Identifying requirements for physics-based reasoning on function structure graphs

Technical function is a key concept in engineering design. Despite the centrality of the concept, a systematic, rigorous analysis of the utility of function and its different conceptualizations is missing in the engineering design literature. This paper addresses this challenge. We investigate the utility of function and its different meanings in the following engineering design contexts: malfunction explanation, innovative design, redesign, and routine design. This analysis provides theoretical justification for the current engineering practice of accepting ambiguity of functional descriptions and for methods to translate and/or convert functional descriptions across engineering design frameworks. We show that the utility of specific meanings of function is highly task dependent, identify novel roles for functional descriptions in engineering design, and present methodological implications for translation methods for functional descriptions.

Section: Implications and Conclusionmentioning

confidence: 85%

Section: Engineering Redesign and Routine Designmentioning

confidence: 99%

In defense of coexisting engineering meanings of function

Eck

Weber

2016

“…Second, we also observed that four machine learning methods had been used in customer reviews, ratings, and design requirements analyses (Suryadi and Kim 2019; Hein et al 2021; Lin and Kim 2021; Saidani et al 2021). Further, two deep learning methods were used along with NLP for design-requirement-extraction through semantic analysis (Wu et al 2022) and automatic extraction of functional requirements (Akay et al 2021); two network theory methods for requirements-identification (Sen and Summers 2013) and customer segmentation (Park and Kim 2021); and one probabilistic method that used Latent Dirichlet allocation (LDA) to identify and generate latent topic for managing design requirements (Chen et al 2021).…”

Section: Findings and Discussionmentioning

confidence: 99%

Mapping artificial intelligence-based methods to engineering design stages: a focused literature review

Khanolkar,

Vrolijk,

Olechowski

2023

Engineering design has proven to be a rich context for applying artificial intelligence (AI) methods, but a categorization of such methods applied in AI-based design research works seems to be lacking. This paper presents a focused literature review of AI-based methods mapped to the different stages of the engineering design process and describes how these methods assist the design process. We surveyed 108 AI-based engineering design papers from peer-reviewed journals and conference proceedings and mapped their contribution to five stages of the engineering design process. We categorized seven AI-based methods in our dataset. Our literature study indicated that most AI-based design research works are targeted at the conceptual and preliminary design stages. Given the open-ended, ambiguous nature of these early stages, these results are unexpected. We conjecture that this is likely a result of several factors, including the iterative nature of design tasks in these stages, the availability of open design data repositories, and the inclination to use AI for processing computationally intensive tasks, like those in these stages. Our study also indicated that these methods support designers by synthesizing and/or analyzing design data, concepts, and models in the design stages. This literature review aims to provide readers with an informative mapping of different AI tools to engineering design stages and to potentially motivate engineers, design researchers, and students to understand the current state-of-the-art and identify opportunities for applying AI applications in engineering design.

“…Nevertheless, those models provide little operational support for function-level or qualitative simulation of system behaviors (Tomiyama et al, 2013). The qualitative simulation should improve the product development process (Sen & Summers, 2013; Tomiyama et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…In his dissertation Sen (2011) stated that a function-based representation is suitable for supporting early design analysis reasoning. Sen and Summers (2013) identified requirements to enable physics-based reasoning from a function model. They extracted the following requirements: Coverage: This is the ability to cover the knowledge and principles of various domains and their interactions, such as electrical, mechanical, thermal, and chemical engineering. Consistency: Consistency is an internal property in the representation to prevent internal conflict. Validity against the laws of physics: The function model representation should remain valid against the existing laws of physics in each domain. Physics-based concreteness: The functions should be defined in terms of physical actions. Normative and descriptive modeling: The representation should support both developing a function model for new product design (so-called normative modeling) and the function modeling of an existing artifact, concepts, or physical principles (so-called descriptive modeling). Qualitative modeling and reasoning: It must enable support for both qualitative and quantitative reasoning. The first three requirements were stated to be generic requirements, and the others are based on their study of identified gaps in function-based design (Summers & Shah, 2004).…”

Section: Introductionmentioning

confidence: 99%

Function modeling combined with physics-based reasoning for assessing design options and supporting innovative ideation

Mokhtarian

Coatanéa

Paris

2017

Functional modeling is an analytical approach to design problems that is widely taught in certain academic communities but not often used by practitioners. This approach can be applied in multiple ways to formalize the understanding of the systems, to support the synthesis of the design in the development of a new product, or to support the analysis and improvement of existing systems incrementally. The type of usage depends on the objectives that are targeted. The objectives can be categorized into two key groups: discovering a totally new solution, or improving an existing one. This article proposes to use the functional modeling approach to achieve three goals: to support the representation of physics-based reasoning, to use this physics-based reasoning to assess design options, and finally to support innovative ideation. The exemplification of the function-based approach is presented via a case study of a glue gun proposed for this Special Issue. A reverse engineering approach is applied, and the authors seek an incremental improvement of the solution. As the physics-based reasoning model presented in this article is heavily dependent on the quality of the functional model, the authors propose a general approach to limit the interpretability of the functional representations by mapping the functional vocabulary with elementary structural blocks derived from bond graph theory. The physics-based reasoning approach is supported by a mathematical framework that is summarized in the article. The physics-based reasoning model is used for discovering the limitations of solutions in the form of internal contradictions and guiding the design ideation effort.