Abstract: Abstract Mechanical mechanisms are designed to perform specific functions in a variety of fields. In most cases, there is not a unique mechanism that performs a well-defined function. For example, vehicle suspensions are designed to improve driving performance and ride comfort, but different types are available depending on the environment in which they are used. This variability in design due to different usage environments makes performance comparison difficult. In addition, the industry’s traditional design process is multi-step, gradually reducing the number of design candidates while performing costly analysis to achieve target performances. Recently, artificial intelligence models have been used to replace the computational cost of finite element analysis (FEA). However, there are limitations in data availability and different analysis environments, especially when moving from low-fidelity to high-fidelity analysis. In this paper, we propose a multi-fidelity design framework aimed at recommending optimal types and designs of mechanical mechanisms. As an application, vehicle suspension systems were selected, and several types were defined. For each type, mechanism parameters were generated and converted into 3D CAD models, followed by low-fidelity rigid body dynamic analysis under driving conditions. To effectively build a deep learning-based multi-fidelity surrogate model, the results of the low-fidelity analysis were analyzed using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and sampled at 5% for the high-cost flexible body dynamic analysis. After training a multi-fidelity model, a multi-objective optimization problem was formulated for the performance metrics of each suspension type. Finally, we recommend the optimal type and design based on the input (sprung mass) to optimize the ride comfort-related performance metrics. Subsequently, to validate the proposed methodology, we extracted basic design rules for Pareto solutions using data mining techniques. We also verified the effectiveness and applicability by comparing the results with those obtained from a conventional deep learning-based design process.

Abstract: A novel feature mapping topology optimization method is presented, allowing for the creation of features with highly flexible shapes. The method easily integrates with conventional density-based formulations. Feature shapes are implicitly described by NURBS control points. The feature shape dictates the locations of two sets of projection points to represent the solid void boundaries. At these projection points, density values are projected onto a finite element mesh. The method optimizes feature shapes in a gradient-based manner, while allowing more specific control of the feature shapes than classical level set methods. Several feature fields can be combined to create a final output design. It is found that the eminent flexibility of the NURBS-based feature definition is a benefit but also requires additional regularization to guarantee stability of the optimization.

Abstract: Introduction This study investigates the impact of repeated exposure to the Engineering Design Process (EDP) through culturally responsive STEM lessons, delivered in an informal science learning (ISL) setting to middle school students from underserved communities in California’s Central Valley. Methods A mixed-methods approach was employed, combining qualitative analysis of student journals and survey responses with quantitative analysis of pre- and post-survey data. The study focused on students’ STEM identity, self-efficacy, and perceptions of engineering knowledge. Results Qualitative findings highlighted key themes of problem-solving and understanding the EDP, demonstrating that students engaged deeply with the process. Quantitative results indicated significant improvements in students’ STEM identity, self-efficacy, and perceptions of engineering knowledge following repeated exposure to the EDP. Discussion These findings suggest that embedding the EDP within culturally relevant, hands-on ISL activities can effectively enhance students’ engagement with STEM subjects, foster stronger STEM identities, and address educational inequities.

Abstract: Bayesian Optimization (BO) is a foundational strategy in engineering design optimization for efficiently handling black-box functions with many constraints and expensive evaluations. This paper introduces a novel constraint-handling framework for Bayesian Optimization (BO) using Prior-data Fitted Networks (PFNs), a foundation transformer model. Unlike traditional approaches requiring separate Gaussian Process (GP) models for each constraint, our framework leverages PFN's transformer architecture to evaluate objectives and constraints simultaneously in a single forward pass using in-context learning. Through comprehensive benchmarking across 15 test problems spanning synthetic, structural, and engineering design challenges, we demonstrate an order of magnitude speedup while maintaining or improving solution quality compared to conventional GP-based methods with constrained expected improvement (CEI). Our approach particularly excels at engineering problems by rapidly finding feasible, optimal solutions. This benchmark framework for evaluating new BO algorithms in engineering design will be published at https://github.com/rosenyu304/BOEngineeringBenchmark.

Abstract: This full research paper investigates the representation of a range of different design paradigms presented in the engineering education literature. As the global community contends with wicked problems, including the United Nations Sustainable Development Goals, the limitations of historically anthropocentric design philosophies have become evident. While human-centered design prioritizes human needs, it often neglects broader environmental and non-human considerations, potentially exacerbating many wicked problems. Life-centered design, by contrast, acknowledges the interconnectedness of all systems—human, non-human, and environmental—and presents a promising alternative for engineering education. This study employs a scoping literature review to determine the prevalence of different design philosophies in engineering education literature and explore the extent to which life-centered design is represented. The research seeks to answer: (1) Across the engineering education literature, to what extent are underlying design paradigms clearly and prominently communicated within design education publications? (2) To what extent do recent publications advocate post-anthropocentric design paradigms? (3) What apparent trends regarding design paradigms exist within the engineering education literature? A systematic review of engineering and design education literature indexed in Scopus was conducted. Relevant abstracts were reviewed and classified to determine the underlying design philosophy. The classification process focused on identifying trends in the apparent focus on general design, human-centered, and post-anthropocentric design approaches in engineering education publications. These findings highlight a gap in engineering education literature regarding explicit engagement with life-centered design principles. Integrating life-centered design into engineering education could increase the preparedness of future engineers to address complex, system-level challenges.

Abstract: Abstract Large-scale design and development projects involve working with immature information and transferring it between colleagues, departments and suppliers. Information’s maturity is an important consideration for many decisions, such as how much effort to dedicate to tasks given the maturities of their inputs, how much to overlap project phases given the maturity of information available for early release, and how much allowance to give for possible changes to preliminary information. An understanding of information maturity could also help to interpret and learn from archived project information. However, there are multiple perspectives on the meaning of information maturity and an agreed understanding of its aspects has not yet emerged. This article contributes an integrating taxonomy that defines and delineates 18 distinct aspects of information maturity in the design and development context. The new taxonomy integrates concepts from literature, clarifies that information can mature along numerous dimensions during design and development, and may inform future research into preliminary information management in large-scale projects.

Abstract: To meet the increasing demands for a cost effective and precise design process of electrical machines, particularly in automotive traction applications, efficient and practical methods for core loss characterization are essential. This work presents a novel test bench concept that simplifies the handling of large sample quantities and inherently includes the influence of lamination stacking, without requiring each specimen to be individually wound as in traditional ring core methods. In addition, the setup allows the application of mechanical force aligned with the magnetic flux direction, making it possible to investigate stress related effects on magnetic behavior during operation. Outlined in detail is the complete design methodology, followed by measurement results that confirm the practical viability of the proposed system. With its compact and adaptable construction, the setup supports rapid testing using standard tools and common workshop equipment, offering a compelling alternative for evaluating iron losses in electrical machines under controlled conditions.