Maximum length scale requirement in a topology optimisation method based on NURBS hyper-surfaces

International audienceThis paper deals with a new method for handling manufacturing and geometrical requirements in the framework of a general Topology Optimisation (TO) strategy. In particular, the maximum length scale constraint (MLSC) implementation is addressed in order to obtain multiple load paths or to locally limit the size of the component. The classic formulation of the MLSC is revisited in the framework of a density-based TO algorithm wherein the pseudo-density field is represented through a NURBS hyper-surface. The NURBS hyper-surface properties are exploited to effectively formulate the MLSC. The effectiveness of the proposed approach is proven on a meaningful 3D benchmark

Intent Detection for Virtual Reality Architectural Design

In the context of optimization and reduction of cycles of product design in indus-try, digital collaborative tools have a major impact allowing an early-stage integra-tion of multidisciplinary challenges and oftentimes the search of global optimum rather than domain specific improvements. This paper presents a methodology for improving participants’ implication and performance during collaborative design sessions through virtual reality (VR) tools thanks to intention detection through body language interpretation and thus, reduction of cognitive workload. A proto-type of the methodology is being implemented based on an existing VR aided de-sign tool called DragonFly developed by Airbus. We will first discuss the choice of the different biological inputs to choose for our purpose, and how to merge these multimodal inputs in a meaningful way in order to ease further evolution and maintenance of our solution. Then, we will focus on the extraction of these inputs, their preprocessing, and the inference of intent and associated parameters. Finally, we will show the beginning of the application of this methodology to our specific use case of aircraft system installation

Information model for tracelinks building in early design stages

Over the last decades many efforts are being made into either both, creating better products or improving processes, yet, generating more information, and usually leaving behind how to manage whole information that already exist and using it to improv

Engineering Structures

This work deals with the multi-scale topology optimisation (TO) of multi-material lattice structures. The proposed approach is based on: non-uniform rational basis spline (NURBS) hyper-surfaces to represent the geometric descriptor related to each material phase composing the representative volume element (RVE), an improved multiphase material interpolation (MMI) scheme to penalise the element stiffness tensor of the multi-material RVE, the strain energy-based homogenisation method (SEHM) to carry out the scale transition. In this context, the design requirements are defined at different scales and their gradient is evaluated by exploiting the properties of the NURBS entities and of the SEHM. Moreover, the improved MMI scheme proposed here does not require the introduction of artificial filtering techniques to smooth the topological descriptors of the material phases composing the RVE. The effectiveness of the method is proven on both 2D and 3D problems. Specifically, a sensitivity analysis of the optimised configuration of the RVE to the parameters tuning the shape of the NURBS entity is conducted. Finally, the influence of the starting point and of the macroscopic loads on the optimal solution is investigated

Intent Detection for Virtual Reality Architectural Design

International audienceIn the context of optimization and cycles reduction for product design in industry, digital collaborative tools have a major impact, allowing an early stage integration of multidisciplinary challenges and oftentimes the search ofglobal optimum rather than domain specific improvements. This paper presents a methodology for improving participants’ implication and performance during collaborative design sessions through virtual reality (VR) tools, thanks to intention detection through body language interpretation. A prototype of the methodology is being implemented based on an existing VR aided design tool called DragonFly developed by Airbus. In what follows we will first discuss the choice of the different biological inputs for our purpose, and how to merge these multi-modal inputs a meaningful way. Thus, we obtain a rich representation of the body language expression, suitable to recognize the actions wanted by the user and their related parameters. We will then show that this solution has been designed for fast training thanks to a majority of unsupervised training and existing pre-trained models, and for fast evolution thanks to the modularity of the architecture

Intent Detection for Virtual Reality Architectural Design

International audienceIn the context of optimization and cycles reduction for product design in industry, digital collaborative tools have a major impact, allowing an early stage integration of multidisciplinary challenges and oftentimes the search ofglobal optimum rather than domain specific improvements. This paper presents a methodology for improving participants’ implication and performance during collaborative design sessions through virtual reality (VR) tools, thanks to intention detection through body language interpretation. A prototype of the methodology is being implemented based on an existing VR aided design tool called DragonFly developed by Airbus. In what follows we will first discuss the choice of the different biological inputs for our purpose, and how to merge these multi-modal inputs a meaningful way. Thus, we obtain a rich representation of the body language expression, suitable to recognize the actions wanted by the user and their related parameters. We will then show that this solution has been designed for fast training thanks to a majority of unsupervised training and existing pre-trained models, and for fast evolution thanks to the modularity of the architecture

Management of product design complexity due to epistemic uncertainty via energy flow modelling based on CPM

Integrated product design and development in today’s highly competitive and economically challenging world is a complex process depending upon client requirements. One of the main factors contributing to the complexity of process is uncertainty due to lack of system knowledge, known as epistemic uncertainty. This paper proposes a systematic approach to reduce epistemic uncertainty in design process in early stages of design. The approach is based on “CTOC” and “CPM” to decompose the system behaviour and determine the relationships between function and structure of a system. An application of the approach is demonstrated through an industrial case study