A multi-criteria based selection method using non-dominated sorting for genetic algorithm based design

The paper presents a generative design approach, particularly for simulation-driven designs, using a genetic algorithm (GA), which is structured based on a novel offspring selection strategy. The proposed selection approach commences while enumerating the offsprings generated from the selected parents. Afterwards, a set of eminent offsprings is selected from the enumerated ones based on the following merit criteria: space-fillingness to generate as many distinct offsprings as possible, resemblance/non-resemblance of offsprings to the good/bad individuals, non-collapsingness to produce diverse simulation results and constrain-handling for the selection of offsprings satisfying design constraints. The selection problem itself is formulated as a multi-objective optimization problem. A greedy technique is employed based on non-dominated sorting, pruning, and selecting the representative solution. According to the experiments performed using three different application scenarios, namely simulation-driven product design, mechanical design and user-centred product design, the proposed selection technique outperforms the baseline GA selection techniques, such as tournament and ranking selections

リバースエンジニアリングのための特徴を考慮した四辺形メッシュ分割

学位の種別:課程博士University of Tokyo(東京大学

Sampling CAD models via an extended teaching–learning-based optimization technique

The Teaching–Learning-Based Optimization (TLBO) algorithm of Rao et al. has been presented in recent years, 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 Sampling-TLBO (S-TLBO). Sampling CAD models in the design space can be useful for both designers and customers during the design stage. A good sampling technique should generate CAD models uniformly distributed in the entire design space so that designers or customers can well understand possible design options. To sample designs in a predefined design space, 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. Four CAD models, a yacht hull, a wheel rim and two different wine glasses, are employed to validate the performance of the S-TLBO approach. Sampling is first done for unconstrained design spaces, whereby the models obtained are shown to users in order to learn their preferences which are represented in the form of geometric constraints. Samples in constrained design spaces are then generated. According to the experiments in this study, S-TLBO outperforms state-of-the-art techniques particularly when a high number of samples are generated

A K-MEANS CLUSTERING BASED SHAPE RETRIEVAL TECHNIQUE FOR 3D MESH MODELS

Due to the large size of shape databases, importance of effective and robust method in shape retrieval has been increased. Researchers mainly focus on finding descriptors which is suitable for rigid models. Retrieval of non-rigid models is a still challenging field which needs to be studied more. For non-rigid models, descriptors that are designed should be insensitive to different poses. For non-rigid model retrieval, we propose a new method which first divides a model into clusters using geodesic distance metric and then computes the descriptor using these clusters. Mesh segmentation is performed using a skeleton-based K-means clustering method.  Each cluster is represented by an area based descriptor which is invariant to scale and orientation. Finally, similar objects for the input model are retrieved. Articulated objects from human to animals are used for this study’s experiments for the validation of the proposed retrieval algorithm

Evolving a psycho-physical distance metric for generative design exploration of diverse shapes

In this paper, a generative design approach is proposed that involves the users’ psychological aspect in the design space exploration stage to create distinct design alternatives. Users’ perceptual judgment about designs is extracted as a psycho-physical distance metric, which is then integrated into the design exploration step to generate design alternatives for the parametric computer-aided design (CAD) shapes. To do this, a CAD model is first parametrized by defining geometric parameters and determining ranges of these parameters. Initial design alternatives for the CAD model are generated using Euclidean distance-based sampling teaching–learning-based optimization (S-TLBO), which is recently proposed and can sample N space-filling design alternatives in the design space. Similar designs are then clustered, and a user study is conducted to capture the subjects’ perceptual response for the dissimilarities between the cluster pairs. In addition, a furthest-point-sorting technique is introduced to equalize the number of designs in the clusters, which are being compared by the subjects in the user study. Afterward, nonlinear regression analyses are carried out to construct a mathematical correlation between the subjects’ perceptual response and geometric parameters in the form of a psycho-physical distance metric. Finally, a psycho-physical distance metric obtained is utilized to explore distinct design alternatives for the CAD model. Another user study is designed to compare the diversification between the designs when the Euclidean and the suggested psycho-physical distance metrics are utilized. According to the user study, designs generated with the latter metric are more distinct

A novel design framework for generation and parametric modification of yacht hull surfaces

This paper proposes 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. Experiments in this study show that a variety of hull shapes can be generated using the proposed design framework with the application of the shape operators

AN EXTENDED LATIN HYPERCUBE SAMPLING APPROACH FOR CAD MODEL GENERATION

In this paper, extended version of Latin hypercube sampling (ELHS) is proposed to obtain different design variations of a CAD model. The model is first represented by design parameters. Design constraints that are relationships between the parameters are then determined. After assigning value ranges for the design parameters, design space is formed. Each design parameter represents a dimension of this design space. Design is a point in the design space and is feasible if it satisfies the predefined design constraints. Otherwise, it is infeasible. ELHS utilizes an input design in order to obtain feasible designs.All dimensions of the design space are divided into equal number of intervals. ELHS perform trials in design space to find feasible designs. In each trial, all the candidate designs are enumerated and one of them is selected based on a cost function. Value of the cost function is zero if all design constraints of the design are satisfied. A similarity constraint is introduced in order to eliminate designs with similar geometries. Three different CAD models are utilized for this study’s experiments in order to show the results of the ELHS algorithm

GenYacht : an interactive generative design system for computer-aided yacht hull design

In the present work, a new digital design system, GenYacht, is proposed for the creation of optimal and user-centred yacht hull forms. GenYacht is a hybrid system involving generative and interactive design approaches, which enables users to create a variety of design alternatives. Among them, a user can select a hull design with desirable characteristics based on its appearance and hydrostatics/hydrodynamic performance. GenYacht first explores a given design space using a generative design technique (GDT), which creates uniformly distributed designs satisfying the given design constraints. These designs are then presented to a user and single or multiple designs are selected based on the user’s requirements. Afterwards, based on the selections, the design space is refined using a novel space-shrinking technique (SST). In each interaction, SST shrinks the design space, which is then fed into GDT to create new designs in the shrank space for the next interaction. This shrinkage of design space guides the exploration process and focuses the computational efforts on user-preferred regions. The interactive and generative design steps are repeated until the user reaches a satisfactory design(s). The efficiency of GenYacht is demonstrated via experimental and user studies and its performance is compared with interactive genetic algorithms

ModiYacht : intelligent CAD tool for parametric, generative, attributive and interactive modelling of yacht hull forms

This work proposes a new design tool called ModiYacht generation of optimal and user-centred yacht hull forms. The proposed system is based on different design modules, namely, parametric, generative, attribute-based, and interactive design. Parametric modelling is performed based on high-level operators, called Shape Modifiers, which uses different quality criteria for generating smooth and feasible designs. Because innovative and creative design is essential to a successful product; therefore, we also bring the benefits of generative design to ModiYacht by introducing a technique called N-TLBO. Instead of a single solution, NTLBO generates a variety of optimal design solutions for users to select a design satisfying their design and performance requirements. Afterwards, design attributes are also developed using machine learning to shorten the communication gap between customers and designers. Designs generated from generative design can also be used to perform interactive design modelling for users to explore a given design space not only based on the performance but also based on their form appearance. For this purpose, ModiYacht uses a space-shrinking technique, which shrinks the design space during optimisation and guides the exploration process to focus the computational efforts on user-preferred regions