On relating functional modeling approaches: abstracting functional models from behavioral models

This paper presents a survey of functional modeling approaches and describes a strategy to establish functional knowledge exchange between them. This survey is focused on a comparison of function meanings and representations. It is argued that functions represented as input-output flow transformations correspond to behaviors in the approaches that characterize functions as intended behaviors. Based on this result a strategy is presented to relate the different meanings of function between the approaches, establishing functional knowledge exchange between them. It is shown that this strategy is able to preserve more functional information than the functional knowledge exchange methodology of Kitamura, Mizoguchi, and co-workers. The strategy proposed here consists of two steps. In step one, operation-on-flow functions are translated into behaviors. In step two, intended behavior functions are derived from behaviors. The two-step strategy and its benefits are demonstrated by relating functional models of a power screwdriver between methodologies

B-Cube, Behavioural modelling of technical artefacts

A new model, B-Cube, is described for managing knowledge at the behaviour level of the function–behaviour–structure framework. The model proposes a three-dimensional approach to the behavioural modelling of technical artefacts using definitions based mainly on the meta-ontology DOLCE as concepts of behaviour. The present work aims to show how these terms and those from the NIST functional basis can complement each other in functional design. It is assumed that this model achieves similar objectives with behaviours to those obtained by the NIST functional basis with functions, i.e. the representation of behaviours in CAD and KBS, a scheme for the modelling of behaviours and a universal set of behaviours. The modelling language IDEF was adapted to be able to produce a graphic example of the modelling of technical artefacts in the FBS framework using B-Cube terminology at the behaviour level

A formal ontological perspective on the behaviors and functions of technical artifacts

In this paper we present a formal characterization of the engineering concepts of behavior and function of technical artifacts. We capture the meanings that engineers attach to these concepts by formalizing, within the formal ontology DOLCE, the five meanings of artifact behavior and the two meanings of function that Chandrasekaran and Josephson identified in 2000 within the functional representation approach.We begin our formalization by reserving the term “behavior” of a technical artifact as “the specific way in which the artifact occurs in an event.” This general notion is characterized formally, and used to provide definitions of actual behaviors of artifacts, and the physically possible and physically impossible behaviors that rational agents believe that artifacts have. We also define several other notions, for example, input and output behaviors of artifacts, and then show that these ontologically characterized concepts give a general framework in which Chandrasekaran and Josephson’s meanings of behavior can be explicitly formalized. Finally we show how Chandrasekaran and Josephson’s two meanings of artifact functions, namely, device-centric and environment-centric functions, can be captured in DOLCE via the concepts of behavioral constraint and mode of deployment of an artifact. A more general goal of this work is to show that foundational ontologies are suited to the engineering domain: they can facilitate information sharing and exchange in the various engineering domains by providing concept structures and clarifications that make explicit and precise important engineering notions. The meanings of the terms “behavior” and “function” in domains like designing, redesigning, reverse engineering, product architecture, and engineering knowledge bases are often ambiguous or overloaded. Our results show that foundational ontologies can accommodate the variety of denotations these terms have and can explain their relationships.Values and TechnologyTechnology, Policy and Managemen

A new set of guidelines for inventive problem solving

The rapid changes characterizing the economy in the last decades convinced companies, especially the most advanced, to heavily invest in innovation and in approaches to support it in a systematic way to increase the qualitative level of their products and reduce the time-to-market. Academia answered to this demand with an increasing number of publications on this topic every year; in addition, industry developed its own procedures, often internally. As result, today a lot of strategies, theories, methods and tools are available for systematic innovation. However, an accepted and unified theory and objective criteria able to assist the problem solver in the selection of the most suitable approach according to her/his needs are still missing. The Ph.D. thesis refers to this context and its main objectives have been: (1) reviewing and classifying the huge multitude of systematic innovation methods (for new concept design, product improvement, robust design, physical investigation, information retrieval, etc.) and (2) developing a methodology to assist the designer in selecting the most suitable method in accordance with the application context. Among the several possibilities, I choose to develop a set of guidelines that are both comprehensive and practical to apply especially in industrial contexts. However, writing guidelines is a complicated activity, as demonstrated by the numerous examples from literature describing problems and limitations in conceiving and/or applying them. Based on literature review, involving not only papers but also patents and empirical evidences collected during the collaborations in industrial projects and tests with students, I identified the main key features of the guidelines for inventive problem solving. They are: the structure of single guideline, the organization of multiple guidelines and the suggested methods and tools. In particular, I focused attention to comprehend how the suggestions provided by the guidelines change in relation to the kinds of addressed problems, the different phases in problem solving activity and the user, and how to enrich them through specific methodological contents. Then, according to the mentioned features, I developed a set of specific guidelines to improve Spark, a methodology for systematic innovation developed at University of Bergamo, reviewing some parts and integrating with some proposed models. The research activities have been carried out in five phases as described in the following. During the first activity, a state of the art about the kinds of addressed problems, and the main problem solving methods, approaches and strategies to support systematic innovation has been carried out. In the second activity, the main features of the guidelines have been identified through a detailed analysis based on literature surveys, of Design models (e.g., FBS), Risk analysis techniques (FMEA), Problem solving tools (TRIZ) and empirical evidences collected in the companies and by involving engineering students. The results have been organized according to three main aspects: the definition of the most suitable structure of a single guideline (in terms of provided text, graphical representations and examples), the organization of multiple guidelines (hierarchical maps, random lists, matrices, etc.) and the models and tools suggested by the guidelines in accordance to the addressed inventive problems and the phase in problem solving activity. This results have then been summarized in a set of rules for writing guidelines. During the third activity, the identified features have been applied to improve some parts of Spark methodology, which is structured as an ordered step by step procedure to enhance the different problem solver skills: function identification, evolutionary overview identification, problem identification, problem reformulation and idea generation. Even if this methodology has been successfully applied in industrial cases studies, it still presents some limitations (e.g., by supporting new product design). I tried to improve Spark by expanding its domain of application to all the considered inventive problems and by ameliorating its comprehension and applicability, through an increased level of awareness of the designer while maintaining the suggested path. To do this, I improved the parts of function and problem identification through the introduction of two specific models derived from FBS and FMEA, and I reformulated the part of idea generation by providing a more rigorous ontology and a more intuitive organization to the already contained guidelines. Finally, I proposed a comprehensive set of guidelines to guide the user in the use of the improved version of Spark. The resulting approach maintains a unique path to face all the considered inventive problems and allows specific iterations and ramifications inside the main steps, depending on the problem and the context of application. In the fourth activity, the goal has been to drive the user to model the problem with a functional approach, in order to be able to consult the Information Retrieval tools in the proper way to find out if someone has already solved the problem in another context. More in detail, this means to conceive a guideline able to support the user in defining the right element on which to work, the function and the behaviour of the solution, at least in terms of physical effect. Patent repository is used as technical source for gathering such an information. During the doctorate, I learned techniques and software prototypes developed by the University of Bergamo, for query expansions based on hyponyms, meronyms, hypernyms and lexical variants. I tested them in industrial case studies, to comprehend how to integrate info gathering into the guidelines structure. During the fifth activity, I recombined all the results previously achieved within of a software platform that I developed. It collects flexible guidelines, able to adapt to the different kinds of problems, which are organized through the conceptual scheme studied during the third activity, and integrates the knowledge retrieval techniques of the fourth activity. The proposed platform and the guidelines have been tested with real industrial case studies proposed by companies with whom I collaborated, such as ABB, Tenacta-Imetec. The tests involved MsD and PhD students, during thesis works, project works and group sessions with more than 10 students each one. The achieved results, compared to traditional Spark and other approaches, have been encouraging in terms of function identification, by facilitating the determination of the required operative zone and operative time, problem identification, with an increased user’s awareness about the dynamic of occurrence, and idea generation, with a great number of qualitatively better achieved solutions