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Developments in artificial intelligence (AI) are opening the possibilities for the development of more advanced design tools. An example of these innovations are generative design tools, in which the generation of complex and high performing products is possible. This study investigates the use of generative design tools and how they may influence the design process and designer behaviour. Six interviews of interdisciplinary designers were conducted to understand the implications of using generative design tools. It was observed that generative design tools primarily allow for quantitative inputs to the tool while qualitative metrics, such as aesthetics, are considered indirectly by designers. The subjectivity of the designer and how they incorporate the quantitative and qualitative metrics in the generative design tool can lead to differing outcomes between designers. Notable differences in tool usage are also observed between expert and novice computational designers. Additional studies should be conducted to further understand the extent generative design tools impact the design process, designer behaviour, and design outcomes.

 

Generative design tools empowered by recent advancements in artificial intelligence (AI) offer the opportunity for human designers and design tools to collaborate in new, more advanced modes throughout various stages of the product design process to facilitate the creation of higher performing and more complex products. This paper explores how the use of these generative design tools may impact the design process, designer behavior, and overall outcomes. Six in-depth interviews were conducted with practicing and student designers from different disciplines who use commercial generative design tools, detailing the design processes they followed. From a grounded theory-based analysis of the interviews, a provisional process diagram for generative design and its uses in the early-stage design process is proposed. The early stages of defining tool inputs bring about a constraint-driven process in which designers focus on the abstraction of the design problem. Designers will iterate through the inputs to improve both quantitative and qualitative metrics. The learning through iteration allows designers to gain a thorough understanding of the design problem and solution space. This can bring about creative applications of generative design tools in early-stage design to provide guidance for traditionally designed products.

 

Nearly every artifact of the modern engineering design process is digitally recorded and stored, resulting in an overwhelming amount of raw data detailing past designs. Analyzing this design knowledge and extracting functional information from sets of digital documents is a difficult and time-consuming task for human designers. For the case of textual documentation, poorly written superfluous descriptions filled with jargon are especially challenging for junior designers with less domain expertise to read. If the task of reading documents to extract functional requirements could be automated, designers could actually benefit from the distillation of massive digital repositories of design documentation into valuable information that can inform engineering design. This paper presents a system for automating the extraction of structured functional requirements from textual design documents by applying state of the art Natural Language Processing (NLP) models. A recursive method utilizing Machine Learning-based question-answering is developed to process design texts by initially identifying the highest-level functional requirement, and subsequently extracting additional requirements contained in the text passage. The efficacy of this system is evaluated by comparing the Machine Learning-based results with a study of 75 human designers performing the same design document analysis task on technical texts from the field of Microelectromechanical Systems (MEMS). The prospect of deploying such a system on the sum of all digital engineering documents suggests a future where design failures are less likely to be repeated and past successes may be consistently used to forward innovation.

 

This paper introduces the Simulated Jet Engine Bracket Dataset (SimJEB) [WBM21]: a new, public collection of crowdsourced mechanical brackets and accompanying structural simulations. SimJEB is applicable to a wide range of geometry processing tasks; the complexity of the shapes in SimJEB offer a challenge to automated geometry cleaning and meshing, while categorical labels and structural simulations facilitate classification and regression (i.e. engineering surrogate modeling). In contrast to existing shape collections, SimJEB's models are all designed for the same engineering function and thus have consistent structural loads and support conditions. On the other hand, SimJEB models are more complex, diverse, and realistic than the synthetically generated datasets commonly used in parametric surrogate model evaluation. The designs in SimJEB were derived from submissions to the GrabCAD Jet Engine Bracket Challenge: an open engineering design competition with over 700 hand‐designed CAD entries from 320 designers representing 56 countries. Each model has been cleaned, categorized, meshed, and simulated with finite element analysis according to the original competition specifications. The result is a collection of 381 diverse, high‐quality and application‐focused designs for advancing geometric deep learning, engineering surrogate modeling, automated cleaning and related geometry processing tasks.

 

Design documentation is presumed to contain massive amounts of valuable information and expert knowledge that is useful for learning from the past successes and failures. However, the current practice of documenting design in most industries does not result in big data that can support a true digital transformation of enterprise. Very little information on concepts and decisions in early product design has been digitally captured, and the access and retrieval of them via taxonomy-based knowledge management systems are very challenging because most rule-based classification and search systems cannot concurrently process heterogeneous data (text, figures, tables, references). When experts retire or leave a design unit, industry often cannot benefit from past knowledge for future product design, and is left to reinvent the wheel repeatedly. In this work, we present AI-based Natural Language Processing (NLP) models which are trained for contextually representing technical documents containing texts, figures and tables, to do a semantic search for the retrieval of relevant data across large corpora of documents. By connecting textual and non-textual data through the use of an associative database, the semantic search question-answering system we developed can provide more comprehensive answers in the context of users’ questions. For the demonstration and assessment of this model, the semantic search question-answering system is applied to the Intergovernmental Panel on Climate Change (IPCC) Special Report 2019, which is more than 600 pages long and difficult to read and understand, even by most experts. Users can input custom queries relating to climate change concerns and receive evidence from the report that is contextually meaningful. We expect this method can transform current repositories of design documentation of heterogeneous data forms into structured knowledge-bases which can return relevant information efficiently as well as can evolve to embody manageable big data for the true digital transformation of design. 

Abstract Surrogate models have several uses in engineering design, including speeding up design optimization, noise reduction, test measurement interpolation, gradient estimation, portability, and protection of intellectual property. Traditionally, surrogate models require that all training data conform to the same parametrization (e.g., design variables), limiting design freedom and prohibiting the reuse of historical data. In response, this article proposes graph-based surrogate models (GSMs) for trusses. The GSM can accurately predict displacement fields from static loads given the structure’s geometry as input, enabling training across multiple parametrizations. GSMs build upon recent advancements in geometric deep learning, which have led to the ability to learn on undirected graphs: a natural representation for trusses. To further promote flexible surrogate models, this article explores transfer learning within the context of engineering design and demonstrates positive knowledge transfer across data sets of different topologies, complexities, loads, and applications, resulting in more flexible and data-efficient surrogate models for trusses. 

Abstract Environmental sustainability, as well as social and economic well-being, must be considered in every stage of a product lifecycle, from conceptual design to its retirement. Even though this sustainability-centric approach represents a critical driver for innovation, it also increases the design complexity. Nowadays, the maritime transport accounts for a large share of transport demand, and the importance of sustainable ship design is increasingly growing, not only for ethical and legislative but also for competitive reasons. The design of a sustainable ship considering all those aspects is a complex process in this regard. One way to manage the complexity is to identify and avoid the functional couplings at the early stage of the design process. This paper presents the conceptual design of a merchant ship's conventional propulsion system with a view to the Axiomatic Design framework and known sustainable engineering principles. We also explore the Bayesian machine learning interface to propose a data-driven method for calculating the probability of achieving specific sustainability-related functional requirements. Data-driven Bayesian reasoning can also be used to select the best design parameter among the proposed alternatives as well as to identify hidden design couplings that have not identified by the designers in the conceptual design stage. 

Abstract Axiomatic Design (AD) provides a powerful thinking framework for solving complex engineering problems through the concept of design domains and diligent mapping and decomposition between functional and physical domains. Despite this utility, AD is yet to be implemented for widespread use by design practitioners solving real world problems in industry and exists primarily in the realm of academia. This is due, in part, to a high level of design expertise and familiarity with its methodology required to apply the AD approach effectively. It is difficult to correctly identify, extract, and abstract top-level functional requirements (FRs) based on early-stage design research. Furthermore, guiding early-stage design by striving to maintain functional independence, the first Axiom, is difficult at a systems level without explicit methods of quantifying the relationship between high-level FRs and design parameters (DPs). To address these challenges, Artificial Intelligence (AI) methods, specifically in deep learning (DL) assisted Natural Language Processing (NLP), have been applied to represent design knowledge for machines to understand, and, following AD principles, support the practice of human designers. NLP-based question-answering is demonstrated to automate early-stage identification of FRs and to assist design decomposition by recursively mapping and traversing down along the FR-DP hierarchical structure. Functional coupling analysis could then be conducted with vectorized FRs and DPs from NLP-based language embeddings. This paper presents a framework for how AI can be applied to design based on the principles of AD, which will enable a virtual design assistant system based on both human and machine intelligence.