval styles reflect their design thinking. The obvious difference between industrial (ID) and mechanical design (MD) education is well known: ID education focuses on cultivating design thinking abilities, while MD education focuses on cultivating technical abilities. The background discipline influences design thinking methods. This study aims at exploring the similarities and differences between ID and MD students' precedents retrieval styles in the design process. Forty-eight junior and senior undergraduate students participated in a brainstorming experiment using the think-aloud method. Participants' prior knowledge and experience retrieved during brainstorming were coded and the frequency ratios were analyzed. We conclude that ID students are more inclined to extract and apply episodic precedents than MD students. Compared with semantic precedents, episodic precedents have a greater possibility for being applied to the final solution. [ABSTRACT FROM AUTHOR]

As computer-aided design (CAD) tools have become an essential aspect of modern mechanical engineering design, the demand for CAD experts has increased significantly. The development from novice, to proficient, to expert user is of particular interest to the industrial and academic design communities. Yet little is known about the development of modeling choices, strategies, and patterns that characterize expert CAD skills; much of the past work that reports user action data is based on student or novice data. We compared the CAD modeling process across nine proficient and ten expert designers as they were tested to complete the same design task. Under identical conditions-the same time constraints in the same CAD platform and with the same task-the expert users were able to complete a larger proportion of the task with higher dimensional accuracy. While the experts were able to dissect and retrieve geometries from manufacturing drawings more efficiently than proficient users, they were also able to plan a modeling strategy that required less effort and revisions. With our experimental findings, we identify the demand for procedural knowledge-building for young engineers, with the ultimate goal of more effectively developing experts in engineering design with CAD. [ABSTRACT FROM AUTHOR]

The process of opportunity recognition is pivotal to engineering design, fostering technological innovation and entrepreneurship. Although there are multiple studies conducted in the area of opportunity recognition studying the behaviors of entrepreneurs, examining engineering students' approaches have been underexplored. There is a lack of research in regards to opportunity recognition processes among engineering students, particularly in the context of design education. The presented study addresses this gap by qualitatively examining approaches to opportunity recognition among mechanical engineering students enrolled in an engineering design course. Using a sample of 23 mechanical engineering students, the approaches to opportunity recognition were analyzed using students' responses to open-ended survey questions and student group discussions. From the analysis of the qualitative data, four key themes emerged regarding the improvement of the device and process, user consideration, environmental sustainability, and information and resources accessed by students. The findings provide a deeper understanding that can be used to inform educational practices and further research in the area of opportunity recognition in engineering education settings. [ABSTRACT FROM AUTHOR]

Hydrogels are porous biomaterials widely used in tissue engineering (TE), but they require a mechanism to enhance their mechanical properties. Electrospun nanofibrous yarn (EN yarn) can be a suitable candidate for reinforcing hydrogels. In this study, aiming to improve mechanical performance, hydrogel-based scaffolds reinforced by EN yarns (yarn reinforced hydrogel) were fabricated. The morphological characterization, swelling measurement, and tensile tests were performed to evaluate the physical, mechanical, and biological function of the designed yarn reinforced hydrogel. The measured mechanical properties were used as inputs in the mathematical model of the modified rule of mixtures to predict the mechanical behavior of different yarn reinforced hydrogels, which could be used as scaffolds for different tissues. Cell-scaffold interactions were also evaluated by in vitro assays. This study indicated that adding EN yarns to the neat hydrogel decreased the porosity and swelling, while the tensile strength and Young's modulus were improved. In vitro assays showed that cells had better adhesion and spread on the surface of the yarn reinforced hydrogel than those on the surface of the neat hydrogel. Regarding the improvement in mechanical and cellular properties, the yarn reinforced hydrogel has the potential to be used as a novel scaffold in TE. [ABSTRACT FROM AUTHOR]

As the most prestigious award in the field of machine design, the ASME Machine Design Award relishes its prestige and prominence. In the past 65 years since its inception in 1958, 60 eminent scholars have received this honor. This paper reviews the history of this prestigious award, including a summary of the contributions to the field of all 60 recipients, and acts as an archival document. Reviewing these awards in the past 65 years by following the tracks of its recipients elucidates developments in the field and significant contributions to the advancement of knowledge in machine design. In particular, novel contributions and remarkable achievements in the fields of mechanical engineering and machine design and development are recorded. This should be of interest to those interested in the study and in the historical development of mechanical engineering, particularly in machine design. [ABSTRACT FROM AUTHOR]

Flexible reinforcements are a promising option in slope engineering owing to their mechanical strength and ecological restoration. An in-depth understanding of the mechanical behavior of a slope reinforced by an anchored geomat system is required to analyze the slope stability and to optimize engineering design. In this study, physical modeling experiments are conducted to investigate the global performance and ultimate capacity of the reinforced slope under different loading levels. A stability model of the reinforced slope and the corresponding computing method are proposed based on a limit equilibrium framework in integral form. Subsequently, the contribution of a flexible protection system and its components to safety factors are quantified against loading tests. The results show that lateral deformation is concentrated in the middle of the reinforced slope, from which the potential failure surface can be derived. The stability enhancement of the reinforced slope is primarily attributed to the anchor bolts, and an increase in cohesion and internal friction angle increases the safety factor more significantly under low-level overloads. This study provides a benchmark for slopes reinforced by an anchored geomat system, which is promising for application in slope engineering. [ABSTRACT FROM AUTHOR]

This paper describes goals, process, and results in the effort to introduce new curricula at The Faculty of Technical Sciences of University Mother Theresa - Skopje fully devoted to teach students in the direction of sustainable development. The idea was born in the process of reaccreditation of the old curricula named "Mechanical Engineering and management" at The Faculty of Technical Sciences. The authors of this paper, and the idea for reconstruction were fully aware of the direction Mechanical Engineering needs to go to contribute to the sustainable development of the country and wider. Therefore, that idea led the developing process into new curricula titled: "Sustainable Design in Mechanical Engineering". From total of 40 courses, 15-20 are fully related to sustainable development goals and the curricula includes also a number of projects and diploma work by which students will additionally have an opportunity to add to their understanding and knowledge of sustainable development tools. The new curricula successfully passed the process of accreditation and it is now a part of this University offer. Based on this curricula, a number of courses for lifelong learning will be developed. It is expected that this curricula will attract attention both of the new students, but also of the professionals at different companies, state and municipality institutions. [ABSTRACT FROM AUTHOR]

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]

This study proposes a novel population-based metaheuristic algorithm called the Gazelle Optimization Algorithm (GOA), inspired by the gazelles' survival ability in their predator-dominated environment. Every day, the gazelle knows that if it does not outrun and outmaneuver its predators, it becomes meat for the day, and to survive, the gazelles have to escape from their predators consistently. This information is vital to proposing a new metaheuristic algorithm that uses the gazelle's survival abilities to solve real-world optimization problems. The exploitation phase of the algorithm simulates the gazelles grazing peacefully in the absence of the predator or while the predator is stalking it. The GOA goes into the exploration phase once a predator is spotted. The exploration phase consists of the gazelle outrunning and outmaneuvering the predator to a safe haven. These two phases are iteratively repeated, subject to the termination criteria, and finding optimal solutions to the optimization problems. The robustness and efficiency of the developed algorithm as an optimization tool were tested using benchmark optimization test functions and selected engineering design problems (fifteen classical, ten composited functions, and four mechanical engineering design problems). The results of the GOA are compared with nine other state-of-the-art algorithms. The simulation results obtained confirm the superiority and competitiveness of the GOA algorithm over nine state-of-the-art algorithms available in the literature. Also, the standard statistical analysis test carried out on the results further confirmed the ability of GOA to find solutions to the selected optimization problems. It also showed that GOA performed better or, in some cases, was very competitive with some state-of-the-art algorithms. Also, the results show that GOA is a potent tool for optimization that can be adapted to solve problems in different optimization domains. [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]

Lower costs and higher employee satisfaction are some of the benefits driving organizations to adopt dispersed and virtual working arrangements. Despite these advantages, product design engineering teams—those who develop physical products—have not widely adopted this working style due to perceived critical dependence on physical facilities and the belief that it is ineffective to communicate technical details virtually. This paper uses the mass shift in working conditions caused by the COVID-19 pandemic to explore the feasibility of virtual and distributed work in product design engineering. We conducted 20 semi-structured interviews with product design engineers working virtually to uncover current challenges of, and the beginning of promising strategies for, effective virtual engineering work. We categorize and analyze Tangible Design activities, Intangible Design activities, and Communication and Project Management activities throughout the product design process. Contrary to present opinions, we found that much of a product design engineer's work is realizable in a virtual and distributed setting. However, there are still many challenges, especially when attempting Tangible Design activities—those that require physical products and tools—from home. These challenges, missing from existing virtual product design engineering literature, include but are not limited to individuals' lessened sense of accountability, fewer de-risking opportunities before product sign-off, and limited supervision of production staff. Product design engineers described novel strategies that emerged organically to mitigate these challenges, such as creating digital alternatives for engineering reviews and sign-offs and leveraging rapid prototyping. Recent advances in technology, an increased commitment to reducing environmental impact, and better work-life balance expectations from new generations of workers will only push society faster towards a distributed working model. Thus, it is critical that we use this opportunity to understand the existing challenges for distributed product design engineers, so that organizations can best prepare and become resilient to future shocks. [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]

Oxidation of graphite components could influence their designed life in a high-temperature nuclear reactor. The oxidized regions could potentially lower the allowed stress capacity. The American Society of Mechanical Engineers rules for the design and construction of graphite-moderated reactors recommend that subsurface regions that might become excessively damaged by oxidation during reactor operation be identified and excluded from geometry and stress calculations. Identification of oxidation-affected regions is possible, in principle, through complex modeling exercises of reactor behavior during hypothetical accident scenarios coupled with graphite oxidation models, but this procedure may not have the precision needed for informed decisions. This paper proposes an alternate method, based on interpretation of a series of well-designed oxidation experiments, which could augment the designer's tools. The procedure is illustrated by data on oxidation by air of several graphite grades (NBG-18, PCEA, IG-110, R4-650) that are corroborated with independent literature information, when available. The Wichner model for graphite oxidation used for this analysis provides conservative results that could be quickly implemented in the design process. [Display omitted] [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]

Hands-on engineering design is a critical element of quality engineering education commonly featured in the first and last year of undergraduate programs. Technical science and engineering coursework dominate the sophomore and junior year, often with fewer hands-on design experiences. We sought to mitigate this by integrating a hands-on engineering design project into a second-year thermodynamics-focused course for mechanical engineering students. We iterated on a heat-to-work device design project developed by Shepard and Hoxie to make it more open-ended, and to introduce a ‘who’ and a ‘why’ into the project by having students design devices to help hypothetical users accomplish tasks. We analyzed student reflections about the project using an inductive thematic approach. Our results indicate that the project was challenging; students consistently described experiencing failures. Most students were eventually able to succeed in the project using a combination of design iteration, risk-taking, and adaptation. They articulated growth in engineering design capabilities (including the valuation of simplicity in designs, brainstorming, applied learning, and time management), and increases in confidence (including takeaways about the importance of self-trust and intuition). In this paper, we present the revised project design and findings from its implementation using student and instructor reflections, and suggestions about how it could be implemented in thermodynamics courses for other mechanical engineering programs. [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]

The goal of this research is to understand, explore, and align the motivation and value that industry gains from sponsoring senior level mechanical engineering capstone design projects at Clemson University. This research compares the expected values of capstone projects from the perspectives of both the sponsoring companies and university faculty. If faculty understand the expected value from the sponsor, faculty could more effectively solicit and scope sponsor-based projects. Interviewing was used as the data collection method to explore faculty and company sponsor perceptions regarding the capstone design program. Interview results are linked and evaluated to extract thematic patterns in the responses. Conclusions of this work show that faculty anticipate companies continue to sponsor projects if the final products generated by the student teams are beneficial to the company. Companies tend to gain value from sponsoring capstone by providing low priority projects to garner solutions with minimal investment. Further, the realized benefit of the projects for the sponsoring companies depends on the structuring of the program, the proximity of the university, and the relationship between the company and university. [ABSTRACT FROM AUTHOR]

Real-world engineering design problems are widespread in various research disciplines in both industry and industry. Many optimization algorithms have been employed to address these kinds of problems. However, the algorithm's performance substantially reduces with the increase in the scale and difficulty of problems. Various versions of the optimization methods have been proposed to address the engineering design problems in the literature efficiently. In this paper, a comprehensive review of the meta-heuristic optimization methods that have been used to solve engineering design problems is proposed. We use six main keywords in collecting the data (meta-heuristic, optimization, algorithm, engineering, design, and problems). It is worth mentioning that there is no survey or comparative analysis paper on this topic available in the literature to the best of our knowledge. The state-of-the-art methods are presented in detail over several categories, including basic, modified, and hybrid methods. Moreover, we present the results of the state-of-the-art methods in this domain to figure out which version of optimization methods performs better in solving the problems studied. Finally, we provide remarkable future research directions for the potential methods. This work covers the main important topics in the engineering and artificial intelligence domain. It presents a large number of published works in the literature related to the meta-heuristic optimization methods in solving various engineering design problems. Future researches can depend on this review to explore the literature on meta-heuristic optimization methods and engineering design problems. [ABSTRACT FROM AUTHOR]

Purpose: In this paper, the authors aim to examine and comparatively evaluate a recently-developed metaheuristic called crystal structure algorithm (CryStAl) – which is inspired by the symmetries in the internal structure of crystalline solids – in solving engineering mechanics and design problems. Design/methodology/approach: A total number of 20 benchmark mathematical functions are employed as test functions to evaluate the overall performance of the proposed method in handling various functions. Moreover, different classical and modern metaheuristic algorithms are selected from the optimization literature for a comparative evaluation of the performance of the proposed approach. Furthermore, five well-known mechanical design examples are utilized to examine the capability of the proposed method in dealing with challenging optimization problems. Findings: The results of this study indicated that, in most cases, CryStAl produced more accurate outputs when compared to the other metaheuristics examined as competitors. Research limitations/implications: This paper can provide motivation and justification for the application of CryStAl to solve more complex problems in engineering design and mechanics, as well as in other branches of engineering. Originality/value: CryStAl is one of the newest metaheuristic algorithms, the mathematical details of which were recently introduced and published. This is the first time that this algorithm is applied to solving engineering mechanics and design problems. [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]

• An efficient robust topology optimization method for mechanical metamaterial. • Double-layer mesh technology improves the efficiency of topology optimization. • The impact of size uncertainty on topology optimization design is quantified. • Multiple mechanical metamaterials have been designed. With the increasingly stringent functional requirement of advanced equipment, it is crucial to design materials and structures with special properties. Improving optimization efficiency and shortening the design cycle are widely concerned as important scientific issues. This paper proposes a novel double-layer mesh-driven robust topology optimization strategy for mechanical metamaterials under size uncertainty. A double-layer mesh technology that establishes a mapping relationship between structural response and sensitivity information is developed, which greatly improves computational efficiency. The influence of size uncertainty is considered in topology optimization, and the robust design of mechanical metamaterials is realized. Various metamaterial designs are realized by solving optimization models based on sensitivity information. These can provide technical support and design references for the engineering application of mechanical metamaterials. [Display omitted] [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]

The usage of the Conceive-Design-Implement-Operate or C-D-I-O initiative is well documented within the context of engineering undergraduate programmes. To date, there are over 140 universities around the world which are part of the initiative, and most, if not all, of these universities have utilised the initiative as a framework within undergraduate curricula. This has been initially done for engineering undergraduate programmes, with other disciplines (outside of engineering) now choosing to implement this framework within its own context – due to its success in enhancing the overall student learning experience, at the engineering undergraduate level. There are, however, limited studies on how the framework is utilised within the context of a postgraduate programme. In particular, there are little to no studies on how this framework may be utilised to enhance the student learning experience within research-based postgraduate programmes e.g. MSc (by research) or PhD programmes. This paper aims to firstly review the available literature on how the framework influences postgraduate programmes globally. An attempt would also be made to discuss how previous studies have applied the framework to postgraduate education. To further narrate the application of the framework to graduate studies, a case study of a research-mode MSc programme will be explored. Specifically, how C-D-I-O influenced the research methodology of a chosen project within the MSc and resulted in designing and building an engineering system in a systematic and structured manner. [ABSTRACT FROM AUTHOR]

Prototyping is an important part of the design process, but researchers currently have a limited understanding of how the resources invested in a prototype or designer teams' intent for a prototype influence decision-making and perceptions of a prototype's value. Prior work has shown that significant investment of time or money in design tasks can lead to design fixation, but little work has explored how these factors might impact design teams' perceived value of prototypes with respect to the design process. Likewise, it is unclear how resource allocation and prototype intention effects the evolution of design team knowledge and subsequent design actions. To explore these relationships, an in situ study of prototyping tendencies was performed across two mechanical engineering design courses. Results suggest that effective design teams leveraged prototypes to uncover new design knowledge and limit uncertainty early in the design process. Importantly, this study did not find any significant relationships between the cost of a prototype in terms of money and time, and the perceived value of that prototype. Nor were any significant relationships found between costs and final design outcomes. Findings suggest that the true cost and value of a prototype with respect to design outcomes are challenging to explicitly quantify. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) processes offer unique capabilities (i.e., opportunities) yet inherent limitations (i.e., restrictions) due to the layer-by-layer fabrication of parts. Despite the newfound design freedom and increased use of AM, limited research has investigated how knowledge of the AM processes affects the creativity of students' ideas after being exposed to AM. This study investigates this gap through an experimental study with 343 participants recruited from a junior-level mechanical engineering design course. The participants were exposed to three variations in design for additive manufacturing (DfAM) education: (1) no DfAM, (2) restrictive DfAM, and (3) opportunistic and restrictive (dual) DfAM education. The effects of these three interventions were measured through differences in (1) participants' self-reported use of DfAM in a design challenge and (2) expert assessment of the creativity of the outcomes from the said design challenge. The results of the study indicated that variations in DfAM content did not result in differences in the participants' self-reported use of either opportunistic or restrictive DfAM, with all three groups reporting similar levels of emphasis. Further, participants from all three groups reported higher use of restrictive DfAM techniques, compared with opportunistic DfAM. Moreover, while variations in the content had no effect on the creativity (uniqueness and usefulness) of the participants' design outcomes, teaching both opportunistic and restrictive DfAM did result in the generation of designs with greater AM technical goodness--a novel and significant finding in our study. The results of this study highlight the need for DfAM educational interventions that encourage students to not only learn about but also integrate both opportunistic and restrictive concepts effectively into their creative design process. This would result in the generation of innovative products that leverage the design freedom enabled by AM, yet addressing the limitations inherent in the process. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) processes offer unique capabilities (i.e., opportunities) yet inherent limitations (i.e., restrictions) due to the layer-by-layer fabrication of parts. Despite the newfound design freedom and increased use of AM, limited research has investigated how knowledge of the AM processes affects the creativity of students' ideas after being exposed to AM. This study investigates this gap through an experimental study with 343 participants recruited from a junior-level mechanical engineering design course. The participants were exposed to three variations in design for additive manufacturing (DfAM) education: (1) no DfAM, (2) restrictive DfAM, and (3) opportunistic and restrictive (dual) DfAM education. The effects of these three interventions were measured through differences in (1) participants' self-reported use of DfAM in a design challenge and (2) expert assessment of the creativity of the outcomes from the said design challenge. The results of the study indicated that variations in DfAM content did not result in differences in the participants' self-reported use of either opportunistic or restrictive DfAM, with all three groups reporting similar levels of emphasis. Further, participants from all three groups reported higher use of restrictive DfAM techniques, compared with opportunistic DfAM. Moreover, while variations in the content had no effect on the creativity (uniqueness and usefulness) of the participants' design outcomes, teaching both opportunistic and restrictive DfAM did result in the generation of designs with greater AM technical goodness--a novel and significant finding in our study. The results of this study highlight the need for DfAM educational interventions that encourage students to not only learn about but also integrate both opportunistic and restrictive concepts effectively into their creative design process. This would result in the generation of innovative products that leverage the design freedom enabled by AM, yet addressing the limitations inherent in the process. [ABSTRACT FROM AUTHOR]

Research in additive manufacturing (AM) has increased the use of AM in many industries, resulting in a commensurate need for a workforce skilled in AM. In order to meet this need, educational institutions have undertaken different initiatives to integrate design for additive manufacturing (DfAM) into the engineering curriculum. However, limited research has explored the impact of these educational interventions in bringing about changes in the technical goodness of students' design outcomes, particularly through the integration of DfAM concepts in an engineering classroom environment. This study explores this gap using an experimental study with 193 participants recruited from a junior-level course on mechanical engineering design. The participants were split into three educational intervention groups: (1) no DfAM, (2) restrictive DfAM, and (3) restrictive and opportunistic (dual) DfAM. The effects of the educational intervention on the participants' use of DfAM were measured through changes in (1) participants' DfAM self-efficacy, (2) technical goodness of their AM design outcomes, and (3) participants' use of DfAM-related concepts when describing and evaluating their AM designs. The results showed that while all three educational interventions result in similar changes in the participants' opportunistic DfAM self-efficacy, participants who receive only restrictive DfAM inputs show the greatest increase in their restrictive DfAM self-efficacy. Further, we see that despite these differences, all three groups show a similar decrease in the technical goodness of their AM designs, after attending the lectures. A content analysis of the participants' design descriptions and evaluations revealed a simplification of their design geometries, which provides a possible explanation for the decrease in their technical goodness, despite the encouragement to utilize the design freedom of AM to improve functionality or optimize the weight of the structure. These results emphasize the need for more in-depth DfAM education to encourage the use of both opportunistic and restrictive DfAM during student design challenges. The results also highlight the possible influence of how the design problem is stated on the use of DfAM in solving it. [ABSTRACT FROM AUTHOR]

Lockbolts become increasingly important for modern mechanical engineering. The advantageous notch effect in consequence of a softer groove geometry, the larger stress cross section for the same nominal diameter and a lower scattering of preload results in the potential for increasing the load capacity of lockbolt systems in mechanical joints in comparison to conventional bolted joints. Consequently costs can be reduced with structural adjustments in mechanical engineering. These include the reduction of the required nominal diameter and consequently the reduction of component dimensions. Furthermore savings in manufacturing and assembly as well as cost reduction for maintenance are possible. For the calculation of lockbolt systems in mechanical joints the Technical Bulletin DVS‐EFB 3435–2 is available now for design and calculation engineers. The Technical Bulletin allows to calculate joints with lockbolt systems according to VDI 2230 – Part 1 [1, 2]. This paper presents joints with lockbolts in mechanical engineering. At first, the lockbolt technology and the assembly preload of the lockbolts will be introduced. After that the structural behavior of joints with lockbolts under static and alternating load will be presented. Finally, the modified calculation steps according to the VDI 2230 – Part 1 will be demonstrated [2]. [ABSTRACT FROM AUTHOR]

The Conceive-Design-Implement-Operate framework was initiated around 20 years ago at Massachusetts Institute of Technology (MIT) in order to reform engineering education to cope with the latest needs and challenges available in the industry. MIT researchers thought of the set of skills that graduates would need to have upon graduation. Then they developed the framework and syllabus, and then ran it through pilot programmes to monitor and refine its underlying approaches. The framework takes into account students' 'Technical Knowledge and Reasoning', 'Personal and Professional Skills', 'Interpersonal Skills' and ability to 'Conceive-Design-Implement and Operate' engineering system or processes. It is now well established with over 100 universities applying its concepts to enhance engineering education. The framework defines 12 standards, which are implemented to describe the frameworks structure. This framework has been applied to several modules (or courses) within Taylor's School of Engineering. In particular, the present investigation discusses the application of this framework within the context of a Capstone Project module in the school's Mechanical Engineering Programme. This module is a yearlong, 3rd year module within a 4-year engineering degree programme where students are tasked to design and build a complex engineering system. This paper discusses how the framework is used to enhance students' ability to brainstorm and develop suitable and sustainable engineering design solutions for a specified design challenge. The paper will discuss what the students have accomplished in each part of the conceive-design-implement-operate framework stages, as well as how these techniques have enabled them to appropriately design an engineering solution in addition to the rubrics used to assess them in each part of the framework. [ABSTRACT FROM AUTHOR]

The manufacturing of moulds and dies requires smooth surfaces and high-quality. This way, the surface roughness is an essential parameter in finishing milling to improve the quality of injected products, such as cell phones, laptops, aerospace and automotive parts which very much depend on the finishing of the moulds. Thus, this paper aims to robust optimisation of surface roughness of AISI H13 hardened steel in the finishing milling using ball nose end mills, by using a combination of methodologies such as robust parameter design (RPD), response surface methodology (RSM), normal boundary intersection (NBI), and mean square error (MSE). Based on this, eight input factors were used in the experimental tests, being four control factors (feed per tooth, axial depth of cut, radial depth of cut, and cutting speed) and four noise factors (tool overhang length, workpiece tilt angle, tool flank wear, and cutting strategy). The surface roughness evaluated in Ra pattern was measured in two cutting directions: in tool feed rate direction (Ra_f z) and in radial depth of cut direction (Ra_a e). The results showed that it was possible to generate a convex and equispaced Pareto frontier to MSE functions of surface roughness Ra and that the achieved optimum decrease the sensitivity of the milling process using ball nose end mills to the variability transmitted by the noise factors. • This paper presented a methodology to the bi-objective robust optimisation of surface roughness obtained by finishing milling of AISI H13 hardened steel. Since the mould and die market is becoming more demanding and competitive, the results achieved with this methodology are essential to attain a competitive advantage in the mould and die industry. • This experimental design and data collection will show moulds and dies manufacturers in the field of mechanical engineering design the relationship and effect between the cutting tool, the workpiece surface roughness and the machining parameters. • This unique multivariate experimental design considers eight factors that potentially influence the surface roughness of AISI H13 hardened steel in the finishing milling using ball nose end mills. • The obtained results show that it was possible to generate a convex and equispaced Pareto frontier to MSE functions of surface roughness Ra and that the achieved optimum lessens the sensitivity of the milling process using ball nose end mills to the variability transmitted by the noise factors. • The RPD-NBI-MSE methodology used in this paper may be applied in other manufacturing processes as a bi-objective robust modelling and optimisation procedure. [ABSTRACT FROM AUTHOR]

Although morphological charts are widely taught used tools in engineering design, little formal guidance is provided regarding their representation and exploration. Thus, an experiment was conducted to elucidate the influence of functional ordering on the exploration of morphological charts. Two design prompts were used, each with five different functional arrangements: (1) most-to-least important function, (2) least-to-most important function, (3) input-to-output function, (4) output-to-input function, and (5) Random. Sixty-seven junior mechanical engineering students were asked to generate integrated design concepts from prepopulated morphological charts for each design prompt. The concepts were analyzed to determine the frequency with which a given means was selected, how much of the chart was explored, the sequence of exploration, and the influence of function ordering. Results indicated a tendency to focus upon the initial columns of the chart irrespective of functional order. The most-to-least-important functional order resulted in higher chances and a uniformity of design space exploration. [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]

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]

Auxetic materials and structures have been attracting attention due to their extraordinary mechanical properties that stand out due to their high capacity to absorb energy. Some types of auxetic tubular structures have been studied and designed in diverse engineering fields such as mechanical, aerospace and medical. This manuscript cites more than a hundred papers containing the definitions, designs, structural analyses and optimization, mechanical properties, and specific applications of tubular design of auxetic structures. It can be noted from the present paper that additive manufacturing has been one of the most common manufacturing techniques used in many cases to manufacture tubular structures, and numerical analysis was essential to analyzing the behavior of the structures. The purpose of this paper is to assist researchers and engineers in using the methodology step by step to develop and apply auxetic tubular structures. • A review of auxetic tubes: definitions, optimization, properties, and applications. • Discussion about the potential applications of auxetic tubular structures. • Design and mechanical behavior of tubular auxetic structures. • Manufacturing trends of general auxetic structures and auxetic tubes. • Challenges and future directions on the auxetic tubes. [ABSTRACT FROM AUTHOR]

Constrained optimization problems (COPs) are most commonly encountered problems in science and engineering design field. To solve such kind of problem effectively, in this paper, we put forward a new approach named CAMDE which integrates adaptive differential evolution (DE) with new multi-population-based mutation operators. In CAMDE, some inferior solutions with low objective values are maintained in an external population. During mutation process, this external population is combined with main population to generate promising progress directions toward optimal region. Furthermore, DE's control parameters F and CR are adaptively adjusted according to the statistical information learnt from the previous searches in generating improved solutions. The advantageous performance of CAMDE is validated by comparisons with some representatives of state of the art in constrained optimization over 24 test instances. Moreover, four widely used constrained mechanical engineering problems are selected to validate the search ability of CAMDE for real-world problems. The performance results show that CAMDE is an effective approach to solving COPs, which is basically enabled by the integration of multi-population-based mutation operators and adaptive control strategy for DE's control parameters. [ABSTRACT FROM AUTHOR]

A significant gap exists between engineering students' perceptions of prototypes and prototyping abilities and professionals' perceptions and abilities. Structured prototyping frameworks have recently been developed and proposed as a means to help students close this gap, but the effects of these frameworks on students' behavior have not been assessed. The purpose of this work is to investigate if and how a structured prototyping framework affects the self-reported prototyping behaviors of engineering students. Understanding how structured prototyping frameworks affect students can provide educators with a deeper understanding of the way their students adopt and understand design methods. A mixed method study is presented. A 15-item survey and two open-ended questions were distributed to 235 students in a junior-level mechanical engineering design class in order to capture self-reported prototyping behavior. Quantitative results indicate that significant differences in engineering students' prototyping behaviors exist across time and between groups. Results from qualitative analysis indicate that students in the control group focused solely on improving technical quality, while students in both experimental groups focused on a wider range of design qualities. This study is the first to demonstrate that a structured prototyping framework can affect engineering students' self-reported prototyping behaviors during design activities. [ABSTRACT FROM AUTHOR]