Choice of an experimental design is an important concern for most researchers. Judicious selection of an experimental design is also a weighty matter in Robust Parameter Design (RPD). RPD seeks to choose the levels of fixed controllable variables that provide insensitivity (robustness) to the variability of a process induced by uncontrollable noise variables. We use the fact that in the RPD scenario interest lies primarily with the ability of a design to estimate the noise and control by noise interaction effects in the fitted model. These effects allow for effective estimation of the process variance — an understanding of which is necessary to achieve the goals of RPD. Possible designs for use in RPD are quite numerous. Standard designs such as crossed array designs, Plackett-Burman designs, combined array factorial designs and many second order designs all vie for a place in the experimenters tool kit. New criteria are developed based on classical optimality criteria for judging various designs with respect to their performance in RPD. Many different designs are studied and compared. Several first-order and many second order designs such as the central-composite designs, Box-Behnken designs, and hybrid designs are studied and compared via our criteria. Numerous scenarios involving different models and designs are considered; results and conclusions are presented regarding which designs are preferable for use in RPD. Also, a new design rotatability entity is introduced. Optimality conditions with respect to our criteria are studied. For designs which are rotatable by our new rotatability entity, conditions are given which lead to optimality for a number of the new design comparison criteria. Finally, a sequential design-augmentation algorithm was developed and programmed on a computer. By cultivating a unique mechanism the algorithm implements a D_s-optimal strategy in selecting candidate points. D_s-optimality is likened to D-optimality on a subset of model parameters and is naturally suited to the RPD scenario. The algorithm can be used in either a sequential design-augmentation scenario or in a design-building scenario. Especially useful when a standard design does not exist to match the number of runs available to the researcher, the algorithm can be used to generate a design of the requisite size that should perform well in RPD. / Ph. D.

A research report submitted to the Faculty of Engineering and the Built Environment, University of the Witwatersrand, Johannesburg, in partial fulfilment of the requirements for the degree of Master of Science in Engineering. Date: September 2017, Johannesburg / The focus in this research investigation is to investigate the Process Parameter Optimisation in Laser Beam Forming (LBF) process using the 4.4 kW Nd: YAG laser system – Rofin DY 044 to form 200 x 50 x 3 mm3 mild steel - AISI 1008 samples. The laser power P, beam diameter B, scan velocity V, number of scans N, and cooling flow C were the five input parameters of interest in the investigation because of their influence in the final formed product. Taguchi Design of Experiment (DoE) was used for the selection and combination of input parameters for LBF process. The investigation was done experimentally and computationally. Laser Beam Forming (LBF) input parameters were categorised to three different levels, low (L), medium (M), and high (H) laser forming (LBF) parameters to evaluate parameters that yield maximum bending and better surface finish/quality. The conclusion drawn from LBF process is that samples which are LBFormed using low parameter settings had unnoticeable bending and good material surface finishing. On the other hand, samples LBFormed using medium parameters yielded visible bending and non-smooth surface finishing, while samples processed using high LBF parameters yielded maximum bending and more surface roughness than the other two process parameters. / MT2018

近年來，隨著消費意識的提升，食品業者正面臨最嚴格的考驗，品質 產 生變異，將會反應在銷售量上，嚴重影響企業的經營。而目前台灣食品 造業者，有許多廠家仍停留在經驗掛帥、老式的檢驗品質階段，對於源流 瑊z的觀念仍不甚了解，致使品質提昇的效率不盡理想。有鑑於此，本研 究B用田口玄一博士生產線外品質工程理念架構，靈活搭配各種工具（如 系統洈k、直交表），以使田口方法更臻完善。同時利用S/N比（signal- to-Noes Ratio）變異數分析兩種方法進行資料解析，提供食品製造業者 最佳生ㄡ捰X。最後，乃以本人所親自參與之食品製造業品質改善研究實 驗為例，憭什ㄟ筆嗾膋熊痕G報告外，另詳述記載寶貴的實驗經歷，可進 一步提供相鶻t商作為改善品質的借鏡。

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The quality of products is the biggest competitive advantage that a company can achieve. The definition states that quality is the degree of compliance requirement with a set of inherent characteristics. The importance of quality have very increased in recent years - quality is a decisive factor for stable economic growth of enterprises, management quality is the most important protective factor of the loss of markets, quality is a major source of saving materials and energy, the quality affects of macroeconomic indicators, the quality is a limiting factor for the sustainable development , quality and consumer protection are connected vessels. Quality costs are the total costs incurred by producers, users and companies associated with the quality of the product. The quality costs of the manufacturer is divided into four basic groups: the costs of prevention, assessment, costs of external defects and internal defects.