Inserts are a crucial part of household and industrial furniture. These small plastic parts which often go unnoticed to the naked eye perform crucial functions like providing a base for the furniture, leveling the furniture, safeguarding the user from edges of the tubes used and providing an aesthetic finish. The inserts have a wing like structure on the exterior which enables them to be inserted and securely held in the tubes. The inserts are assembled into the pipes manually or through machines. The force required to install these inserts in the tube is called a push-in force whereas a pull-out force is the force required for removal of the is called a pull-out force. These forces are experienced by someone who assembles the furniture together. Thus, these forces directly define the ease with which the furniture can be assembled. In the first part of the present thesis, these push-in and pull-out forces are predicted using finite element simulations. These finite element simulations were validated by performing physical assembly and disassembly experiments on these inserts. It was found that the finite element simulations of the insert are useful tool in predicting the push-in forces with a high accuracy.   These push-in and pull-out forces for a single insert vary by 2-5 times when the dimensional variations in the tube are considered. The dimensional variations can be a result of the manufacturing processes from which these tubes are produced. The maximum and minimum dimensions that the tube can have are defined by the maximum material condition (MMC) and the least material condition (LMC). To reduce the variation in push-in and pull out forces, a stricter tolerance control can be applied to the manufacturing process. To avoid this cost while having a lower variation in the push-in and pull out forces, the design of the insert was modified. To achieve this enhanced design of the insert, a metamodel based optimization technique was used in the second part of the thesis. For this optimization, the geometrical parameters - wing height, wing diameter and stem thickness the of the insert were identified as the crucial factors which govern the assembly/disassembly forces. The identification of these parameters was done through a design of experiments. These parameters were then varied simultaneously in a metamodel based optimization which had an objective to minimize the variation in forces observed for an insert when the maximum material condition and the least material conditions are considered. The result for the enhanced design of the insert was then stated in terms of the ratio of these identified parameters. The modified design of the insert not only enables the manufacturer to have better performance, but also reduces the amount of plastic material required for manufacturing of the insert.

Automotive companies are exposed to tough competition and therefore strive to design better products in a cheaper and faster manner. This challenge requires continuous improvements of methods and tools, and simulation models are therefore used to evaluate every possible aspect of the product. Optimization has become increasingly popular, but its full potential is not yet utilized. The increased demand for accurate simulation results has led to detailed simulation models that often are computationally expensive to evaluate. Metamodel-based design optimization (MBDO) is an attractive approach to relieve the computational burden during optimization studies. Metamodels are approximations of the detailed simulation models that take little time to evaluate and they are therefore especially attractive when many evaluations are needed, as e.g. in multidisciplinary design optimization (MDO). In this thesis, state-of-the-art methods for metamodel-based design optimization are covered and different multidisciplinary design optimization methods are presented. An efficient MDO process for large-scale automotive structural applications is developed where aspects related to its implementation is considered. The process is described and demonstrated in a simple application example. It is found that the process is efficient, flexible, and suitable for common structural MDO applications within the automotive industry. Furthermore, it fits easily into an existing organization and product development process and improved designs can be obtained even when using metamodels with limited accuracy. It is therefore concluded that by incorporating the described metamodel-based MDO process into the product development, there is a potential for designing better products in a shorter time.

The United States Navy is committed to developing technology for an All-Electric Ship (AES) that promises to improve the affordability and capability of its next-generation warships. With the addition of power-intensive 21st century electrical systems, future thermal loads are projected to exceed current heat removal capacity. Furthermore, rising fuel costs necessitate a careful approach to total-ship energy management. Accordingly, the aim of this research is to develop computer tools for early-stage design of shipboard energy distribution systems. A system-level model is developed that enables ship designers to assess the effects of thermal and electrical system configurations on fuel efficiency and survivability. System-level optimization and design exploration, based on these energy system models, is challenging because the models are sometimes computationally expensive and characterized by discrete design variables and discontinuous responses. To address this challenge, a classifier-guided sampling (CGS) method is developed that uses a Bayesian classifier to pursue solutions with desirable performance characteristics. The CGS method is tested on a set of example problems and applied to the AES energy system model. Results show that the CGS method significantly improves the rate of convergence towards known global optima, on average, when compared to genetic algorithms. / text

Multidisciplinary design optimization (MDO) can be used as an effective tool to improve the design of automotive structures. Large-scale MDO problems typically involve several groups who must work concurrently and autonomously for reasons of efficiency. When performing MDO, a large number of designs need to be rated. Detailed simulation models used to assess automotive design proposals are often computationally expensive to evaluate. A useful MDO process must distribute work to the groups involved and be computationally efficient. In this thesis, MDO methods are assessed in relation to the characteristics of automotive structural applications. Single-level optimization methods have a single optimizer, while multi-level optimization methods have a distributed optimization process. Collaborative optimization and analytical target cascading are possible choices of multi-level optimization methods for automotive structures. They distribute the design process, but are complex. One approach to handle the computationally demanding simulation models involves metamodel-based design optimization (MBDO), where metamodels are used as approximations of the detailed models during optimization studies. Metamodels can be created by individual groups prior to the optimization process, and therefore also offer a way of distributing work. A single-level optimization method in combination with metamodels is concluded to be the most straightforward way of implementing MDO into the development of automotive structures.