Multi-objective robust concept exploration within the made-to-order sector

Concept exploration is an activity of fundamental importance when designing large, complex made-to-order engineering products. At the concept design stage of the design process, it is essential that many design alternatives are evaluated. In the case of large made-to-order products, the evaluation of a particular design can be both complicated and time consuming. Under these circumstances, designers often resort to the use of concept design models enabling both a reduction in complexity and time for evaluation. Stochastic optimisation methods are then typically used to explore the design space facilitating the selection of optimum or near optimum designs. These optimisation methods can however increase the concept exploration time considerably due to their often random search manner. The objective of this work is therefore to produce a generic framework that would enable a designer to efficiently explore the design space within the MTO domain facilitating the selection of robust designs

LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching

The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most of existing LRFs are hand-crafted and suffer from limited repeatability and robustness. This paper presents the first attempt to learn an LRF via a Siamese network that needs weak supervision only. In particular, we argue that each neighboring point in the local surface gives a unique contribution to LRF construction and measure such contributions via learned weights. Extensive analysis and comparative experiments on three public datasets addressing different application scenarios have demonstrated that LRF-Net is more repeatable and robust than several state-of-the-art LRF methods (LRF-Net is only trained on one dataset). In addition, LRF-Net can significantly boost the local shape description and 6-DoF pose estimation performance when matching 3D point clouds.Comment: 28 pages, 14 figure

A robust design methodology suitable for application to one-off products

Robust design is an activity of fundamental importance when designing large, complex, one-off engineering products. Work is described which is concerned with the application of the theory of design of experiments and stochastic optimization methods to explore and optimize at the concept design stage. The discussion begins with a description of state-of-the-art stochastic techniques and their application to robust design. The content then focuses on a generic methodology which is capable of manipulating design algorithms that can be used to describe a design concept. An example is presented, demonstrating the use of the system for the robust design of a catamaran with respect to seakeeping