Mechanical Engineering Design Research Articles

An adaptive gravitational search algorithm for optimizing mechanical engineering design and machining problems

The gravitational search algorithm (GSA) is widely used for solving optimization problems because it performs in a superior manner as compared to various competing evolutionary and swarm-based metaheuristics. However, GSA frequently gets trapped in local optima due to a lack of solution diversity. Although chaotic gravitational search algorithm (CGSA) can resolve this issue to some extent but its degraded exploitation rate and convergence speed may not result in desired outcome. To this end, diversity-based chaotic GSA (DCGSA) has exhibited its capability to resolve the issues encountered with GSA and CGSA to a certain extent for unconstrained problems. DCGSA achieves this characteristic through the use of an adaptive gravitational constant, which varies according to the diversity values of the population. Since most real-world problems are subjected to some constraints, it is prudent to improve the performance of GSA in solving constrained optimization problems. The present study integrates the enhanced search capability of DCGSA with a generalized constraint handling mechanism to improve the performance of GSA in solving constrained problems. It is observed that DCGSA significantly outperforms GSA on both CEC (Congress on evolutionary computation) 2006, 2010 and 2017 functions and competes strongly with CGSA. Diversity analysis shows that the capability to balance exploration and exploitation rates is enhanced using CGSA and DCGSA. Furthermore, DCGSA algorithms outperform GSA and CGSA on real-world machining and CEC 2020 mechanical design problems. Comparison with state-of-the-art algorithms is made to analyze the performance of the algorithms from a larger perspective.

Evaluation of Science Implementation in Mechanical Engineering Design Curriculum Class 2A State Polytechnic of Jakarta Pekalongan City Campus Academic Year 2023/2024

Evaluation of Science Implementation in Mechanical Engineering Design Curriculum Class 2A State Polytechnic of Jakarta Pekalongan City Campus Academic Year 2023/2024

Hybridization of Social Group Optimization and Differential Evolution Algorithm for Solving Speed Reducer Design Problem

Objectives: The objective of this study is to present a hybrid approach named SGO-DE, which combines the Social Group Optimization (SGO) algorithm and differential evolution (DE), aiming to balance exploration and exploitation capacities to improve the accuracy of the optimization algorithm in finding optimal solution for speed reducer design problem. This hybrid approach is simulated for a speed reducer mechanical engineering design problem and the results are compared to several other state-of-the-art optimization algorithms. Method: To improve the exploration and exploitation of SGO, in its acquiring phase Differential Evolution (DE) is introduced. The individual candidate solutions derived from the Improving phase of SGO tries to acquire better values using DE. This helps in striking a better balance between exploration and exploitation, there by achieving improved optimal values and not getting trapped in local optima. The performance of the SGO-DE method is then evaluated and compared to other optimization algorithms through experimentation on the speed reducer design challenge. Findings: The findings of this study indicate that the SGO-DE hybrid approach outperforms other state-of-the-art algorithms by a significant margin in terms of optimization results. The numbers of function evaluations (FEs) significantly go low as less as 6000 compared to other state-of-the algorithms. The comparison demonstrates the efficacy of the SGO-DE method in enhancing solution quality and speeding up execution. Novelty: The novelty of this study lies in the development a hybrid approach of SGO and DE which is efficient in achieving competitive performance in less numbers of function evaluation in speed reducer design problem. This hybridization can strike a better balance between exploration and exploitations. Keywords: SGO, DE, Hybridization, Nature-inspired, Optimization algorithm

Editorial

The Italian Association for Stress Analysis (AIAS) was founded in 1971 by researchers from academia, research centers and industry. AIAS was intended as a community where to discuss, to share and to develop scientific knowledge related to all technical aspects of stress analysis. In the years, from an initial focus on experimental techniques, AIAS contributed considerably to the development of modern numerical methods and computational techniques for the mechanical engineering design. In 2015, AIAS turned in the Italian Scientific Society of Mechanical Engineering Design.Today, AIAS is an institutional partner that supports the instances from academia in subject area of the mechanical engineering design. Every year, AIAS organizes a technical conference offering the possibility to present research updates, share new ideas and foster collaborations. The AIAS conference has become a fundamental event for all those interested in current developments in mechanical engineering design and stress analysis, where to meet researchers, testing equipment and software developers.The 52nd AIAS Conference edition was held in Genoa, Italy. The response of researchers and students has been outstanding: over 200 oral contributions have been presented during the three days of the conference, with four parallel sessions. In addition to the thematic sessions on AIAS traditional subjects, special sessions on additive manufacturing, energy methods for structural analysis, circular design in mechanical engineering, mechanical behavior under extreme conditions, have been successfully organized with the contribution of the AIAS technical committees.Among all contributions presented at the conference, 44 have been selected to be published after peer review, in this volume. This was made possible thanks to the active participation of all AIAS members, to the work of the AIAS Scientific Committee panel (Profs Umberto Galietti, AIAS Scientific Coordinator, Gabriele Arcidiacono AIAS Scientific coordinator for external relationships, Enrico Armentani, Davide Castagnetti, Virgilio Fontanari and Aurelio Somá) and Conference Papers Reviewers. Their outstanding contribution is gratefully acknowledged. Disclaimer AIAS2023 Conference was held in presence on Sept. 6-9, 2023. Presentations were arranged on four parallel sessions with a time slot of 15 min assigned to each presenter. Approximately 280 participants attended the conference during the three days event. All sessions have been continuously monitored by the organizers to provide technical support. The conference ran smoothly, and participants feedback was very positive.

Enhancing the Outcome of Mechanical Engineering Design Course with the SCAMPER Methodology Framework

Methodology frameworks in engineering education are structured approaches or models used to design, implement, and evaluate educational practices in engineering disciplines. These frameworks provide a systematic way to organize and optimize the teaching and learning process, ensuring that engineering students receive a comprehensive and effective education. SCAMPER is a creative thinking technique used to generate new ideas and approaches for problem-solving or project development. Each letter in SCAMPER stands for a different prompt to enhance the process targeted. These prompts stand for the substitute, combine, adapt, modify, put to another use, eliminate, and reverse. Evolving SCAMPER as a methodology framework for Mechanical Engineering education involves adapting its principles and prompts to cater specifically to the needs and challenges of engineering students. This study presents the use of SCAMPER methodology as an educational framework for mechanical engineering and application to the Mechanical Engineering Design course given for senior year students with proposed examples based on experiences collected from previous classes thought.

Enabling cross-morphological performance comparison: A case study in heat management design

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-calledtertia 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.

Design and evaluation of a smart passive dynamic arm support for robotic-assisted laparoscopic surgery

Surgeons performing robotic-assisted laparoscopic surgery experience physical stress and overuse of shoulder muscles due to sub-optimal arm support during surgery. The objective is to present a novel design and prototype of a dynamic arm support for robotic laparoscopic surgery to evaluate its ergonomics and performance on the AdLap-VR simulation training device. The prototype was designed using the mechanical engineering design process: Technical requirements, concept creation, concept selection, 3D-design and built of the prototype. A crossover study was performed on a marble sorting task on the AdLap-VR. The first group performed four trials without the arm support, followed by four trials with the arm support, and the other group executed the sequence vice versa. The performance parameters used were time to complete (s), path length (mm), and the number of collisions. Afterward, the participants filled out a questionnaire on the ergonomic experience regarding both situations. 20 students executed 160 performed trials on the AdLap-VR Significant decreases in the subjective comfort parameters mental demand, physical demand, effort and frustration were observed as a result of introducing the novel arm support. Significant decreases in the objective performance parameters path length and the number of collisions were also observed during the tests. The newly developed dynamic arm support was found to improve comfort and enhance performance through increased stability on the robotic surgery skills simulator AdLap-VR.

Analysis of structures with arbitrary kinematic boundary conditions by the semi-analytical finite element method

The successful application of FEM to the analysis of structures is largely due to the efficiency of the use of modern software packages, in connection with which the role of program systems that implement the solution process increases. The correct organization of the computing complex, the choice of optimal algorithms for solving systems of linear and nonlinear equations largely determine the possibilities of the method in terms of the structural complexity of the objects under consideration, the accuracy of the results obtained, and the complexity of setting nonlinear problems. Therefore, there is an increased interest in the development of fairly universal computing complexes based on FEM. One of the effective software complexes is the "Strength" system, designed to conduct comprehensive research in the field of mechanics of a deformable solid, the basic principles of construction of which are used in this work in the implementation of a semi-analytical version of the FEM.
 In this work, solutions of a significant number of control problems of deformation of massive and thin-walled prismatic bodies under different boundary conditions and loads are obtained. In the process of solving new problems, the estimation of the convergence of results was carried out on the basis of a sequential increase in finite elements and contained terms of decomposition, an increase in the accuracy of systems of linear and nonlinear equations, and the accuracy of satisfaction with natural boundary conditions was checked. The developed effective method for solving new complex problems of deformation of prismatic bodies is implemented in the form of complex programs and can be used in design and construction practice in construction, mechanical engineering and other fields of technology.

Preface

The International Conference on Applications and Design in Mechanical Engineering 2023 (ICADME 2023) was held on 4th and 5th September 2023 in Kuala Lumpur, Malaysia. ICADME 2023 was organized by the Mechanical Engineering Programme, under the purview of Faculty of Mechanical Engineering and Technology, Universiti Malaysia Perlis.The ICADME 2023 aims to provide a platform for participants from diverse field of Mechanical Engineering backgrounds to exchange ideas and findings to promote cooperation, networking as well as knowledge sharing in both engineering practice and research in higher education. The conference includes the fields of i) Material Science & Engineering, ii) Design & Manufacturing, iii) Energy & Thermofluids, iv) Robotics, Automation & Control System as well as v) Biomechanics. The conference received 37 papers from Malaysia and other countries over the world. After a rigorous peer-review process, 28 papers were accepted. The accepted papers that focused on theory, applications, design and experimental studies relevant to mechanical engineering fields were invited to present in this conference.We want to express our gratitude to all reviewers, both local and international, for their time and effort in reviewing the papers. Their feedbacks to the authors have been excellent and useful to ensure the high quality of papers for the conference. Our sincere thanks also go to all sponsors for their generosity. Last but not least, we would like to thank the organizing committee members for their hard work, cooperation and insights in ensuring the success of this conference.List of Editors are available in the pdf.

Mathematical Model for Excited State Fluid Dynamics

The rational mechanic’s research method is synthesized with the help of mathematical means such as generalized function analysis and tensor analysis. The basic definition of the fluctuation velocity generation is based on phenomenological physics in this article. The basic control equations of general excited state fluid dynamics applicable to the flowfield are obtained based on the basic principles of quantum mechanical superposition states. The simplified basic equations of the excited State are finally obtained through time and space discretization. The basic theory of excited state fluid dynamics is established, providing new ideas for the innovation and application of flow control, fluid mechanical engineering design, and other aspects of research work.

DTV-CNN: Neural network based on depth and thickness views for efficient 3D shape classification

Fast and effective algorithms for deep learning on 3D shapes are keys to innovate mechanical and electronic engineering design workflow. In this paper, an efficient 3D shape to 2D images projection algorithm and a shallow 2.5D convolutional neural network architecture is proposed. A smaller convolutional neural network (CNN) model is achieved by information enrichment at the preprocessing stage, i.e. 3D geometry is compressed into 2D “thickness view” and “depth view”. Fusing the depth view and thickness view (DTV) from the same projection view into a dual-channel grayscale image, can improve information locality for geometry and topology feature extraction. This approach bridges the gap between mature image deep learning technologies to the applications of 3D shape. Enhanced by several essential scalar geometry properties and only 3 projection views, a mixed CNN and multiple linear parameter (MLP) neural network model achives a validation accuracy of 92 % for ModelNet10 mesh-based dataset, while the training time is one order of magnitude less than the original multi-view CNN approach. This study also creates new 3D shape datasets from 2 open source CAD projects. Higher validation accuracy is obtained for realistic CAD datasets, i.e. 97 % for FreeCAD's mechanical part library and 95 % for KiCAD electronic part library. The training cost reduces to tens of minutes on a laptop CPU, given the smaller input data size and shallow neural network design. It is expected that this approach can be adapted for other machine learning scenarios involved in CAD geometry.

DATA REPLICATION IN DISTRIBUTED SYSTEMS USING OLYMPIAD OPTIMIZATION ALGORITHM

Achieving timely access to data objects is a major challenge in big distributed systems like the Internet of Things (IoT) platforms. Therefore, minimizing the data read and write operation time in distributed systems has elevated to a higher priority for system designers and mechanical engineers. Replication and the appropriate placement of the replicas on the most accessible data servers is a problem of NP-complete optimization. The key objectives of the current study are minimizing the data access time, reducing the quantity of replicas, and improving the data availability. The current paper employs the Olympiad Optimization Algorithm (OOA) as a novel population-based and discrete heuristic algorithm to solve the replica placement problem which is also applicable to other fields such as mechanical and computer engineering design problems. This discrete algorithm was inspired by the learning process of student groups who are preparing for the Olympiad exams. The proposed algorithm, which is divide-and-conquer-based with local and global search strategies, was used in solving the replica placement problem in a standard simulated distributed system. The 'European Union Database' (EUData) was employed to evaluate the proposed algorithm, which contains 28 nodes as servers and a network architecture in the format of a complete graph. It was revealed that the proposed technique reduces data access time by 39% with around six replicas, which is vastly superior to the earlier methods. Moreover, the standard deviation of the results of the algorithm's different executions is approximately 0.0062, which is lower than the other techniques' standard deviation within the same experiments.

Bio-Inspired Design in Mechanical Engineering: From Nature to Innovation

Bio-inspired design in mechanical engineering represents a transformative approach that draws inspiration from nature's ingenious solutions to address complex engineering challenges. This article explores the essence, applications, and future prospects of bio-inspired design. The essence of this discipline involves a meticulous process that includes observation, analysis, and the application of principles inspired by the natural world. Bio-inspired design has yielded remarkable innovations in various fields, from aerospace to healthcare, with applications encompassing energy-efficient transportation, bionic prosthetics, and bio-inspired materials. As the world grapples with pressing environmental issues, the future of bio-inspired design holds great promise in delivering sustainable solutions, advancing materials science, and revolutionizing energy technologies. It will continue to shape the world of mechanical engineering, offering a harmonious synergy between human creativity and the timeless wisdom of nature, guiding us toward a more sustainable and innovative future.

Application of Modified Adaptive Bats Sonar Algorithm with Doppler Effect and Levy Flight (MABSA-DELF) to Optimize Mechanical Engineering Problems

This paper describes the application of the Modified Adaptive Bats Sonar Algorithm with Doppler Effect and Levy Flight (MABSA-DELF) to mechanical engineering design optimization issues. MABSA-DELF is a new algorithm that employs Doppler Effect and Levy Flight theory to improve the position of the transmitted bats’ beam. This project served as a showcase for the superior performance of MABSA-DELF. It was created using the MATLAB software’s computer simulation method. MABSA-DELF demonstrated a superior ability to solve engineering design problems in the fields of business, mechanical/manufacturing engineering, and electrical engineering. MABSA-DELF’s result are compared to those of other established algorithms.

What Sets Proficient and Expert Users Apart? Results of a Computer-Aided Design Experiment

AbstractAs 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.

An efficient hybrid differential evolution–Jaya algorithm for enhancing vibration behaviour in automotive turbocharger systems

Enhancing the vibration behaviour of rotating systems while meeting conflicting design needs is a difficult task, which is often treated as an inverse issue and formulated as a nonlinear optimization problem with constraints. To tackle this intricate challenge, a novel hybrid algorithm (HDE-Jaya) has been developed, combining the differential evolution (DE) and Jaya methods. The Jaya model is integrated into DE as a mutation operator to boost exploration and exploitation. In addition, the crossover probability is generated randomly at each iteration to preserve diversity. This makes HDE-Jaya a parameter-free algorithm. The performance of HDE-Jaya was validated by optimizing six common mechanical engineering design problems and further evaluated on an automotive turbocharger design, aiming to reduce vibrations, minimize binding stress and improve dynamic stability. The results indicate that HDE-Jaya is superior to other algorithms, including DE and the Jaya algorithm, in terms of solution accuracy and computational efficiency.

Augmented Reality and Deep Learning Integration for Enhanced Design and Maintenance in Mechanical Engineering.

The convergence of augmented reality (AR) and deep learning has emerged as a transformative paradigm in the field of mechanical engineering, revolutionizing the design and maintenance processes. This paper explores the integration of AR and deep learning technologies to create a synergistic framework that enhances efficiency, precision, and overall performance in mechanical engineering applications. The fusion of AR, a technology that overlays digital information onto the physical world, and deep learning, a subset of artificial intelligence (AI) characterized by intricate neural networks, presents a groundbreaking approach to address the challenges faced in the design and maintenance phases of mechanical systems. In the design realm, this integration facilitates a more immersive and collaborative environment for engineers and designers. AR provides real-time visualization of complex 3D models and simulations, allowing engineers to interact with virtual prototypes overlaid onto physical objects. [1] Deep learning algorithms enable predictive modeling and analysis, enhancing the understanding of system behavior under various conditions. The combination empowers designers to iterate rapidly, make informed decisions, and optimize designs, ultimately reducing time-to-market and improving overall product quality. In the maintenance domain, the AR and deep learning integration transforms traditional approaches by offering intelligent and context-aware solutions. AR-enabled smart glasses or devices equipped with computer vision capabilities allow technicians to access real-time data, such as equipment specifications, maintenance procedures, and live diagnostics. Deep learning algorithms enhance fault detection and predictive maintenance by analyzing historical data and identifying patterns indicative of potential issues. This proactive approach minimizes downtime, reduces maintenance costs, and extends the lifespan of mechanical systems. The paper illuminates the potential of their integration in reshaping the landscape of mechanical engineering. The presented framework not only fosters innovation in design and maintenance but also sets the stage for a new era in which intelligent technologies work in tandem to propel the field forward.

Optimization of turning process parameters using a new hybrid evolutionary algorithm

In this article, a new hybrid improved differential evolution and Nelder-Mead (IDE-NM) is introduced for optimizing the multi-objective machining process during the turning operation under three modes of lubrication conditions. The fitness functions are the tangential cutting force, the surface roughness, and the cutting power. Five mixed design variables are considered in the optimization procedure including the cutting speed, feed rate, and depth of cut, mode of lubrication, and the type of cutting material. The mathematical expressions of the three objectives are created based on experimental results and modeled using the artificial neural network (ANN). In the first step, the proposed method is examined by solving seven mechanical engineering design problems. The comparison results illustrate that the IDE-NM algorithm outperforms other state-of-the-art optimization methods considered in the literature. Moreover, for the turning operation problem, the results of IDE-NM are compared with those of four recent metaheuristics. The results show that the proposed method outperforms the four compared algorithms in terms of robustness, high success rate, and can provide effective solutions.

Mechatronics: A Study on Its Scientific Constitution and Association with Innovative Products

Mechatronics as a science is a synergic combination of mechanical engineering, electronic control, and software design in product development and manufacturing processes. To understand how the field of knowledge that incorporates mechatronics in innovative products, given that it is not in itself a basic engineering discipline but an integration of fields of knowledge, has advanced, it was developed a bibliometric and qualitative study through systematic review with an analytical framework for the establishment of variables to subsidize the construction of the selected theoretical body. The results and conclusions of the sampled publications show that mechatronics performs one of the principal roles in innovation due to the multidisciplinary integration that the scope of innovation in product engineering is propitiating. The study classified five global scenarios: practical approaches aimed at product development, research that studies curricula and education in engineering, studies involving components of a mechatronic system, use of artificial intelligence, and methodologies for designing mechatronic systems. In addition to underscoring that the use of the term innovation associated with mechatronics in a large proportion of the publications extrapolates the operational level, characterizing an attribution to the term that is always associated with the applications, ramifications, and perspectives that the respective product, design, robot, or system could offer to the market or future research. Similarly, it was found that the results of many publications associate the term innovation with a return on investments or operational costs and emphasize the advantages of using the technology for commercial ends.

Методы и подходы к решению задачи автоматизации проектирования технологических процессов для единичных и мелкосерийных производств

This paper gives an overview of existing solutions in the field of automation of technological process design in mechanical engineering. The applicability of existing methods for solving the problem of automating the design of technological processes for individual and small-scale production is assessed. A concept of building a system of automated design of technological processes for individual and small-scale production is formulated.