Development of a CAD system for parametric and attribute-based modification of yacht hull models

With the invention of computers, traditional design and manufacturing techniques have been taken over by advanced design techniques, such as computer-aided design, computer-aided engineering and computer-aided manufacturing. Designers have been utilizing these techniques to develop reliable products and advanced engineering designs. In the field of computer aided design, parametric design approach is the most robust technique which helps designers to perform instant topological changes of their designs. Another design technique can be attribute-based design which has been studied by different researchers. The aim of this research is to develop a sophisticated design platform for designers and naval architects to generate parametric and attribute-based yacht hull forms. In this dissertation, we first proposed a new design framework for the parametric design and shape modification of a yacht hull. In this framework, the hull is divided into three regions (entrance, middle and run) and each region is represented separately. In this way, a designer has better design flexibility so that higher design variations of the hull can be achieved. Each region consists of keel line(s), deck line, chine line(s) and station lines that are represented using Bezier curves and these lines are called feature curves. A 3D surface model of a yacht hull is obtained by generating Coons patches using feature curves. Shape operators are also introduced and implemented for the modification of the given hull shape while considering some quality criteria such as hull fairness. For the attribute based modeling of yacht hulls, we proposed a sampling technique based on the Teaching-Learning Based Optimization (TLBO) algorithm. TLBO algorithm has been presented in recent years by Rao et al., which is a population-based algorithm and operates on the principle of teaching and learning. This algorithm is based on the influence of a teacher on the quality of learners in a population. In this study, TLBO is extended for constrained and unconstrained CAD model sampling which is called S-TLBO. To sample $N$ designs in a predefined design space, $N$ sub-populations are first generated each of which consists of separate learners. Teaching and learning phases are applied for each sub-population one by one which are based on a cost (fitness) function. Iterations are performed until change in the cost values becomes negligibly small. Teachers of each sub-population are regarded as sampled designs after the application of S-TLBO. For unconstrained design sampling, the cost function favors the generation of space-filling and Latin Hypercube designs. Space-filling is achieved using the Audze and Eglais' technique. For constrained design sampling, a static constraint handling mechanism is utilized to penalize designs that do not satisfy the predefined design constraints. Dogan proposed attribute-based mathematical equations for yacht hull modeling. These equations are employed as constraints, and S-TLBO is implemented in constrained design spaces to generate attribute-based yacht hull forms. Performance of both parametric-based design framework and S-TLBO is tested through several experiments. Experiments in this dissertation showed that a variety of hull shapes can be generated using the proposed design framework with the application of the shape operators. Experiments reveal that S-TLBO is robust to perform sampling in constrained and unconstrained design spaces and can produce attribute-based yacht hull forms. Results also showed that S-TLBO outperforms state-of-the-art techniques particularly when a high number of samples are generated. Finally, based on the proposed techniques a user application called ModiYacht is developed. ModiYacht consists of two modules: one for the parametric-based design modification and the other for the attribute-based generation of yacht hull forms.