During product engineering, engineers draw on existing knowledge as a basis. This knowledge is applied in design processes either by adopting elements, alternating some attributes, or changing the solution principles. While concepts for design reuse exist, the proactive design of a suitable knowledge base is still a challenge. In this paper, we show influencing factors in engineering and their influence on the knowledge base. These factors are of the areas market, company, project, and team. Based on our results, we intent to develop a support for engineers to set up the knowledge base.

Progressive stamping tools are widely used in mass-production of sheet metal components and their performance is critical as the design of the tool impact the cost of the manufactured component significantly.

Knowledge reuse is an important part of successful design in general, and in progressive stamping tool design in particular. In the study described in this paper, 8 tool designers from 5 different Swedish companies were interviewed about (1) at what points in the tool design process they search for previously designed tools for information and knowledge reuse, (2) under what conditions and for what reasons does this reuse take place, and (3) what types of information and knowledge are sought for at each point. The results show that reuse of information and knowledge from previously designed tools happens in many parts of the tool design process. The reasons and conditions for reuse vary depending on where in the process the designer is. High component complexity is one example of a common factor triggering reuse. Also, information about the performance of the tool is important to tool designers, as they only want to reuse information and knowledge from tools with good performance and low maintenance.

Open-source hardware (OSH) development is a new design paradigm from a commercial perspective. Openly sharing designs of technical products is a step towards democratising access to new technologies for the benefit of individuals and communities in society. At the core of the open-source hardware definition lies the freedom for anyone to replicate the hardware based on the design. Thus, enabling this freedom is a step towards developing a successful OSH. Previous research supposes that a bill of materials and assembly instructions are enough for this. In this study, we question this assumption and investigate what other factors may influence replicability of an OSH. Using data from a survey and interviews with OSH practitioners, we identify and describe these factors, which relate to the documentation, the design and the context of the person replicating the hardware. Using these insights, we present a diagram of the replication process along with questions the person replicating the hardware would ask to check whether an OSH is replicable. Finally, we synthesise this information into practical advice for OSH projects to increase the replicability of the designs they produce, and thus the likelihood of their project's success.

Design rationale (DR) explains why an artifact is designed the way it is. An explicit representation of DR is helpful to designers, allowing them to understand, improve, and reuse previous designs. The argumentation-based representation is the mainstream approach to DR representation. It has a semiformal graphical format to depict the structure of arguments for solving a design problem. This paper argues that because the design is not just a problem-solving process but also a cognitive activity that is continuously iterative and evolving, the conventional argumentation-based representation of DR has some inherent limitations. An improved, intent-driven representation model is proposed to capture and formalize the DR and its evolving history to support DR reuse. The model's knowledge structure, consisting of DR elements and their relationships, is detailed. A preliminary knowledge representation of the model based on Web Ontology Language is introduced. Furthermore, the context of DR is defined to document the complete DR and support effective traceability of design thinking. A graphical DR modeling system is developed, and an example is demonstrated to verify the system's application and the effectiveness of the proposed representation model. The paper provides an effective method to retain and manage a designer's implicit design knowledge, which has the potential to significantly improve the integrated management of product development knowledge.

This paper describes current research toward automating the redesign process. In redesign, a working design is altered to meet new problem specifications. This process is complicated by interactions between different parts of the design, and many researchers have addressed these issues. An overview is given of a large design tool under development, the Circuit Designer's Apprentice. This tool integrates various techniques for reengineering existing circuits so that they meet new circuit requirements. The primary focus of the paper is one particular technique being used to reengineer circuits when they cannot be transformed to meet the new problem requirements. In these cases, a design plan is automatically generated for the circuit, and then replayed to solve all or part of the new problem. This technique is based upon the derivational analogy approach to design reuse. Derivational Analogy is a machine learning algorithm in which a design plan is saved at the time of design so that it can be replayed on a new design problem. Because design plans were not saved for the circuits available to the Circuit Designer's Apprentice, an algorithm was developed that automatically reconstructs a design plan for any circuit. This algorithm, Reconstructive Derivational Analogy, is described in detail, including a quantitative analysis of the implementation of this algorithm.

This article presents Kuaba, a new design rationale representation approach that enables employing design rationale to support reuse of model-based designs, particularly, software design. It is shown that this can be achieved through the adoption of an appropriate vocabulary that allows design rationale representations to be computationally processed. The architecture and implementation of an integrated design environment to support recording design rationale using Kuaba is also shown. The Kuaba approach integrates the design rationale representation model with the formal semantics provided by the metamodel of the design method or modeling language used for describing the artifact being designed. This integration makes the design rationale representations more specific according to the design methods and enables a type of software design reuse at the highest abstraction level, where rationales can be integrated and reemployed in designing a new artifact.

This paper describes a generic approach to guiding designers when making decisions during the early stages of design. The objective of the research is to enable designers to foresee unintended life-cycle consequences during mechanical component design. Engineering design is a process of evolving solutions to a design problem through the commitment of decisions. As a designer commits a new design decision, a more concrete design solution is generated. Decisions made can have intended and unintended consequences on the performance of the life phase activities that follow, such as manufacturing, assembly, and disposal. Many existing tools only consider the impact of the design solution on later life-cycle phases when the solution is almost complete. This makes changes expensive and difficult. This paper presents a novel approach to how consequences encountered in down stream life-cycle phases can be brought to the designer's attention early in generation of component form. For this purpose, a knowledge model has been derived from a phenomena model. The phenomena model describes how life-cycle consequences are generated during component synthesis. An insight into the representation of the resultant knowledge model is discussed through examples. The implementation of a prototype Knowledge Intensive CAD tool, entitled FORESEE, aimed at supporting life-oriented, feature-based component synthesis and exploration, is also described. The results of the evaluation of FORESEE with a range of designers show that by using the system designers are motivated to explore alternative design solutions and are able to make more informed design decisions. This highlights that the knowledge structure provides a base for extending feature-based component design to a ‘Design Synthesis for Multi-X’ approach.

This paper presents a formalism for considering the issues of learning in design. A foundation for machine learning in design (MLinD) is defined so as to provide answers to basic questions on learning in design, such as, “What types of knowledge can be learnt?”, “How does learning occur?”, and “When does learning occur?”. Five main elements of MLinD are presented as the input knowledge, knowledge transformers, output knowledge, goals/reasons for learning, and learning triggers. Using this foundation, published systems in MLinD were reviewed. The systematic review presents a basis for validating the presented foundation. The paper concludes that there is considerable work to be carried out in order to fully formalize the foundation of MLinD.