The sustainability concept is becoming ingrained in the international engineering community. The next generation of engineers has to be trained to appreciate economic, environmental, and societal impacts of its decisions, with an international perspective and at a local and global scale. The aim of this paper is to contribute to identifying the strengths and limits of current sustainability education practices to will be able to improve future decision-makers' and product designers' training. The first step of our research concerns the identification of knowledge, skills, attitudes, and values associated with sustainability and ecodesign to highlight what kind of engineers' competencies for sustainability have to be trained. Based on this identification, we examine the French education system from secondary school to university to study sustainability and ecodesign integration in different technological French curricula and practices. The objective is to estimate the effective place of sustainable development and ecodesign in the technological training courses. We analyze programs and complete our approach by observing teachers in their classrooms or by conducting interviews. Even if concepts, knowledge and skills related to ecodesign appear in many different programs, our study emphasizes that there is not a continuum between pre-secondary school, high school, and university. As a consequence, teachers have difficulties to understand the aims and the coherence of the program, and students are not really comfortable with the issues of sustainability and ecodesign. We propose to improve programs and practices by developing multidisciplinary curricula covering most of the sustainable issues and integrating best practices of international universities.

Innovation and creativity are a mandatory for companies who wish to stay competitive. In order to promote an inventive dynamic, it implies to set up tools, habits, and an adapted environment to foster creativity. Creativity is the wealth of companies that should be valorized. To promote creativity, companies implement creativity workshops that gather people with various roles and expertise exchange and create knowledge to solve collectively open-ended engineering problems. However, group dynamics or facilitation can make the wrong decision and make the creative problem-solving unfruitful. The aim of our research project is to create a digital system to manage and valorize knowledge during creativity workshops. To design this system, we need to formalize the knowledge domain of creative workshops. The ontologies are used for decades to structure and manage information and knowledge in different domains. However, methodologies to design these ontologies are either hardly reproducible or not oriented to extract knowledge from organization. This article describes a methodology based on an organizational modeling to build ontologies. We will illustrate our approach by designing an ontology that models knowledge of creativity workshops.

Configuration and parameterization of optimization frameworks for the computational support of design exploration can become an exclusive barrier for the adoption of such systems by engineers. This work addresses the problem of defining the elements that constitute a multiple-objective design optimization problem, that is, design variables, constants, objective functions, and constraint functions. In light of this, contributions are reviewed from the field of evolutionary design optimization with respect to their concrete implementation for design exploration. Machine learning and natural language processing are supposed to facilitate feasible approaches to the support of configuration and parameterization. Hence, the authors further review promising machine learning and natural language processing methods for automatic knowledge elicitation and formalization with respect to their implementation for evolutionary design optimization. These methods come from the fields of product attribute extraction, clustering of design solutions, relationship discovery, computation of objective functions, metamodeling, and design pattern extraction.

One of the biggest challenges associated with design and digital reconstruction of free forms comes from uniqueness and unrepeatability of these shapes. During digital reconstruction of these forms, the designer has to choose the right set of geometric features and then compose them in a way that will enable the most accurate reconstruction of the geometry. While doing this, the designer primarily relies on personal experience gained through work with free-form objects of similar geometry. In our opinion, the analysis of free-form objects geometry should rely upon semantic interpretation of their geometric and other features, and the greatest challenge of automation of digital reconstruction and free-form object design in general is closely related to automation of semantic interpretation of geometric and other free-form object features. In this paper, a case of chest bone implant digital reconstruction is presented, where a new semantic model called the active semantic model was used for modeling the meaning of geometric elements, that is, the semantic features of a free-form object. The active semantic model and its analogy-based reasoning algorithms have shown themselves as applicable for the automation of semantic interpretation of the unique, unrepeatable, and unpredictable forms of chest bone. Moreover, this semantic model showed the potential to help automate selecting and composing of geometric features for efficient digital reconstruction of the geometry of free forms.

In many cases, the use of product configurators provides a number of benefits for engineering-oriented companies, such as shorter lead times, the use of fewer man hours, and improved product quality. However, not all of these projects are successful. The task of defining what should be implemented into the configurator is much more complex than anticipated in many configurator projects. In the worst-case scenario, this implies that the configurator is never fully developed or it fails to support the users in a satisfactory manner. The difficulties in creating adequate specifications of what to implement in the knowledge base of a configurator have been explained in much of the configuration literature by the assumption that much of the relevant knowledge of the product experts is tacit. However, this paper shows that this kind of explanation can be misleading and may even be incorrect. This is accomplished by discussing the problems inherent in such explanations and through interviews with configuration project knowledge engineers. Therefore, according to this basis, this paper provides general recommendations concerning the use of the term tacit knowledge and outlines issues related to the acquisition of product knowledge in configuration projects.

This paper presents the development and application of a software tool for modeling knowledge to be used in knowledge-based systems or the Semantic Web. The inferential modeling technique, which is a technique for modeling the static and dynamic knowledge elements of a problem domain, provided the basis for the tool. A survey of existing knowledge modeling tools revealed they typically failed to provide support in four main areas: support for an ontological engineering methodology or technique, support for dynamic knowledge modeling, support for dynamic knowledge testing, and support for ontology management. Dyna, a Protégé plug-in, has been developed, which supports the Inferential Modeling Technique, dynamic knowledge modeling, and dynamic knowledge testing. Protégé and Dyna are applied for constructing an ontological model in the domain of selecting a remediation technology for petroleum contaminated sites. Dynamic knowledge testing in Dyna enabled creation of a more complete knowledge model.