Toward Automated Functional Modeling: An Association Rules Approach for Mining the Relationship between Product Components and Function

Tensa, Melissa; Edmonds, Katherine; Ferrero, Vincenzo; Mikes, Alex; Zurita, Nicolas Soria; Stone, Rob; DuPont, Bryony

doi:10.1017/dsi.2019.177

Cited by 10 publications

(4 citation statements)

References 24 publications

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“…Tensa et al [20] shows an approach on how to learn association rules between functions, parts and flows via an a priori algorithm. This approach builds on and analyzes existing product data.…”

Section: Ai Approaches In Function Decompositionmentioning

confidence: 99%

Plan-Based Derivation of General Functional Structures in Product Design

Philipp¹,

Demke²,

Mantwill³

et al. 2023

Preprint

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In product design, a decomposition of the overall product function into a set of smaller, interacting functions is usually considered a crucial first step for any computer-supported design tool. Here, we propose a new approach for the decomposition of functions especially suited for later solutions based on Artificial Intelligence.The presented approach defines the decomposition problem in terms of a planning problem-a well established field in Artificial Intelligence. For the planning problem, logic-based solvers can be used to find solutions that compute a useful function structure for the design process. Well-known function libraries from engineering are used as atomic planning steps.The algorithms are evaluated using two different application examples to ensure the transferability of a general function decomposition.

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Section: Ai Approaches In Function Decompositionmentioning

confidence: 99%

Plan-Based Derivation of General Functional Structures in Product Design

Philipp¹,

Demke²,

Mantwill³

et al. 2023

Preprint

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“…Previous efforts have been made to use machine learning for improving function-based design methodologies. In other research, association rules and weighted confidence has been used to determine the function of a component within product configurations [35,36,37]. Decision trees have proved useful in reducing the feasible design space of functional assignment when considering product assembly [38].…”

Section: Function-based Product Designmentioning

confidence: 99%

Classifying Component Function in Product Assemblies with Graph Neural Networks

Ferrero¹,

Hassani²,

Grandi³

et al. 2021

Preprint

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Function is defined as the ensemble of tasks that enable the product to complete the designed purpose. Functional tools, such as functional modeling, offer decision guidance in the early phase of product design, where explicit design decisions are yet to be made. Function-based design data is often sparse and grounded in individual interpretation. As such, function-based design tools can benefit from automatic function classification to increase data fidelity and provide function representation models that enable function-based intelligent design agents. Functionbased design data is commonly stored in manually generated design repositories. These design repositories are a collection of expert knowledge and interpretations of function in product design bounded by function-flow and component taxonomies. In this work, we represent a structured taxonomy-based design repository as assembly-flow graphs, then leverage a graph neural network (GNN) model to perform automatic function classification. We support automated function classification by learning from repository data to establish the ground truth of component function assignment. Experimental results show that our GNN model achieves a micro-average F 1 -score of 0.832 for tier 1 (broad), 0.756 for tier 2, and 0.783 for tier 3 (specific) functions. Given the imbalance of data features, the results are encouraging. Our efforts in this paper can be a starting point for more sophisticated applications in knowledge-based CAD systems and Design-for-X consideration in function-based design.

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“…There may be a reason that no human decision-maker in the history of the earth, and no decisionmaking system has yet been able to solve poverty, environmental degradation, and other collective challenges facing us, as evidenced by the fact that such challenges still exist. In many cases [28], identifying these barriers requires a model of GCI. These barriers may not have even been conceived of by decision-makers or current decision-making systems (are invisible to them), and therefore fall outside of the choices any such individuals or systems are capable of making.…”

Section: Further Thoughtsmentioning

confidence: 99%

General Collective Intelligence and the Constraints to Group Decision-Making

Williams¹

2020

Preprint

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This paper addresses the question of how current group decision-making systems, including collective intelligence algorithms, might be constrained in ways that prevent them from achieving general problem solving ability. And as a result of those constraints, how some collective issues that pose existential risks such as poverty, the environmental degradation that has linked to climate change, or other sustainable development goals, might not be reliably solvable with current decision-making systems. This paper then addresses the question that assuming specific categories of such existential problems are not currently solvable with any existing group decision-systems, how can decision-systems increase the general problem solving ability of groups so that such issues can reliably be solved? In particular, how might a General Collective Intelligence, defined here to be a system of group decision-making with general problem solving ability, facilitate this increase in group problem-solving ability? The paper then presents some boundary conditions that a framework for modeling general problem solving in groups suggests must be satisfied by any model of General Collective Intelligence. When generalized to apply to all group decision-making, any such constraints on group intelligence, and any such system of General Collective Intelligence capable of removing those constraints, are then applicable to any process that utilizes group problem solving, from design, to manufacturing or any other life-cycle processes of any product or service, or whether research in any field from the arts to the basic sciences. For this reason these questions are important to a wide variety of academic disciplines. And because many of the issues impacted represent existential risks to human civilization, these questions may also be important by to all by definition.

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Toward Automated Functional Modeling: An Association Rules Approach for Mining the Relationship between Product Components and Function

Cited by 10 publications

References 24 publications

Plan-Based Derivation of General Functional Structures in Product Design

Plan-Based Derivation of General Functional Structures in Product Design

Classifying Component Function in Product Assemblies with Graph Neural Networks

General Collective Intelligence and the Constraints to Group Decision-Making

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