Engineering design process | EPFL Graph Search

The engineering design process, also known as the engineering method, is a common series of steps that engineers use in creating functional products and processes. The process is highly iterative - parts of the process often need to be repeated many times before another can be entered - though the part(s) that get iterated and the number of such cycles in any given project may vary. It is a decision making process (often iterative) in which the basic sciences, mathematics, and engineering sciences are applied to convert resources optimally to meet a stated objective. Among the fundamental elements of the design process are the establishment of objectives and criteria, synthesis, analysis, construction, testing and evaluation. Common stages of the engineering design process It's important to understand that there are various framings/articulations of the engineering design process. Different terminology employed may have varying degrees of overlap, which affects what ste

Towards Seamless Continuation of Knowledge in Product Lifecycle Management

Apostolos Perdikakis

The current ICT for manufacturing landscape is characterized by scattered data formats, tools and processes dedicated to different phases in the product lifecycle. Due to the diversity of tools and data formats, manufacturing struggles to cope with new trends in this area. What is clearly missing in the current ICT landscape for manufacturing is an integrated, holistic view on data about products to be manufactured, resources (including human resources) and processes across the full product lifecycle. There is a need to formalize knowledge for the automatic integration in existing engineering tools. In this work ontology-based semantics were exploited to develop machine-understandable models, which are able to understand the meaning of the data and information they contain. The system developed using semantic web methods and tools is designed to support interoperability and data integration, as well as lead a way towards the usage of information contained in the system to extract useful knowledge. This knowledge may provide feedback for improving the design of future generations of products. Thus, the concept of seamless continuation of knowledge all along product-system lifecycles is introduced.The first step of this work was to study the state-of-the-art in PLM and Semantic web Technologies. This included theoretical background in concepts like Beginning Of Life (BOL), Middle Of Life (MOL) and End Of Life (EOL) regarding PLM as well as the definition and theoretical background of Ontologies and Semantic Web Technologies. After completing the study of theoretical background, more advanced research concepts and methodologies for both domains were thoroughly analysed. Examples include Closed Loop Lifecycle Management (CL2M) for PLM and NEON Method-ology for Ontology engineering. The next logical step followed was to find and discover relevant standards like STEP for PLM and W3C standards for Ontologies and Semantic Web Technologies. The engineering aspect of this study imposed the review of available tools both for PLM and Ontology Engineering. Ontology development tools like Protégé, TopBraid composer and Anzo Ontology editor were studied. More advanced software solutions for applications of the semantic web were also studied. Examples include Anzo Enterprise and the Open Semantic Framework. Inspiration also came from previous European projects like EC FP6 Promise. The problem of discontinuation, even after an Ontology model is created, is evident in most of the applications studied as background of this work. Ontology models have to be exploitable and that requires a framework of which they can be part of. The research for successful implementations of ontology driven frameworks was imperative. At first, the open seman-tic framework (osf) was the software stack that came into focus. After being thoroughly studied, it proved difficult to deploy and maintain, for it had still many problematic areas with its configuration and required people with very ad-vanced software skills (both in programing and system configuration). Nevertheless, the examples of use are promis-ing and give many ideas for exploitation. This finding triggered the efforts towards the definition and application of the solution proposed in this work, which has the following characteristics. First and most important, the solution is easy to deploy and maintain, since it is targeting SMEs and large enterprises that want to test the capabilities of the proposed[...]

EPFL2015

Machine Learning Techniques to address classification issues in Reverse Engineering

Jonathan Dekhtiar

This paper aims to provide a road map for future works related to reverse engineering field of expertise. Reverse Engineering, in a mechanical context, relates to any process working in a bottom-up fashion, namely that it goes from a lower level concept or product (closer to the final product) to a higher level one (closer to the ideation step). Nowadays, the manufacturing industry is facing unprecedented increase in data exchange and data warehousing. This comes with new issues that our work will not explore, such as "how to store these data in an efficient manner?", "what should be stored?" and so on. Nonetheless, this trend also creates new opportunities if we manage to integrate these data into the expertise workflows. In this paper we will cover the possibilities offered by machine learning to succeed in this challenge. We will also present a first and major step in our road map in order to achieve our research goals. We plan to design a metric to quantify how well and how precise can we perform some specific reverse engineering tasks such as detection, segmentation and classification of mechanical parts in imagery data. We aspire to open this metric; and make it freely and widely available to researchers and industry in order to compare the effectiveness, robustness and preciseness of the existing and future approaches.

Springer-Verlag Berlin2017

A reduced order model to predict the machining time and cost of small-scale radial-inflow turbines

Matthias Geiselhart, Jürg Alexander Schiffmann

This paper proposes a novel approach to predict the machining time of small-scale radial-inflow turbine impellers for organic Rankine cycle power systems based on preliminary design parameters. The time prediction method uses machining databases of different milling tools for the estimation of the cutting parameters, on which basis the flow channel volume and surfaces are discretized for the generation of preliminary milling toolpaths. The parameter selection comprises the material selection, the volume removed, the demanded surface quality and the tool selection governed by the accessibility due to geometrical limitations induced by the blades. The model is verified using computer-aided manufacturing software for two radial-inflow turbines cases: a state-of-the-art turbine using air and a turbine using the working fluid Novec 649 for a heat recovery application. The results highlight the governing role of the tool slenderness ratio on the actual manufacturing time. The relative time distribution among roughing, semi-finishing and finishing operations remain similar when considering the same production chain. The tool roughing strategy holds potential for optimization by contributing to approximately 60 % of the total milling time. Furthermore, the results indicate that the turbine manufacturing time can be improved by up to approximately 44 % when the maximum permissible load limits of the tools are exploited by customized cutting data. The presented machining time model can be applied in future works to predict the manufacturing costs of impellers and to quantify the influence of individual design features on the total cost by performing a sensitivity analysis. Moreover, the design space for optimal designs of small-scale radial-inflow turbines for organic Rankine power applications considering both the manufacturing time and the performance of the expander can be explored.

Technical University of Munich2021