Projection-based Topology Optimization Method for Linear and Nonlinear Design

Lighter designs are desirable in many industrial applications and structural optimization is an effective way to generate lightweight structures. Topology optimization is an important tool for investigating the optimal design of engineering structures. Although continuum topology optimization method has already achieved remarkable progress in recent years, there still exist several challenges for conventional density-based method such as manufacturability. Additive manufacturing (AM) is a rapidly developing technology by which the design can achieve more freedom. However, it does not mean that the optimized design generated by topology optimization algorithm can be directly manufactured without any geometry post-processing. Besides AM techniques, the traditional manufacturing methods of machining and casting are also popular in recent years, because the majority of engineering parts are manufactured through these methods. It is difficult for conventional density-based method to account for these manufacturing constraints. The projection-based topology optimization approach is a new trend in this field to properly restrict the optimal solutions by implementing geometric constraints. The nature of projection method is to apply new design variables projected in a pseudo-density domain to find the optimal solutions. In this dissertation, several advanced projection-based topology optimization schemes are proposed to resolve linear and nonlinear design problems and demonstrated through numerical examples. In chapter 2 and 3, a new projection technique is proposed to resolve nonlinear topology optimization problems with large deformation. Chapter 4 describes a novel design method, which combines the TPMS (Triply periodic minimal surface) formulation with standard projection-based method to design functionally graded TPMS lattice. In chapter 5, a projection-based method is combined with moving particles for reverse shape compensation for additive manufacturing technique. Chapter 6 describes a density‐based boundary evolving algorithm based on projection function for continuum‐based topology optimization. In the chapter 7, a novel projection-based method for structural design considering restrictions of multi-axis machining processes is proposed

Feature technology and its applications in computer integrated manufacturing

A Thesis submitted for the degree of Doctor of Philosophy of University of LutonComputer aided design and manufacturing (CAD/CAM) has been a focal research area for the manufacturing industry. Genuine CAD/CAM integration is necessary to make products of higher quality with lower cost and shorter lead times. Although CAD and CAM have been extensively used in industry, effective CAD/CAM integration has not been implemented. The major obstacles of CAD/CAM integration are the representation of design and process knowledge and the adaptive ability of computer aided process planning (CAPP). This research is aimed to develop a feature-based CAD/CAM integration methodology. Artificial intelligent techniques such as neural networks, heuristic algorithms, genetic algorithms and fuzzy logics are used to tackle problems. The activities considered include: 1) Component design based on a number of standard feature classes with validity check. A feature classification for machining application is defined adopting ISO 10303-STEP AP224 from a multi-viewpoint of design and manufacture. 2) Search of interacting features and identification of features relationships. A heuristic algorithm has been proposed in order to resolve interacting features. The algorithm analyses the interacting entity between each feature pair, making the process simpler and more efficient. 3) Recognition of new features formed by interacting features. A novel neural network-based technique for feature recognition has been designed, which solves the problems of ambiguity and overlaps. 4) Production of a feature based model for the component. 5) Generation of a suitable process plan covering selection of machining operations, grouping of machining operations and process sequencing. A hybrid feature-based CAPP has been developed using neural network, genetic algorithm and fuzzy evaluating techniques