Knowledge engineering for design automation

Engineering design teams face many challenges, one of which is the time pressure on the product creation process. A wide range of Information and Communications Technology solutions is available to relieve the time pressure and increase overall efficiency. A promising type of software is that which automates a design process and generates design candidates, based on specifications of required behavior. Visual presentation of multiple solutions in a “solution space” provides insight in the trends, limitations and possibilities. This higher-level knowledge enables the use of “design intent” and tacit experience knowledge to select the best design for a specific application. This thesis focuses on software support for (engineering) design processes that use existing technologies and knowledge, with parametric information and quantitative data. This covers continuous and discontinuous parameters, as well as a mix of linear and non-linear equations, logic and fuzzy estimations, for static and dynamic topologies. The scope includes the design of machine elements, product components and product systems. Academic research has explored the automation of design processes for a wide range of engineering problems, including the scope of this thesis. A variety of theories, frameworks and techniques are developed to automate models of design problems. Sophisticated software support is made possible with advanced functionalities to navigate and explore design solutions. Although the technical feasibility appears to be proved, the intended software support is not present in industry to the extent that it could. The goal of this thesis is to increase the use of design automation software in industrial environments. The focus lies on efficient development of the models that are required for automation, with the emphasis on expert knowledge for the design creation phase. A method is proposed to acquire the necessary models and determine the software functionality. The functionality of the software is described in advance to discuss the added value with the engineers that will use the software. The method integrates concepts from existing domains of knowledge acquisition, modeling, automation and software development. The input and output of each step are standardized to allow a predictable development method. Standardization is done by using generic models of the design process and expert knowledge. Observations “how” designers design are used to define these models. The result is a generic procedure that starts with a design process and ends with software that generates multiple designs. The first step of the method is to bring overview to the design environment. The original design context is divided, or decomposed, into distinct levels of abstraction, each with their own expressiveness and characteristics. The levels of abstraction discriminate between issues of higher or lower importance. A suitability check is provided to determine if the procedures from this thesis are applicable. After the levels of abstraction are identified, the sub-process of analysis is used to prescribe the further breakdown into sub-processes and information. Analysisoriented decomposition identifies three distinct types of information: performance, scenario and embodiment. The design process is divided in sub-processes of analysis, synthesis, evaluation and adjustment. The decomposition phase is a key aspect for predictable and efficient modeling and software development. Decomposition allows fast knowledge acquisition, less complex modeling, automation with a generic and predictable software functionality. The generic model of the design process is used to provide a standardized description of a design process. The functionalities of the software modules, as well as the complete system, are known at this point. The step after decomposition acquires the expert knowledge and models it in a format called PaRC (acronym for Parameters, Resolve rule and Constrain rule). PaRC consists of entities to define the design artifact (parameters and topological elements) and knowledge rules that enable design generation (resolve, constrain and expand rules: R-, C- and X-rules). The acquired model describes the design expert’s experience and know-how in solving design problems. The last steps of the procedure involve automation of the knowledge models and software development. A generic software architecture mimics the model of the design process and has generic interfaces to the PaRC knowledge models. As a result, software development effort is reduced when building multiple software programs. The proposed development method is applied to two industrial expert design cases and four cases with explicitly documented knowledge. The design process and expert knowledge are both modeled, and software prototypes are developed