Elicitation of Preference Structure in Engineering Design

Engineering design processes, which inherently involve multiple, often conflicting criteria, can be broadly classified into synthesis and analysis processes. Multiple Criteria Decision Making addresses synthesis and analysis processes through multiple objective optimisation to generate sets of efficient design solutions (i.e. on Pareto surfaces) and multiple attribute decision making to analyse and select the most preferred design solution(s). MCDM, therefore, has been widely used in all fields of engineering design; for example it has been applied to such diverse areas as naval battle ships criteria analysis/selection and product appearance design. Given a list of design alternatives with multiple conflicting criteria, preferences often determine the final selection of a particular set of design alternative(s). Preferences may also be used to drive the design/design optimisation processes. Various methods have been proposed to model preference structure, for example simple weights, multiple attribute utility theory, pairwise comparison, etc. Preference structure is often non-linear, discontinuous and complex. An Artificial Neural Network (ANN) learning-based preference elicitation method is presented in this paper. ANNs efficiently model the non-linearity, complexity and discontinuity nature of any given preference structure. A case study is presented to illustrate the learning-based approach to preference structure elicitation.

Marine propeller optimisation tools for scenario-based design

The marine propulsion system is one of the most important components of a ship in order to cover the demanding operating needs of propulsion nowadays and to increase performance in a wide range of operating conditions. Marine propellers are designed with the purpose of matching the hull and machinery system, create the required thrust for the entire operational profile, and fulfil the techno-economical requirements that depend on the decision-making of several stakeholders. The final product must represent a unique propeller, designed for a specific vessel, and is a trade-off between all requirements. In an industrial framework, the marine propeller design process should therefore be straightforward and well-developed. The limited time under which the design process must be performed, plays a decisive role in the methods utilised to carry it out, as for example in the selection of the analysis tools, which must be fast and they usually involve semi-empirical evaluations. Since blade design is a multi-objective and multidisciplinary problem, automated optimisation has been used with the aim to search good solutions in the design space efficiently. However, automated optimisation has failed to be used in industrial applications due to obtaining solutions with high performance but with infeasible geometries, and as a method it proved to be inferior to the manual design process, something that shows the importance of the designer\u27s expertise. The main research question of this thesis is therefore related to incorporating optimisation in a systematic way in order to improve the propeller design process and assist the blade designers to obtain feasible and high-performing propellers in strict time constraints. A methodology is proposed that combines interactive optimisation with machine learning and in parallel new objectives are implemented for more complex scenarios. The designer is enabled to manually evaluate cavitation nuisance during the optimisation and guide the algorithm towards areas of the design space with satisfactory cavitation characteristics. Several scenario-based situations have been investigated by using the proposed methodology, that involve different propeller types, design and off-design conditions, several objectives and constraints, cavitation nuisance on the suction and the pressure side of the blade, and applications within conventional and wind propulsion. The results have shown that by involving the blade designer\u27s expertise in the design and optimisation process systematically, competitive propeller designs with feasible geometries can be obtained efficiently

A multi-objective genetic algorithm for the design of pressure swing adsorption

Pressure Swing Adsorption (PSA) is a cyclic separation process, more advantageous over other separation options for middle scale processes. Automated tools for the design of PSA processes would be beneficial for the development of the technology, but their development is a difficult task due to the complexity of the simulation of PSA cycles and the computational effort needed to detect the performance at cyclic steady state. We present a preliminary investigation of the performance of a custom multi-objective genetic algorithm (MOGA) for the optimisation of a fast cycle PSA operation, the separation of air for N2 production. The simulation requires a detailed diffusion model, which involves coupled nonlinear partial differential and algebraic equations (PDAEs). The efficiency of MOGA to handle this complex problem has been assessed by comparison with direct search methods. An analysis of the effect of MOGA parameters on the performance is also presented

Redesign optimization for manufacturing using additive layer techniques

Improvements in additive manufacturing technologies have the potential to greatly provide value to designers that could also contribute towards improving the sustainability levels of products as well as the production of lightweight products. With these improvements, it is possible to eliminate the design restrictions previously faced by manufacturers. This study examines the principles of additive manufacturing, design guidelines, capabilities of the manufacturing processes and structural optimisation using topology optimisation. Furthermore, a redesign methodology is proposed and illustrated through a redesign case study of an existing bracket. The optimal design is selected using multi-criteria decision analysis method. The challenges for using additive manufacturing technologies are discussed

Mapping customer needs to engineering characteristics: an aerospace perspective for conceptual design

Designing complex engineering systems, such as an aircraft or an aero-engine, is immensely challenging. Formal Systems Engineering (SE) practices are widely used in the aerospace industry throughout the overall design process to minimise the overall design effort, corrective re-work, and ultimately overall development and manufacturing costs. Incorporating the needs and requirements from customers and other stakeholders into the conceptual and early design process is vital for the success and viability of any development programme. This paper presents a formal methodology, the Value-Driven Design (VDD) methodology that has been developed for collaborative and iterative use in the Extended Enterprise (EE) within the aerospace industry, and that has been applied using the Concept Design Analysis (CODA) method to map captured Customer Needs (CNs) into Engineering Characteristics (ECs) and to model an overall ‘design merit’ metric to be used in design assessments, sensitivity analyses, and engineering design optimisation studies. Two different case studies with increasing complexity are presented to elucidate the application areas of the CODA method in the context of the VDD methodology for the EE within the aerospace secto

A metamodel based optimisation algorithm for metal forming processes

Cost saving and product improvement have always been important goals in the metal\ud forming industry. To achieve these goals, metal forming processes need to be optimised. During\ud the last decades, simulation software based on the Finite Element Method (FEM) has significantly\ud contributed to designing feasible processes more easily. More recently, the possibility of\ud coupling FEM to mathematical optimisation algorithms is offering a very promising opportunity\ud to design optimal metal forming processes instead of only feasible ones. However, which\ud optimisation algorithm to use is still not clear.\ud In this paper, an optimisation algorithm based on metamodelling techniques is proposed\ud for optimising metal forming processes. The algorithm incorporates nonlinear FEM simulations\ud which can be very time consuming to execute. As an illustration of its capabilities, the\ud proposed algorithm is applied to optimise the internal pressure and axial feeding load paths\ud of a hydroforming process. The product formed by the optimised process outperforms products\ud produced by other, arbitrarily selected load paths. These results indicate the high potential of\ud the proposed algorithm for optimising metal forming processes using time consuming FEM\ud simulations

A collaborative platform for integrating and optimising Computational Fluid Dynamics analysis requests

A Virtual Integration Platform (VIP) is described which provides support for the integration of Computer-Aided Design (CAD) and Computational Fluid Dynamics (CFD) analysis tools into an environment that supports the use of these tools in a distributed collaborative manner. The VIP has evolved through previous EU research conducted within the VRShips-ROPAX 2000 (VRShips) project and the current version discussed here was developed predominantly within the VIRTUE project but also within the SAFEDOR project. The VIP is described with respect to the support it provides to designers and analysts in coordinating and optimising CFD analysis requests. Two case studies are provided that illustrate the application of the VIP within HSVA: the use of a panel code for the evaluation of geometry variations in order to improve propeller efficiency; and, the use of a dedicated maritime RANS code (FreSCo) to improve the wake distribution for the VIRTUE tanker. A discussion is included detailing the background, application and results from the use of the VIP within these two case studies as well as how the platform was of benefit during the development and a consideration of how it can benefit HSVA in the future

Numerical product design: Springback prediction, compensation and optimization

Numerical simulations are being deployed widely for product design. However, the accuracy of the numerical tools is not yet always sufficiently accurate and reliable. This article focuses on the current state and recent developments in different stages of product design: springback prediction, springback compensation and optimization by finite element (FE) analysis. To improve the springback prediction by FE analysis, guidelines regarding the mesh discretization are provided and a new through-thickness integration scheme for shell elements is launched. In the next stage of virtual product design the product is compensated for springback. Currently, deformations due to springback are manually compensated in the industry. Here, a procedure to automatically compensate the tool geometry, including the CAD description, is presented and it is successfully applied to an industrial automotive part. The last stage in virtual product design comprises optimization. This article presents an optimization scheme which is capable of designing optimal and robust metal forming processes efficiently