engineering design research paper

RED’s mission

The international journal Research in Engineering Design ( RED ) is one of the premier journals on the subject of design, with particular emphasis on engineering design.

Papers that appeared in RED since its founding in 1989 are cited continually by the research community; they make their impact on other researchers and eventually find their way into practice. This marks the leading role that the journal serves in the community as the place to find papers with lasting impact on engineering design. We strive to publish the best research work that is relevant for the present but that also makes a lasting impact.  

As a leading journal in engineering design, RED mission is to publish the highest quality papers that define what engineering design is.

This mission is translated into several activities:

• Continuously improve the quality of papers published in RED.
• Help foster better scholarship among engineering design researchers.
• Bring design into broader audience. This will help shape our position of what design is.

The journal scope is:

Research in Engineering Design is an international journal that publishes research papers on design theory and methods in all fields of engineering. The journal is designed for professionals in academia, industry and government interested in research issues relevant to design practice. Papers emphasize underlying principles of engineering design and discipline-oriented research where results are of interest or extendible to other engineering domains. General areas of interest include theories of design, foundations of design environments, representations and languages, models of design processes, and integration of design and life cycle issues. The journal also publishes state-of-the-art review articles.

As a journal, RED retains the characteristics of a classical academic journal whose mission is to accept papers, review and publish those that pass the review process in a timely manner. However, in order to foster dialogue between members of the engineering design research community, RED also publishes, letters to the editor and commentaries by leading experts (invited only). Other means of publication will be sought to support the mission of the journal including communication means outside the published pages of the journal.

The information accessed through this page is meant to help researchers submit quality papers that get accepted and make impact on subsequent research and practice of design.

(1)   RED Editorial Board

(2)   How to get your paper published? Guidelines for writing successful papers to RED (incomplete)

(3)   How to review papers? Guidelines for reviewing papers submitted to RED (incomplete)

(4)   RED review process

(5)   Special Issues policy

Journal Papers - Engineering Design Research Laboratory - Purdue University

Journal Papers

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Wu, T., J. Najmon, A. Tovar. Coupled Thermal-Fluid Topology Optimization Considering External Heat Flux. International Journal of Heat and Mass Transfer (Under review).

Sego, T.J., Y-T. Hsu, T-M. G. Chu, A. Tovar. Modeling Anisotropic Damage Accumulation in Bone Remodeling. Journal of Biomechanics (Under review).

Sego, T.J.; M. Prideaux, B. McCarthy, P. Li, L. Bonewald, B. Ekser, A. Tovar, Smith, L., Computational Fluid Dynamic Analysis of Bioprinted Self-Supporting Perfused (SSuPer) Tissue Models. Biotechnology and Bioengineering, https://doi.org/10.1002/bit.27238, 2019.

Liu, K, T. Wu, D. Detwiler, J. Panchal, A. Tovar. Design for crashworthiness of categorical multimaterial structures using cluster analysis and Bayesian optimization. ASME Journal of Mechanical Design, Special issue on Machine Learning, Vol.: 141, Issue: 12, Pages: 121701 (15 pages), https://doi.org/10.1115/1.4044838, 2019.

Wu, T. and A. Tovar. Multiscale, thermomechanical topology optimization of self-supporting cellular structures for porous injection molds. Rapid Prototyping Journal, Vol. 25, Issue 9, Pages: 1482-1492, https://doi.org/10.1108/RPJ-09-2017-0190, 2019.

Raeisi, S, J. Kadkhodapour, and A. Tovar. Mechanical properties and energy absorbing capabilities of Z-pinned aluminum foam sandwich. Journal of Sandwich Structures and Materials, Vol.: 214, Pages: 34-46, https://doi.org/10.1016/j.compstruct.2019.01.095, 2019.

Han, X., W. An, A. Tovar. Targeting the Force-Displacement Response of Thin-walled Structures Subjected to Crushing Load using Curve Decomposition and Topometry Optimization. Structural and Multidisciplinary Optimization, Vol.: 59, Issue: 6, Pages: 2303-2318, https://doi.org/10.1007/s00158-019-02197-8, 2019.

Arcos-Legarda, J., J.A. Cortes, A. Tovar. Robust Compound Control of Dynamic Bipedal Robots. Mechatronics, Vol. 59, Pages 154-167, https://doi.org/10.1016/j.mechatronics.2019.04.002, 2019.

Arcos-Legarda, J., J.A. Cortes, A. Beltran-Pulido, A. Tovar. Hybrid disturbance rejection control of dynamic bipedal robots. Multibody System Dynamics, Vol.: 46, Issue: 3, Pages: 281-306, https://doi.org/10.1007/s11044-019-09667-3, 2019.

Najmon, J., DeHart, J., Wood, Z., and A. Tovar., Development of a Helmet Liner through Bio-Inspired Structures and Topology Optimized Compliant Mechanism Arrays, SAE International Journal of Transportation Safety 6(3), https://doi.org/10.4271/2018-01-1057, 2018.

Liu, K., D. Detwiler, A. Tovar. Cluster-based optimization of cellular materials and structures for crashworthiness. ASME Journal of Mechanical Designs, special issue on Special Issue on Design of Engineered Materials and Structures, Vol. 140, Issue 11, Pages: 111412 (10 pages), https://doi.org/10.1115/1.4040960, 2018.

Sego, T.J., U. Kasacheuski, D. Hauersperger, A. Tovar, N.I. Moldovan. A Heuristic Computational Model of Basic Cellular Processes and Oxygenation during Spheroid-Dependent Biofabrication. Biofabrication, Vol. 9, Issue 2, Pages 024104, 2017.

Liu, K., D. Detwiler, A. Tovar. Optimal Design of Nonlinear Multimaterial Structures for Crashworthiness using Cluster Analysis. ASME Journal of Mechanical Design, Vol. 139, Issue 10, Pages 101401 (11 pages), doi: 10.1115/1.4037620, 2017.

Wu, T., K. Liu, A. Tovar. Multiphase Topology Optimization of Lattice Injection Molds. Computers & Structures, Vol. 192, Pages 71-82, https://doi.org/10.1016/j.compstruc.2017.07.007, 2017.

Jahan, S. A., T. Wu, Y. Zhang, J. Zhang, A. Tovar, H. El-Mounayri. Thermo-mechanical design optimization of conformal cooling channels using design of experiments approach. Procedia Manufacturing, Vol. 10, Pages 898-911, 2017.

Jahan, S. A., T. Wu, Y. Zhang, H. El-Mounayri, A. Tovar, J. Zhang, D. Acheson, R. Nalim, X. Guo, W. H. Lee. Implementation of Conformal Cooling and Topology Optimization in 3D Printed Stainless Steel Porous Structure Injection Molds. Procedia Manufacturing, Vol. 5, Pages 901-9015, 2016.

Wu, T., S.A. Jahan, P. Kumaar, A. Tovar, H. El-Mounayri, Y. Zhang, J. Zhang, D. Acheson, K. Brand, R. Nalim. A framework for optimizing the design of injection molds with conformal cooling for additive manufacturing. Procedia Manufacturing, Vol. 1, Pages: 404-415, doi:10.1016/j.promfg.2015.09.049, 2015

Bandi, P., D. Detwiler, J. Schmiedeler, and A. Tovar. Design of Progressively Folding Thin-Walled Tubular Components Using Compliant Mechanism Synthesis. Thin-Walled Structures, Vol. 37, Issue 2, Pages: 723-735, doi:10.1007/s40430-014-0197-0, 2015.

León, D., N. Arzola, and A. Tovar. Statistical analysis of the influence of tooth geometry in the performance of harmonic drive. Journal of the Brazilian Society of Mechanical Sciences and Engineering. Vol. 37, Pages: 723-735, 2015, doi:10.1007/s40430-014-0197-0, 2015.

Liu, K. and A. Tovar. An efficient 3D topology optimization code written in Matlab. Structural and Multidisciplinary Optimization, Vol. 50, Issue 6, Pages: 117-1196, 2014, doi:10.1007/s00158-014-1107-x, 2014.

Lee, S. and A. Tovar. Outrigger placement in tall buildings using topology optimization. Engineering Structures. Vol. 74, Issue 1, Pages: 122-129, doi:10.1016/j.engstruct.2014.05.019, 2014.

Bandi, P., J. Schmiedeler, and A. Tovar. Design of Crashworthy Structures with Controlled Energy Absorption in the HCA Framework. ASME Journal of Mechanical Design, Vol. 135, Issue 9, Pages 091002.1-091002.11, 2013.

Uribe, B., L.M. Méndez, A. Tovar, J.P. Charalambos, O. Arcila, and A.D. López. Mixed Reality Boundaries in Museum Preservation Areas. International Journal of Art, Culture and Design Technologies, Vol. 3, Issue 2, Pages: 63-74, 2013.

Shinde, S., P. Bandi, D. Detwiler, and A. Tovar. Structural Optimization of Thin-Walled Tubular Structures for Progressive Buckling Using Compliant Mechanism Approach. SAE International Journal of Passenger Cars – Mechanical Systems, Vol. 6, Issue 1, Pages: 109-120, 2013.

Tovar, A. and K. Khandelwal. Topology Optimization for Minimum Compliance using a Control Strategy. Engineering Structures, Vol. 48, Pages: 674-682, 2013.

Lee, S., and A. Tovar. Topology Optimization of Piezoelectric Energy Harvesting Skin using Hybrid Cellular Automata. ASME Journal of Mechanical Design, Vol. 135, Issue 3, Pages: 031001.1-031001.12, 2013.

Arcos, W.J. and A. Tovar. LQR optimal control of an exoskeleton for walking. Intekhnia, Vol. 2, Issue. 2, 2013.

Penninger, C.L. A. Tovar, V. Tomar, and J.E. Renaud. A high fidelity HCA model for bone adaptation with cellular rules for bone resorption. Journal of Surfaces and Interfaces of Materials, Vol. 1, Issue: 1, Pages: 60-70, 2013.

Yokota, H., A. Tovar, and A. Robling. Dynamic Muscle Loading and Mechanotransduction. BONE, Vol. 51, Issue 4, Pages 826-827, 2012.

Goetz, J.C., H. Tan, A. Tovar, and J.E. Renaud. Two-material structural topology optimization for blast mitigation using hybrid cellular automata. Engineering Optimization. Vol. 44, Issue 8, Pages 985-1005, 2012.

Mozumder, C., A. Tovar, and J.E. Renaud. Topometry optimization for crashworthiness design using hybrid cellular automata. International Journal of Vehicle Design, Vol. 60, Issue 1/2, Pages: 100-120, 2012.

Guo, L., J. Huang, A. Tovar, and J.E. Renaud. Multidomain Topology Optimization for Crashworthiness based on Hybrid Cellular Automata. Key Engineering Materials. Vol. 486, Pages 250-253, 2011.

Penninger, C.L., A. Tovar, L.T. Watson, and J.E. Renaud. KKT conditions satisfied using adaptive neighboring in hybrid cellular automata for topology optimization. International Journal of Pure and Applied Mathematics. Vol. 66, Issue 3, Pages 245-262, 2011.

Guo, L., A. Tovar, C.L. Penninger and J.E. Renaud. Strain-based topology optimization for crashworthiness using hybrid cellular automata. International Journal of Crashworthiness. Vol. 16, Issue 3, Pages 239-252, 2011.

Goetz, J.C., H. Tan, A. Tovar, J.E. Renaud. Optimization of One-Dimensional Aluminum Foam Armor Model for Pressure Loading, SAE International Journal of Materials and Manufacturing, Vol. 4, Issue 1, Pages 1138-1146, 2011.

Penninger, C.L., L.T. Watson, A. Tovar, and J.E. Renaud. Convergence Analysis of Hybrid Cellular Automata for Topology Optimization. Structural and Multidisciplinary Optimization. Vol. 40, Issue 1-6, Pages 271-282, 2010.

Galeano, C.H., C.A. Duque, and A. Tovar. Interactive Optimization Tool for the Optimum Design of Helical Extension Springs (in Spanish). Revista Técnica de la Facultad de Ingeniería Universidad del Zulia. Vol. 32, Issue 2, Pages 98-108, 2009.

Patel, N.M., B.S. Kang, J.E. Renaud, and A. Tovar. Crashworthiness design using topology optimization. ASME Journal of Mechanical Design. Vol. 131, Issue 6, Pages 061013.1-061013.12, 2009.

Vera, A. and A. Tovar. Computational study on the effect of microcracks, cellular aging and apoptosis in bone remodeling (in Spanish). Revista Ingeniería Biomédica. Vol. 2, Issue 4, Pages 73-83, 2008.

Patel, N.M., D. Tillotson, A. Tovar, K. Izui, and J.E. Renaud. A comparative study of topology optimization techniques. AIAA Journal. Vol. 46, Issue 8, Pages 1963-1975, 2008.

Penninger, C.L., N.M. Patel, G.L. Niebur, A. Tovar, and J.E. Renaud. A fully anisotropic hierarchical hybrid cellular automaton algorithm to simulate bone remodeling. Mechanics Research Communications. Vol. 35, Issue 1-2, Pages 32-42, 2008.

Arzola, N., A. Tovar, and A. Gómez. Retrofit and optimization of a steel-bar bending machine (in Spanish). Ingeniería y Competitividad, University of Valle. Vol. 9. Issue 2, Pages 7-19, 2007.

Tovar, A., N.M. Patel, A.K. Kaushik, and J.E. Renaud. Optimality Conditions of the Hybrid Cellular Automata for Structural Optimization. AIAA Journal. Vol. 45, Issue 3, Pages 673-683, 2007.

Tovar, A., N. Arzola and A. Gómez. Multidisciplinary Design Optimization Techniques (in Spanish). Ingeniería e Investigación, National University of Colombia. Vol, 7, Issue 1, Pages 84-92, 2007.

Tovar, A., N.M. Patel, G.L. Niebur, M. Sen, and J.E. Renaud. Topology Optimization Using a Hybrid Cellular Automaton Method with Local Control Rules. ASME Journal of Mechanical Design. Vol. 128, Issue 6, Pages 1205-1216, 2006.

Gano, S.E., J.E. Renaud, H. Agarwal, and A. Tovar. Reliability Based Design Using Variable Fidelity Optimization. Structure and Infrastructure Engineering. Vol. 2, Issue 3-4, Pages 247-260, 2005.

Tovar, A., Topology Optimization with the Hybrid Cellular Automaton Technique (in Spanish). Optimización Topológica con la Técnica de los Autómatas Celulares Híbridos. Revista Internacional de Métodos Numéricos para el Cálculo y Diseño en Ingeniería. Vol. 21, Issue 4, Pages 365-383, 2005.

Tovar, A., S.E. Gano, J.J. Mason, and J.E. Renaud. Optimum Design of an Interbody Implant for Lumbar Spine Fixation. Journal of Advances in Engineering Software. Special number in Design Optimization. Vol. 36, Issue 9, Pages 634-642, 2005.

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Artificial Intelligence and Engineering Design

Engineering design research themes, ai method themes, future opportunities for ai and engineering design research, special issue: artificial intelligence and engineering design.

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Allison, J. T., Cardin, M., McComb, C., Ren, M. Y., Selva, D., Tucker, C., Witherell, P., and Zhao, Y. F. (January 11, 2022). "Special Issue: Artificial Intelligence and Engineering Design." ASME. J. Mech. Des . February 2022; 144(2): 020301. https://doi.org/10.1115/1.4053111

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Artificial intelligence (AI) has had a strong presence in engineering design for decades, and while theory, methods, and tools for engineering design have advanced significantly during this time, many grand challenges remain. Modern advancements in AI, including new strategies for capturing, storing, and analyzing data, have the potential to revolutionize engineering design processes in a variety of ways. The purpose of this special issue is to consolidate recent research activities that utilize existing or new AI methods to advance engineering design knowledge and capabilities.

During the conception of this special issue, we identified three core interfaces between the research domains of engineering design and AI: (1) leveraging AI methods directly in engineering design methods, (2) creating new AI capabilities that are inspired by unique challenges that arise in engineering design, and (3) creating and analyzing design methods that are tailored for the design of engineering systems where the systems themselves utilize AI, such as autonomous vehicles. The diverse body of research articles that now comprise this special issue gravitate toward the first of these themes: advancing engineering design capability through the use of AI. While these articles are an exciting contribution to the design research literature, significant opportunities exist for more fully exploring the remaining two interfaces, ideally through more unified interdisciplinary efforts. During the process of synthesizing this editorial, we recognized a fourth interface between engineering design and AI: specifically, investigating how AI could be used as an increasingly powerful tool for conducting engineering design research, such as AI tools that are used directly in research activities (e.g., experiment planning or gathering information from human designers) and that are not necessarily part of the designed system or the design method.

Two sets of clear themes have emerged from this special issue. The first set is expressed from the perspective of engineering design research and design processes. The second set is organized in terms of AI methods. We have organized these themes in this way in part to facilitate clearer communication across AI and Engineering Design research communities and to enable the productive collaboration that is needed to address open questions. Later, we discuss the relationship between AI and engineering design and then articulate the two sets of themes found in this special issue. Finally, a vision is presented for advancing interdisciplinary research in this area, including an initial outline of promising research topics.

This special issue consolidates exciting new outcomes from recent research that focused on the application of modern AI methods to the creation and analysis of methods that advance engineering design capabilities. Design researchers have long recognized that AI tools can be used in many ways to advance how engineers perform design. For example, designers can now leverage AI to support more automatic and intelligent knowledge extraction and knowledge representation, support early design ideation, and even to discover design solutions to previously unsolved engineering problems. In addition, the capabilities of modern AI algorithms can enhance the efficacy of later-stage system design activities, such as those involving high-dimension and strongly interrelated detailed design decisions.

Tasks at early design stages, such as concept generation, have the potential to effect transformational change upon engineering system capabilities. Yet in practice, these early-stage tasks rely heavily upon designer experience and intuition. Human cognitive limits constrain such design processes, creating an opportunity for AI models to accelerate the ability of engineering to better meet the needs of humanity. Limitations of conventional methods and tools constrain the fidelity and scope of the design spaces that are tractable. AI methods are helping to solve problems where human intuition or human processing capabilities are insufficient, making it possible to expand the richness of design spaces that can be navigated successfully through AI support.

Some engineering functions may not even have known solutions, even via biological analogies; AI-based design strategies have the potential to aid in the discovery of design solutions to previously unrealized functions through nonobvious design configurations. Used as an identification tool, AI is enabling more efficient and higher-performance design solutions during design revision at the manufacturing and assembly level by screening and identifying part consolidation opportunities and multifunctional design potential.

More comprehensive investigation of the role of AI in engineering design methods and research appears to be reshaping how we think about design. AI methods that can transformatively restructure design spaces for various goals exist, such as more efficient navigation of design alternatives or insightful interrogation to extract design knowledge. Design spaces can be adaptable throughout design process stages. Less rigid formulations can produce more fluid design paradigms compared to established frameworks, such as design optimization. AI models can also serve as the design solution method without needing to be linked to iterative design optimization. Moreover, inverse design approaches fit well with AI tools, further bending our conception of design strategies. Publications at the interface of AI and engineering design have often been led by design research experts rather than AI experts, resulting in potential bias toward particular research questions and classes of AI methods. Approaching this interface from additional complementary directions could help us build a more complete understanding of how AI methods can further benefit society through advancements in engineering design.

Articles in this issue link to a diverse set of approaches used in AI and utilize various neural network architectures to develop surrogate models. These surrogate models typically learn abstract representations to understand the underlying processes, which can be then explained to gain further insight. Several approaches also assist the designer through human augmentation and increased automation, while also expanding design spaces through transfer learning. These approaches are applied to many aspects of the design process including systems design, conceptual design, generative design, and several detailed design applications in areas including supply chains, medical devices, and aeronautics.

Readers may be keenly aware that the guest editors have not attempted to define AI. While some excellent definitions exist, conflicts in (sometimes evolving) AI definitions held by such a diverse set of potential authors are likely. We opted to consider submissions that utilized AI as defined by the authors, as long as the definition is justifiable, and if the submission makes a notable contribution to the body of engineering design knowledge. As a result of this inclusive approach, some readers may not consider some methods appearing in this issue to be AI. The definition of AI will not be settled here; rather, we hope that one of the primary contributions of this special issue is a helpful step toward more successful integration of AI and engineering design research.

This special issue incorporates a variety of topics that represent the state of the art in how AI is changing how we do engineering design today. Articles solicited represent intellectual contributions to engineering design research at the intersection of unmet engineering challenges, advances in AI, and design-relevant contexts. Articles published in this issue fall largely within the general area of AI being utilized as an enabling tool for advancing engineering design capabilities. In particular, many articles focus on enhancing early-stage design capabilities. Previous special issues exist for related topics, including Machine Learning for Engineering Design and Data-Driven Design . This special issue has provided an opportunity to publish design research linking to the broad area of AI that may not have fit well with these previous special issues. For example, one article (Gyory et al.) utilizes an AI agent to perform dynamic design team management based on predefined logical rules.

The guest editors have identified significant themes that have emerged from this collection of articles, organized first into topics that are presented from an engineering design research perspective and second into topics that are articulated from an AI method perspective. Please note that the distinctions between these themes can be fuzzy; some studies lie at the intersection between two or more themes. The following two sections introduce these themes and point readers to relevant articles in this special issue. This editorial then concludes with remarks on open research questions at the interface of AI and engineering design.

When viewed through the lens of engineering design research, four broad themes emerge from this special issue, each of which is discussed here.

Design Theme 1: Conceptual Design and Design Synthesis. Several articles involve advances in utilizing AI for more expansive and comprehensive yet practical exploration of complex design configuration spaces, including topological changes, design synthesis, and support for enhanced human creativity. Methods are either automated or involve a partnership between AI and human designers. More conventional design paradigms induce a different constraining of design spaces than might be possible with this class of AI-based design methods. Natural language processing (NLP) is utilized in several studies presented here as a key interface with the human creative process (T. Chen et al., Han et al., and Lee et al.). Two survey papers also provide context and future directions for utilizing the AI-based methods for this and other design needs (Han et al. and Jiang et al.). The article by Song et al. presents a study of the influence that AI tools have on strategies followed by designers during engineering design processes. As noted earlier, several articles link to two or more themes. For example, Behzadi et al., Q. Chen et al., Nobari et al., Quigley et al., and Raina et al. all link to both methods for conceptual design and Design Theme 2: Accelerating Design Processes.

Design Theme 2: Accelerating Design Processes. Multiple contributions in this special issue utilize AI tools to reduce the time, computational iterations, or other resources required to obtain a good design solution. Two key AI-based strategies employed to accelerate iterative design processes include (1) transfer and representation learning (Behzadi et al., Herzog et al., and Whalen et al.) and (2) invertible neural networks for optimization (Ghosh et al. and Oddiraju et al.). While many current AI-related research efforts outside of engineering design research focus on training models from scratch for maximum accuracy with large datasets, many articles in this special issue (and other design research publications) gravitate toward pretrained networks, such as used in transfer learning, to enhance accuracy with limited datasets (Ferrero et al. and Yuan et al.). This observation links to the Transfer Learning AI theme discussed later. In addition, AI methods that better support generation of design solutions that are substantially distinct from previous designs, such as transfer learning methods that are successful in design domains that are more distant from the training data, may be particularly valuable.

Design Theme 3: AI-Based Direct Estimation and Tuning. In addition to accelerating iterative design processes, some methods proposed AI-based tools that reduce or eliminate the need for iterative design. Articles by Burge et al., Dachowicz et al., and Li et al. used neural networks to design solutions for given initial conditions. The article by Raina et al. used learning by demonstration to provide design solutions, and the article by Caputo et al. utilized deep reinforcement learning to achieve adaptive responses to changing environmental conditions without the need for any new design iterations.

Design Theme 4: Broader Design Process Support. A few articles make broader contributions that cannot be strictly classified along a specific theme mentioned earlier. The article by Wang et al. makes a theoretical contribution involving the use of Gaussian processes in a scalable manner to represent complex spaces. The article by Kim et al. utilizes AI-based tools to perform sentiment analysis on product listings and how they evolve. The article by Gyory et al. proposes and tests an AI-based team management system against a human-based management system; an overall increase in productivity and content is realized through the AI system. The article by Caputo et al. proposes a new approach to conceptualize real options and flexibility analysis in engineering systems design, building upon principles from deep reinforcement learning. This supports the view that more work is needed to develop new AI and data-driven methods to design complex engineered systems so that they can better deal with uncertainty and risks—which is much needed, especially given ongoing threats from climate change and global healthcare emergencies.

When viewed from the perspective of identifying specific AI methods that are shown to be useful for advancing engineering design capabilities, five themes materialize. An exposition of these themes is provided below.

AI Theme 1: Natural Language Processing. Natural language processing has been used in several papers included in this special issue. NLP serves as a human-interpretable way to interact with many AI-based systems, and so improvements to NLP methods can benefit the tools adapted to engineering design problems (T. Chen et al., Han et al., Kim et al., Lee et al., and Yuan et al.).

AI Theme 2: Graph (Neural) Networks . Many design representations may be represented as graphs (e.g., assemblies (Ferrero et al.) and structures (Whalen et al.)). In future work, it would be beneficial to see more studies that address remaining challenges in applying graph networks to engineering design, such as invariance/equivariance properties, explainability, and transferability.

AI Theme 3: Generative Models. Use of generative models continues as a demonstrated AI-based design strategy (Behzadi et al., Q. Chen et al., Nobari et al., Oddiraju et al., Quigley et al., Yuan et al.); one potential benefit is the ability for these models to produce solutions without iterative optimization. Within the set of articles utilizing generative models, articles by Behzadi et al. and Nobari et al. address the challenges with small data set sizes, which is an important characteristic of many data-driven design strategies. To enhance what can be done using small data sets, the article by Nobari et al. introduces a label-aware self-augmentation training approach, and the article by Behzadi et al. uses knowledge transfer.

AI Theme 4: Transfer Learning . Transfer learning is emerging as an important tool for dealing effectively with the small data sets that are common in engineering design. The article by Whalen et al. explores the transferability of a learned graph network on a variety of structural design problems. The article by Herzog et al. presents a study of the effectiveness of knowledge transfer in the context of semi-supervised learning of labels on 3D geometries.

AI Theme 5: Representation Learning. The article by Raina et al. introduces new ways of representing design policies (e.g., sequences of design decisions) when the action space structurally changes during the design process. Gaussian process (GP) models that learn latent categorical representations are presented in the article by Wang et al., and the scalability issue of GP models is investigated.

This special issue offers a partial snapshot of the current research results in AI and engineering design. The previous sections summarize some aspects that were addressed in the special issue; here, we articulate what appears to be missing, and what could serve as the basis for rich future collaborations in interdisciplinary research between AI and engineering design researchers. We identify both AI methods that need further study as elements in engineering design methods, as well as ways AI could be addressed differently in the context of engineering design (e.g., new AI contributions motivated by design, design of AI-based systems, and AI methods as tools in engineering design research).

The collection of articles utilizes several well-established model-free AI methods for the purpose of accelerating engineering design processes, but leaves open the opportunity to investigate the use of model-based learning techniques in these contexts. This may be because most of the applications studied here do not have clear associated dynamics. The solutions obtained from these methods can be used not only as solutions to engineering design problems but also model-based strategies that leverage assumptions about the form of the system being modeled (e.g., physics-based phenomena or distributions) could lead to a deeper qualitative design understanding. Model- and dynamics-based learning methods could be exciting and insightful avenues for engineering design research.

Several articles in this issue utilize pretrained networks, which are either adapted to identify/classify design models or to develop a framework for explaining engineering design related processes. While the pretraining approach has been discussed in AI under the names of transfer and curriculum learning, its deeper understanding is particularly important to engineering design due to frequent scenarios where data are limited or expensive, e.g., during the exploration of new material systems. In such scenarios, it would be valuable to be able to learn effective representations, rules, or solution search strategies from one set of design problems and apply that knowledge to a different (or more complicated) set of problems. While empirical evidence has been reported where such transfer has potential, we expect deeper investigation into the theoretical conditions for certifiable performance of pretrained models, and more comprehensive ontologies that define knowledge transfer within the broad context of engineering design. The tendency toward pretraining approaches in engineering design methods exemplifies the deeper underlying needs that are unique to engineering design and motivate additional fundamental AI investigations. This observation is representative of the second interface between AI and engineering design research, as identified in the second paragraph of this editorial, and is an opportunity for productive interdisciplinary discussion.

An interesting future direction for this effort would be to solve engineering design problems using both the pretraining and from scratch strategies for neural networks and then to compare the abstractions learned using both approaches. It would also be valuable to evaluate hybrid methods, where certain layers are allowed to mutate to adapt to the given problem. Alternately, networks trained from one paradigm can be used as the initialization for the other, and optimal abstractions can be learned for the given problem sets. As observed earlier, many articles in this issue concentrated on new AI-enabled design methods to better support early-stage design tasks. Research topics that are notably absent from a design methods research perspective include those that investigate AI-based methods intended for more comprehensive late-stage design (e.g., AI agents that aid automation of fully detailed design specifications required for the production).

In addition to the outputs of AI research benefitting design research, the converse is also possible. For example, tools from current engineering design research, such as design automation methods, could be used to rapidly generate rich and tailored data sets that amplify the impact of AI methods. Furthermore, engineering design challenges and knowledge from design research could help inform new advancements in AI, presenting a new opportunity for interdisciplinary collaboration and motivation for enhanced communication across research disciplines. Another class of open research questions involves the creation of design methods that are tailored to meet the needs that are unique to design systems where AI is part of the system being designed and not necessarily part of the design method. Finally, AI methods could be used as a tool in engineering design research, which is distinct from use as part of a design method. For example, many design research studies involve human components; AI could play a significant role as a research tool in interfacing with human designers, users of designed systems, and other stakeholders. It could also transform planning of research experiments and data collection.

Addressing these questions is distinct from fundamental advances in AI theory and algorithms alone; generation of new knowledge that is relevant to engineering design and that links to AI requires scientific rigor in both domains and motivates deeper collaboration between design research and AI research experts. Completely new AI concepts may take engineering design capabilities in unexpected directions, and vice versa. Many important open questions remain, and only a few have been highlighted here. We encourage readers to apply their own unique perspective and expertise to distill and identify additional scientific questions at the intersection of AI and engineering design research, especially those that may benefit from deep interdisciplinary collaboration. We hope to work collectively toward more impactful research at the interface of AI and engineering design. Achieving this will require extensive future efforts. Existing work does involve independent forays into adjacent disciplines (e.g., design researchers gaining AI expertise), but, on the whole, studies at this interface are still performed in a largely siloed manner. Perhaps the fundamental differences in language, perspectives, and goals between the AI and engineering design research communities add to the underlying difficulties. A significant investment across disciplines would be required to realize this vision of intrinsic interdisciplinarity for research at the interface of AI and engineering design.

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Topology optimization with explicit components considering stress constraints.

1. Introduction

2. mmc topology optimization framework and geometrical description, 2.1. mmc topology optimization method, 2.2. a new topology description function, 3. problem formulation, 3.1. problem statement and mathematical formulation, 3.2. global stress control, 4. sensitivity analysis, 5. numerical solution aspects, 5.1. l-shaped beam, 5.2. t-shaped beam, 6. conclusions, author contributions, institutional review board statement, informed consent statement, data availability statement, conflicts of interest, abbreviations.

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Share and Cite

Ma, Y.; Li, Z.; Wei, Y.; Yang, K. Topology Optimization with Explicit Components Considering Stress Constraints. Appl. Sci. 2024 , 14 , 7171. https://doi.org/10.3390/app14167171

Ma Y, Li Z, Wei Y, Yang K. Topology Optimization with Explicit Components Considering Stress Constraints. Applied Sciences . 2024; 14(16):7171. https://doi.org/10.3390/app14167171

Ma, Yubao, Zhiguo Li, Yuxuan Wei, and Kai Yang. 2024. "Topology Optimization with Explicit Components Considering Stress Constraints" Applied Sciences 14, no. 16: 7171. https://doi.org/10.3390/app14167171

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BYU: Engineering research finds key to quicker nuclear power — artificial intelligence

By todd hollingshead - special to the daily herald | aug 10, 2024.

Courtesy BYU Photo

A Brigham Young University professor has figured out a way to shave critical years off the complicated design and licensing processes for modern nuclear reactors: artificial intelligence.

You heard that right, AI is teaming up with nuclear power. And while that may seem like a worrisome bit straight out of a science fiction movie, chemical engineering professor Matt Memmott says it’s not what it sounds like; no one is giving AI the nuclear codes. It’s all about speeding up the process to get more nuclear power online.

The typical time frame and cost to license a new nuclear reactor design in the United States is roughly 20 years and $1 billion. To then build that reactor requires an additional five years and between $5 billion and $30 billion. By using AI in the time-consuming computational design process, Memmott estimates a decade or more could be cut off the overall timeline, saving millions and millions of dollars in the process — which should prove critical given the nation’s looming energy needs.

“Our demand for electricity is going to skyrocket in years to come and we need to figure out how to produce additional power quickly,” Memmott said. “The only baseload power we can make in the gigawatt quantities needed that is completely emissions free is nuclear power. Being able to reduce the time and cost to produce and license nuclear reactors will make that power cheaper and a more viable option for environmentally friendly power to meet the future demand.”

Designing and building a nuclear reactor is complex and time consuming because it requires multi-scale efforts, according to Memmott. Engineers deal with elements from neutrons on the quantum scale all the way up to coolant flow and heat transfer on the macro scale. He also said there are multiple layers of physics that are “tightly coupled” in that process: The movement of neutrons is tightly coupled to the heat transfer, which is tightly coupled to materials, which is tightly coupled to the corrosion, which is coupled to the coolant flow.

“A lot of these reactor design problems are so massive and involve so much data that it takes months of teams of people working together to resolve the issues,” he said. “When I was at Westinghouse, it took the team of neutron guys six months just to run one of their complete-core multiphysics models. And if they made a mistake two months in, then they just wasted two months of the valuable computational time and they would have to start over.”

Memmott is finding AI can reduce that heavy time burden and lead to more power production to not only meet rising demands but also to keep power costs down for general consumers. In recent years, homeowners and renters nationwide have felt the sting of rising utility costs.

Technically speaking, Memmott’s research proves the concept of replacing a portion of the required thermal hydraulic and neutronics simulations with a trained machine learning model to predict temperature profiles based on geometric reactor parameters that are variable and then optimizing those parameters. The result would create an optimal nuclear reactor design at a fraction of the computational expense required by traditional design methods.

For his research, he and BYU colleagues built a dozen machine learning algorithms to examine their ability to process the simulated data needed in designing a reactor. They identified the top three algorithms, then refined the parameters until they found one that worked really well and could handle a preliminary data set as a proof of concept. It worked (and they published a paper on it), so they took the model and (for a second paper) put it to the test on a very difficult nuclear design problem: optimal nuclear shield design.

The resulting papers, recently published in academic journal Nuclear Engineering and Design, showed that their refined model can geometrically optimize the design elements much faster than the traditional method. For example, it took Memmott’s AI algorithm just two days to come up with an optimal shield design for a nuclear reactor while local molten salt reactor company Alpha Tech Research Corp. took six months to do the same.

“When you look at nuclear reactor design, you have this huge design space of possibilities — it’s as if you have people combing through this mile-wide area looking for the right reactor design,” Memmott said. “Now AI can help those people focus on that little quarter-sized sweet spot of design which will drastically reduce the search time. Of course, humans still ultimately make the final design decisions and carry out all the safety assessments, but it saves a significant amount of time at the front end.”

Fellow BYU researchers include Andrew Larsen, Ross Lee, Braden Clayton, Edwards Mercado, Ethan Wright, Brent Edgerton, Brian Gonda and chemical engineering professor John Hedengren. Collaborators from Alpha Tech, Caden Wilson and John Benson, also contributed their efforts to the research.

Todd Hollingshead is the media relations manager for University Communications at BYU.

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