Journal of Engineering Design and Analysis

Priya Sharma

2025-05-02T14:06:09+00:00

Evolutionary algorithms (EAs) have emerged as powerful optimization techniques in engineering problem-solving, offering robust solutions for complex, nonlinear, and multi-objective problems. These nature-inspired algorithms, including Genetic Algorithms (GAs), Evolution Strategies (ES), Differential Evolution (DE), and Genetic Programming (GP), mimic biological evolution to iteratively improve candidate solutions through selection, crossover, and mutation operations. Due to their adaptability and global search capabilities, EAs have been extensively applied in various engineering domains, such as structural optimization, mechanical design, robotics, and industrial automation.

This review provides a comprehensive overview of the fundamental principles of EAs, their key variations, and their role in solving real-world engineering challenges. The study highlights the strengths and limitations of different evolutionary techniques and their performance in handling constrained, dynamic, and multi-objective optimization problems. Furthermore, the integration of evolutionary algorithms with machine learning, swarm intelligence, and metaheuristic hybridization is discussed, demonstrating their enhanced efficiency in tackling complex engineering tasks.

The article also explores recent advancements in evolutionary computation, including hybridization with artificial intelligence (AI), quantum-inspired evolutionary computing, and adaptive parameter control. These emerging trends aim to improve convergence speed, solution accuracy, and computational efficiency. Future research directions focus on developing more intelligent, scalable, and domain-specific evolutionary techniques that can address the increasing complexity of modern engineering problems.

By summarizing the evolution, applications, and future scope of EAs, this review provides valuable insights into their continued relevance and potential impact on engineering optimization and automation.

Vibration Analysis in Structural and Mechanical Components: Methods and Applications

2025-05-02T14:52:41+00:00

Vibration analysis is a crucial aspect of evaluating the dynamic behavior of structural and mechanical components. It aids in identifying faults, predicting failures, and enhancing performance. By analyzing vibration characteristics, engineers can assess structural integrity, optimize designs, and develop effective maintenance strategies. Various industries, including aerospace, automotive, civil engineering, and manufacturing, rely on vibration analysis for quality control, safety enhancement, and efficiency improvement.

This review article provides an overview of the various methods used in vibration analysis, including experimental, analytical, and numerical techniques. Experimental methods, such as modal analysis and impact testing, enable direct measurement of vibration characteristics, while analytical methods use mathematical models to describe dynamic behavior. Numerical approaches, including finite element analysis (FEA), provide computational solutions for complex vibratory problems. The effectiveness of these methods is discussed in relation to their applications in real-world engineering problems.

The applications of vibration analysis in diverse fields such as aerospace, automotive, civil engineering, and machinery health monitoring are also examined. In the aerospace sector, vibration analysis helps monitor structural health and detect fatigue-induced damage. The automotive industry employs it for engine diagnostics, chassis optimization, and ride comfort improvement. Civil engineers utilize vibration analysis for bridge monitoring, earthquake-resistant design, and structural health assessment. Additionally, in industrial machinery, vibration-based condition monitoring is essential for predictive maintenance and failure prevention.

Emerging trends in vibration analysis, including machine learning and artificial intelligence integration, are explored to highlight future directions in this domain. The incorporation of smart sensors and IoT-enabled real-time monitoring systems is revolutionizing the field, allowing for automated fault detection and predictive analytics. Advanced data-driven approaches, such as deep learning algorithms, are being employed to enhance diagnostic accuracy and decision-making processes. Furthermore, energy harvesting techniques utilizing vibrational energy for power generation are gaining interest as a sustainable solution.

The continuous advancement of vibration analysis techniques is expected to drive improvements in structural health monitoring, reliability assessment, and system performance optimization across multiple industries. This paper provides insights into the current state of vibration analysis and highlights the innovations shaping its future.

Surrogate Modeling for Complex Engineering Design Problems: A Comprehensive Review

2025-05-02T14:45:48+00:00

Surrogate modeling has emerged as a powerful tool for solving complex engineering design problems by providing computationally efficient approximations of high-fidelity simulations. In disciplines such as aerospace, automotive, structural engineering, and biomedical applications, high-fidelity models require significant computational resources, making optimization and real-time decision-making challenging. Traditional optimization methods often struggle with the computational burden associated with iterative simulations, necessitating alternative approaches that can reduce computational cost while maintaining accuracy.

Surrogate models, including polynomial response surfaces, Kriging, artificial neural networks (ANNs), Gaussian process regression, radial basis function (RBF) models, and support vector regression (SVR), enable rapid evaluations and facilitate efficient design exploration. These models approximate expensive simulations and enable engineers to perform parametric studies, uncertainty quantification, and multi-disciplinary optimization without the need for exhaustive computations. Additionally, hybrid surrogate modeling approaches, which combine multiple modeling techniques or integrate multi-fidelity simulations, have shown promising results in balancing accuracy and computational efficiency.

This review presents an in-depth discussion of surrogate modeling techniques, their theoretical foundations, practical applications in engineering optimization, and recent advancements in hybrid and adaptive approaches. Special attention is given to the role of machine learning and artificial intelligence in enhancing surrogate model performance, particularly in high-dimensional and nonlinear optimization problems. Furthermore, the challenges associated with surrogate modeling, such as model selection, generalization, error estimation, and robustness, are explored in detail.

Future research directions are identified, including the development of adaptive AI-driven frameworks, automated model refinement techniques, and improved uncertainty quantification methods. The integration of surrogate modeling with digital twins, real-time decision-making systems, and high-performance computing is expected to further advance its capabilities and expand its application across diverse engineering domains. This study aims to provide insights into emerging trends that will shape the next generation of design optimization frameworks, making them more efficient, scalable, and intelligent.

Interdisciplinary Coupling in Multidisciplinary Design Optimization (MDO): Challenges and Innovative Solutions

2025-05-02T14:29:47+00:00

Multidisciplinary Design Optimization (MDO) is a powerful computational approach for optimizing complex engineering systems by integrating multiple disciplines. One of the fundamental challenges in MDO is interdisciplinary coupling, which arises due to interdependencies between different subsystems. These couplings introduce significant computational complexity, data integration issues, uncertainty propagation, and convergence difficulties, making optimization challenging. This review article explores the key challenges associated with interdisciplinary coupling in MDO, including computational costs, model uncertainties, and disciplinary conflicts. Additionally, the paper highlights innovative solutions such as surrogate modeling, machine learning-based acceleration techniques, multi-fidelity modeling, coupled solvers, and blockchain-enabled data sharing. Recent advancements in aerospace, automotive, and civil engineering applications demonstrate the effectiveness of these techniques in overcoming interdisciplinary coupling issues. The study concludes that integrating adaptive artificial intelligence-driven MDO frameworks can further streamline interdisciplinary collaboration and enhance optimization efficiency. Future research should focus on improving computational scalability, uncertainty quantification, and real-time data integration in MDO systems.

Feature-Based Design Automation: A Review of Techniques and Applications

2025-05-02T14:14:45+00:00

Feature-Based Design Automation (FBDA) is a crucial advancement in modern engineering design, enabling increased efficiency, accuracy, and flexibility in product development. By leveraging predefined design features, parametric modeling, and rule-based automation, FBDA reduces manual effort, enhances design consistency, and facilitates mass customization. It plays a significant role in streamlining complex design processes, reducing lead times, and improving product quality.

This review article explores various techniques used in FBDA, including parametric modeling, knowledge-based engineering (KBE), artificial intelligence (AI)-driven optimization, and generative design. Parametric modeling allows for rapid modifications and adaptability, while KBE integrates expert knowledge and decision-making rules into the design workflow. AI-driven techniques enhance automation by incorporating machine learning algorithms that optimize design parameters, whereas generative design enables the creation of innovative solutions by exploring multiple configurations under defined constraints.

Furthermore, this paper discusses the applications of FBDA across multiple industries, including automotive, aerospace, and manufacturing. In the automotive industry, FBDA facilitates mass customization and aerodynamic optimization, while in aerospace, it supports lightweight structure design and automated component standardization. The manufacturing sector benefits from feature-based design through efficient CAD-CAM integration, fixture automation, and additive manufacturing applications.

In addition to current methodologies and applications, this review highlights emerging trends in FBDA, such as machine learning-driven design automation, cloud-based collaborative modeling, digital twin integration, and advanced generative algorithms. These advancements are reshaping design automation by enabling real-time data sharing, predictive maintenance, and AI-assisted innovation. The integration of FBDA with modern computational tools is expected to drive the future of intelligent and autonomous design, leading to greater sustainability, efficiency, and competitiveness in engineering industries.

By providing a comprehensive overview of techniques, applications, and future developments, this article aims to offer valuable insights into the ongoing evolution of Feature-Based Design Automation and its growing impact on modern product development.