Abstract: In the past two decades, there has been increasing use of semantic networks in engineering design for supporting various activities, such as knowledge extraction, prior art search, idea generation and evaluation. Leveraging large-scale pre-trained graph knowledge databases to support engineering design-related natural language processing (NLP) tasks has attracted a growing interest in the engineering design research community. Therefore, this paper aims to provide a survey of the state-of-the-art semantic networks for engineering design and propositions of future research to build and utilize large-scale semantic networks as knowledge bases to support engineering design research and practice. The survey shows that WordNet, ConceptNet and other semantic networks, which contain common-sense knowledge or are trained on non-engineering data sources, are primarily used by engineering design researchers to develop methods and tools. Meanwhile, there are emerging efforts in constructing engineering and technical-contextualized semantic network databases, such as B-Link and TechNet, through retrieving data from technical data sources and employing unsupervised machine learning approaches. On this basis, we recommend six strategic future research directions to advance the development and uses of large-scale semantic networks for artificial intelligence applications in engineering design.

Abstract: The advent of Additive Manufacturing (AM) is uncovering the limits of the current CAD systems and, at the same time, is highlighting the potentials of the Topology Optimization (TO) and Generative Design (GD) tools that had not been fully exploited until now. Differently from the traditional design approach in which designers occupy a predominant role in each stage of the design process, the introduction of such tools in the product development process pushes toward simulation-driven design approaches which imply a significant change in the role of the designer. To this end, the paper presents a comparison of two different design methods for Additive Manufacturing based on the adoption of TO and GD tools. The comparison aims to offer a reflection on the evolution of the traditional approach when TO and GD tools are used, and to highlight the potential and limitations of these optimization tools when adopted in an integrated manner with the CAD systems. Furthermore, this comparative study can be a useful and practical source for designers to identify the most appropriate approach to adopt based on their needs and project resources. The comparative study is carried out through the design study of a prototype of a rocker arm and a brake pedal for the Formula Student race car. Their results, compared in terms of mechanical performances, show that both TO and especially GD tools can be efficiently adopted early in a design process oriented to AM to redesign components to make them lighter and stronger.

Abstract: Managing the design process of teams has been shown to considerably improve problem-solving behaviors and resulting final outcomes. Automating this activity presents significant opportunities in delivering interventions that dynamically adapt to the state of a team in order to reap the most impact. In this work, an artificial intelligence (AI) agent is created to manage the design process of engineering teams in real time, tracking features of teams’ actions and communications during a complex design and path-planning task in multidisciplinary teams. Teams are also placed under the guidance of human process managers for comparison. Regarding outcomes, teams perform equally as well under both types of management, with trends toward even superior performance from the AI-managed teams. The managers’ intervention strategies and team perceptions of those strategies are also explored, illuminating some intriguing similarities. Both the AI and human process managers focus largely on communication-based interventions, though differences start to emerge in the distribution of interventions across team roles. Furthermore, team members perceive the interventions from both the AI and human manager as equally relevant and helpful, and believe the AI agent to be just as sensitive to the needs of the team. Thus, the overall results show that the AI manager agent introduced in this work is able to match the capabilities of humans, showing potential in automating the management of a complex design process.

Abstract: Due to product individualization, customization and rapid technological advances in manufacturing, production systems are faced with frequent reconfiguration and expansion. Industrial buildings that allow changing production scenarios require flexible load-bearing structures and a coherent planning of the production layout and building systems. Yet, current production planning and structural building design are mostly sequential and the data and models lack interoperability. In this paper, a novel parametric evolutionary design method for automated production layout generation and optimization (PLGO) is presented, producing layout scenarios to be respected in structural building design. Results of a state-of-the-art analysis and a case study are combined to develop a novel concept of integrated production cubes and the design space for PLGO as basis for a parametric production layout design method. The integrated production cubes concept is then translated into a parametric PLGO framework, which is tested on a pilot-project of a hygiene production facility to evaluate the framework and validate the defined constraints and objectives. Results suggest that our framework can produce feasible production layout scenarios which respect flexibility and building requirements. In future research the design process will be extended by the development of a multi-objective evolutionary optimization process for industrial buildings to provide flexible building solutions that can accommodate a selection of several prioritized production layouts.

Abstract: The design and operation of systems are conventionally viewed as a sequential decision-making process that is informed by data from physical experiments and simulations. However, the integration of these high-dimensional and heterogeneous data sources requires the consideration of the impact of a decision on a system’s remaining life cycle. Consequently, this introduces a degree of complexity that in most cases can only be solved through a simplified decision-making approach. In this perspective paper, we use the digital twin concept to formulate an integrated perspective for the design of systems. Specifically, we show how the digital twin concept enables the integration of system design decisions and operational decisions during each stage of a system’s life cycle. This perspective has two advantages: (i) improved system performance as more effective decisions can be made, and (ii) improved data efficiency as it provides a framework to utilize data from multiple sources and design instances. The novelty in the presented perspective is that it necessitates an approach that enables fleet-level (i.e., decisions that influence a plurality of systems) and system-level decisions. From a formal definition, we identify a set of eight capabilities that are vital constructs to bring about the potential, as defined in this paper, that the digital twin concept holds for the design of systems. Subsequently, by comparing these capabilities with the available literature on digital twins, we identify research questions and forecast their broader impact. By conceptualizing the potential that the digital twin concept holds for the design of systems, we hope to contribute to the convergence of definitions, problem formulations, research gaps, and value propositions in this burgeoning field. Addressing the research questions, associated with the digital twin-inspired formulation for the design of systems, will bring about more advanced systems that can meet some of the societies’ grand challenges.

Abstract: Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been developed for design in domains with partial available knowledge about the underlying process. This article focuses on a powerful class of model-based experimental design called the mean objective cost of uncertainty (MOCU). The MOCU-based techniques are objective-based, meaning that they take the main objective of the process into account during the experimental design process. However, the lack of scalability of MOCU-based techniques prevents their application to most practical problems, including large discrete or combinatorial spaces. To achieve a scalable objective-based experimental design, this article proposes a graph-based MOCU-based Bayesian optimization framework. The correlations among samples in the large design space are accounted for using a graph-based Gaussian process, and an efficient closed-form sequential selection is achieved through the well-known expected improvement policy. The proposed framework's performance is assessed through the structural intervention in gene regulatory networks, aiming to make the network away from the states associated with cancer.

Abstract: In recent decades, metaheuristics have proven their effectiveness in solving large-scale real-world problems with multiple objectives. However, we still need to design robust algorithms capable of converging and approximating efficiently the true Pareto set. In this paper, we extend the recently Manta Ray foraging optimization (MOMRFO) to the multiobjective case. MOMRFO uses a population archive to store the non-dominated solutions generated so far by the exploration process. The leader’s solutions are selected from the population archive to guide the Manta Rays population towards promising search regions. We use crowding distance and e -dominance to provide a good compromise between diversity and convergence of the obtained potential Pareto set. The proposed algorithm is validated on five bi-objective test functions, seven three objective test functions, and is applied to structural design problems such as four-bar truss design, speed reduced design, welded beam design, and disk brake design. The algorithm is compared with four well-known multi-objective meta-heuristics. The experimental results show that the MOMRFO algorithm outperforms against the selected multiobjective meta-heuristics by providing better convergence behaviour with a better diversity of solutions.

Abstract: Designing is a problem-solving activity. The process is usually iterative: a solution is proposed, then analysed and tested until it satisfies all constraints and best fulfils the criteria. Usually, a designer proposes a solution based on intuition, experience, and knowledge. However, this does not work for problems they are facing for the first time. An alternative approach is generative design, where the designer focuses on iteratively defining a problem with its constraints and criteria in the form of a parametric computational model, and then leaves the search for the solution to the algorithms and their ability to rapidly generate and test several alternatives. The result of this approach is not only a set of solutions embedding implicitly the knowledge but also a model where problem-defining knowledge is quite explicit. The idea of the proposed approach is the exploitation of synergies between the designer and the algorithms. The designer focuses on problem definition and the algorithm focuses on finding a solution, showing that the capacity of the generative approach to replace the designer is limited. In the paper, we first present the framework of generative design, then apply the process to a case study of designing an efficient shading solution, and in the end, we present the results and compare them with the traditional approach. The approach is general and can be applied in other areas of engineering. It is relevant both to designers as well as software developers who are expected to take this approach further. More theoretical work is needed to study problem definitions as a form of knowledge representation in engineering.

Abstract: Abstract A flexible problem-specific multiscale topology optimization is introduced to associate different areas of the design domain with diverse microstructures extracted from a dictionary of optimized unit cells. The generation of the dictionary is carried out by exploiting micro-SIMP with AnisoTropic mesh adaptivitY (microSIMPATY) algorithm, which promotes the design of free-form layouts. The proposed methodology is particularized in a proof-of-concept setting for the design of orthotic devices for the treatment of foot diseases. Different patient-specific settings drive the prototyping of customized insoles, which are numerically verified and successively validated in terms of mechanical performances and manufacturability.

Abstract: Off-site construction (OSC) offers a promising means to improve the efficiency of construction projects. However, the lack of experience and knowledge regarding its use results in errors in design owing to conflicts and omissions of considerations for OSC projects. To mitigate these problems, the design for manufacturing and assembly (DfMA) is widely used to include the considerations in the OSC design process. Several studies concerning the DfMA application in OSC have been performed, but the comprehensive design process is not suggested for mitigating the aforementioned problems. This study proposes an OSC design process by integrating the fragmented DfMA considerations reported in previous studies. The considerations are identified through a systematic literature review and classified into structural and architectural types. To validate the proposed process, an OSC project design has been undertaken as a case study, wherein a significant portion of the building structure has been modified to comprise precast concrete (PC), instead of its reinforced counterpart, with a demonstrated reduction in the PC element design duration. The proposed process would guide and support the design process for reduction in the duration and errors incurred in the process. Moreover, the process can be considered a design guideline for the execution of future projects.

Abstract: Abstract In this work, we study the potential of using kriging metamodelling to perform multi-objective structural design optimization using finite element analysis software and design standards while keeping the computational efforts low. A method is proposed, which includes sustainability and buildability objectives, and it is applied to a case study of reinforced concrete foundations for wind turbines based on data from a large Swedish wind farm project. Sensitivity analyses are conducted to investigate the influence of the penalty factor applied to unfeasible solutions and the size of the initial sample generated by Latin hypercube sampling. A multi-objective optimization is then performed to obtain the optimum designs for different weight combinations for the four objectives considered. Results show that the kriging-obtained designs from samples of 20 designs outperform the best designs in the samples of 1000 designs. The optimum designs obtained by the proposed method have a sustainability impact 8–15% lower than the designs developed by traditional methods.

Abstract: The introduction of the ‘Safe Return to Port’ (SRtP) regulations strongly impacted the design of passenger ships. To meet the functional requirements of these regulations, the systems on board reached an extreme level of complexity in terms of redundancy and segregation, considerably increasing the difficulties to assess the compliance of the designs. However, non-compliant designs are a major risk for every design company which must prevent at all costs the possibility of expensive re-designs in later stages of the process. The aim of this research is to address the gap in the literature and in the market of design support tools, presenting a method to effectively mitigate the risks of non-compliant solutions with SRtP. The method comprises a thorough analysis of the spaces on board and a software tool for the assessment of the correct placement of the systems components. The value of the solution proposed was assessed in two case studies in which the method has been proven capable of effectively identifying the non-compliant solutions in a convenient and time-saving manner. Additional features for the suggestion of solutions to achieve the compliance have been implemented in the tool to further support designers during the complex design process of SRtP projects.

Abstract: Abstract In this work, we study the potential of using kriging metamodelling to perform multi-objective structural design optimization using finite element analysis software and design standards while keeping the computational efforts low. A method is proposed, which includes sustainability and buildability objectives, and it is applied to a case study of reinforced concrete foundations for wind turbines based on data from a large Swedish wind farm project. Sensitivity analyses are conducted to investigate the influence of the penalty factor applied to unfeasible solutions and the size of the initial sample generated by Latin hypercube sampling. A multi-objective optimization is then performed to obtain the optimum designs for different weight combinations for the four objectives considered. Results show that the kriging-obtained designs from samples of 20 designs outperform the best designs in the samples of 1000 designs. The optimum designs obtained by the proposed method have a sustainability impact 8–15% lower than the designs developed by traditional methods.

Abstract: Conceptual design is the foundational stage of a design process that translates ill-defined design problems into low-fidelity design concepts and prototypes. While deep learning approaches are widely applied in later design stages for design automation, we see fewer attempts in conceptual design for three reasons: 1) the data in this stage exhibit multiple modalities: natural language, sketches, and 3D shapes, and these modalities are challenging to represent in deep learning methods; 2) it requires knowledge from a larger source of inspiration instead of focusing on a single design task; and 3) it requires translating designers' intent and feedback, and hence needs more interaction with humans, either designers or users. With recent advances in deep learning of cross-modal tasks (DLCMT) and the availability of large cross-modal datasets, we see opportunities to apply these learning-based methods to the conceptual design of product shapes. In this paper, we conduct a systematic review on the methods for DLCMT that involve three design modalities: natural language, sketches, and 3D shapes, which revealed 50 articles in the fields of computer graphics, computer vision, and engineering design. This review work identifies the key challenges and opportunities in applying DLCMT in the conceptual design of engineered products. The authors also propose a list of five research questions that point to future directions and call on the community to devote itself to principled research investigations that help translate knowledge from computer science to engineering design.

Abstract: Abstract Feedback is essential in the design and development process, occurring in the generation of new designs, in the adaptation of development projects to emerging information, and in coordination and collaboration of project participants—among many other aspects. Feedback also contributes to development project complexity and may cause resistance to desirable changes. But despite the importance of feedback in the design and development process (DDP), relatively few publications have examined this topic in an integrated way. This article makes two contributions towards addressing the gap. First, a conceptual framework is developed to organise perspectives on feedback in the DDP literature. The framework shows how feedback occurs at different levels of the design and development process and how it affects important DDP behaviours, namely goal-seeking, learning and emergence. Second, a system-theoretic model of feedback situations in the design and development process is introduced to synthesise key ideas. We provide concrete examples to show how this new model can be used to frame DDP situations and draw out feedback-related insight.

Abstract: Abstract An intelligent manufacturing paradigm requires material systems, manufacturing systems, and design engineering to be better connected. Surrogate models are used to couple product-design choices with manufacturing process variables and material systems, hence, to connect and capture knowledge and embed intelligence in the system. Later, optimisation-driven design provides the ability to enhance the human cognitive abilities in decision-making in complex systems. This research proposes a multidisciplinary design optimisation problem to explore and exploit the interactions between different engineering disciplines using a socket prosthetic device as a case study. The originality of this research is in the conceptualisation of a computer-aided expert system capable of exploring process–structure–property–performance linkages in digital manufacturing. Thus, trade-off exploration and optimisation are enabled of competing objectives, including prosthetic socket mass, manufacturing time, and performance-tailored socket stiffness for patient comfort. The material system is modelled by experimental characterisation—the manufacturing time by computer simulations, and the product-design subsystem is simulated using a finite element analysis (FEA) surrogate model. We used polynomial surface response-based surrogate models and a Bayesian Network for design space exploration at the embodiment design stage. Next, at detail design, a gradient descent algorithm-based optimisation exploits the results using desirability functions to isolate Pareto non-dominated solutions. This work demonstrates how advanced engineering design synthesis methods can enhance designers’ cognitive ability to explore and exploit multiple disciplines concurrently and improve overall system performance, thus paving the way for the next generation of computer systems with highly intertwined material, digital design and manufacturing workflows. Graphical abstract

Abstract: Tire tread patterns have played an important role in the automotive industry because they directly affect automobile performances. The conventional tread pattern development process has successfully produced and manufactured many tire tread patterns. However, a conceptual design process, which is a major part of the whole process, is still time-consuming due to repetitive manual interaction works between designers and engineers. In the worst case, the whole design process must be performed again from the beginning to obtain the required results. In this study, a deep generative tread pattern design framework is proposed to automatically generate various tread patterns satisfying the target tire performances in the conceptual design process. The main concept of the proposed method is that desired tread patterns are obtained through optimization based on integrated functions, which combine generative models and tire performance evaluation functions. To strengthen the effectiveness of the proposed framework, suitable image pre-processing, generative adversarial networks (GANs), 2D image-based tire performance evaluation functions, design generation, design exploration, and image post-processing methods are proposed with the help of domain knowledge of the tread pattern. The numerical results show that the proposed automatic design framework successfully creates various tread patterns satisfying the target tire performances such as summer, winter, or all-season patterns.

Abstract: In this work, we have developed a data-driven artificial intelligence (AI) solution to assist the ship hull design process. Specifically, we have developed and implemented an AI-based multi-input neural network (MINN) model to realize a real-time prediction of the total resistance of the ship hull structure while avoiding the inconsistent estimates from different types of design input parameters. It is demonstrated that the developed AI-based machine learning algorithm as a prediction tool can assist the ship hull design process by accurately providing the total resistance of ship hulls in real-time. Moreover, we have conducted design tasks to validate the pro-posed method, and the validation results show that a well-trained artificial neural network (ANN) model can avoid the problem of different sensitivities due to the different degrees of influence of the input parameters on the output parameter. The proposed AI-based data-driven solution provides a real-time hydrodynamic performance calculation, which can predict the hyperdynamic performances of ship hulls based on their geometry modification parameters. This approach gives a consistent prediction in terms of accuracy when facing different geometry modification parameters, and it in turn provides a fast and accurate AI-based method to assist ship hull design to achieve an optimum forecast accuracy in the entire design space, making an advance to artificial intelligence assist design (AIAD) in naval architecture engineering.