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This thesis explores the use of statistically valid process improvement tools in low-volume applications. Setting out the following research questions: How can the Six Sigma Measure and Analyse phases of a chronic quality problem be statistically validated in a low-volume process? How can a statistically valid approach for process control be implemented in a low-volume process? And how can this tool be extended to fit multivariate processes and can the calculation of control parameter adjustments be automated? In answer, the thesis presents an enhanced PROcess VAriation Diagnosis Tool (PROVADT) method, driving a Six Sigma improvement project through the Measure and Analyse phases. PROVADT provides a structured sampling plan to perform a Multi-Vari study, Isoplot, Gage R&R and Provisional Process Capability in as few as twenty samples and eighty measurements, making the technique suited to low-volume applications. The enhanced PROVADT method provides a Gage R&R without confounded variation sources, as was the case in the original method, and its practical application was demonstrated through two case studies. Process control tools for low-volume, high-variety manufacturing applications were developed. An adjustable traffic-light chart, with control limits linked to tolerance and simple decision rules, was used for monitoring univariate processes. This tool, the Set-Up Process Algorithm (SUPA), uses probability theory to provide 98% confidence that the process is operating at a pre-specified minimum level of Cp in as few as five samples. SUPA was extended to deal with high-complexity applications, resulting in multivariate SUPA (mSUPA). mSUPA maintains SUPA’s principles, but presents the information about multiple process features on one chart, rather than multiple univariate charts. To supplement the mSUPA tool, a theoretical method for calculating optimal process adjustment when a multivariate process is off-target was introduced, combining discrete-event simulation and numerical optimisation to calculate adjustments.

The work develops the application of evolutionary algorithms in the domain of automotive heat exchanger design. The principal employed is that of computer regulated changes to a physical model which attempts optimisation using methods analogous to biological evolution. It shows that the use of airside fins with differing louvre angles can enhance the performance of automotive heat exchangers by a useful margm. This has been achieved with a wind tunnel model that allows automatic configuration of the louvre angles, and novel instrumentation allowing heat transfer performance to be assessed in terms of shear and drag forces acting on the louvres. During the investigation an important coupling between the behaviour of adjacent louvres was discovered, manifested as a loss in useful shear force at around ±12° relative angle. The work as a whole shows that specific louvre angle selections and quantitative estimates of the potential performance gains could be made with the following improvements to the physical model and search algorithms. The number of louvre rows should be doubled (to 16) to better represent typical matrices and the instrumented louvres should be centrally positioned in the air stream. Improved data filtering is required for reliable operation and the specific figure of merit has been shown to be an important factor in the optimisation process. A parallel area of application for the optimisation strategies was the solution of the Wilson plot problem. This represents a novel approach to the analysis of heat exchanger experimental test data where an alternative curve fitting and visualisation format allows more accurate models to be established. By these methods functions defining heat transfer coefficients for both sides of a heat exchanger may be determined that give a fit to experimental data to within less than 1. 5% on measured overall heat transfer coefficient.

The adverse effects of contaminated outdoor air have been recognized and subject to control for many years. More recently environmental engineers and health professionals have become cognizant of the hazards associated with contaminated indoor air. It is now understood that contaminated indoor air negatively impacts human health, worker productivity, and physical property. Volatile organic compounds (VOCs) are a common class of indoor air pollutants. Building materials such as treated wood, pressed-wood products, wallboard, sealants, adhesives, floor coverings, and paints can be sources of VOC emissions. The knowledge-base necessary to develop effective solutions to indoor air quality problems requires an understanding of the emissions behavior of indoor materials. Environmental chambers are often utilized to characterize indoor material as sources of VOC emissions to indoor air. Chamber studies, although expensive and time consuming, can be utilized to provide estimates of the rates at which a particular material emits VOCs under a specific set of environmental conditions. By fitting curves to emissions data obtained through chamber studies, VOC emissions models have been constructed. These models are frequently empirical and as a consequence, 1) apply only to the specific material and environmental conditions investigated, 2) provide little understanding of the source/sink characteristics of the material, and 3) provide little knowledge of the mass transfer processes governing emissions behavior. As a result, our understanding of the mechanisms that control VOC emissions from indoor materials remains rudimentary. Physically-based models that describe the emissions characteristics of building materials would greatly facilitate the process of improving indoor air quality. Evidence exists suggesting well-established fundamental mass transfer mechanisms govern emissions from indoor materials. Of the various mechanisms governing emissions behaviors, diffusion appears to be one of the most significant. The primary objective of this research was to demonstrate that the VOC emissions source behavior of a diffusion-controlled homogenous building material could be predicted using a mechanistic mathematical model. A commercial grade sheet vinyl flooring (VF) was selected for study because VF is present in many residential and commercial buildings, is relatively homogenous, and has been shown to emit hazardous organic chemicals. If successful, this research would demonstrate that the proposed strategy could be generalized to other VOC sources using appropriately constructed mathematical models. Satisfying the research objective required development of a physically-based model to predict gas-phase VOC concentrations resulting from exposure to a diffusion-controlled material. Key parameters for this model are the solid-phase diffusion coefficient, D; the solid/air partition coefficient, K; and the initial solid-phase VOC concentration, C0. D and K have been previously quantified for only a few indoor materials and methods for determining C0 are rudimentary. Therefore, this research project required development and execution of methods for quantifying D, K, and C0. D and K were quantified using a recording microbalance. C0 was evaluated using a new technique of cryogenic milling followed by fluidized bed desorption. The model was validated by exposing a VF sample in an environmental chamber and directly measuring gas-phase VOC concentrations resulting from mass transfer from the solid material. Further model validation was achieved by directly measuring the VOC concentration profiles after exposure in environmental chambers. Because the key model parameters were quantified independently of chamber studies, the model validation process provided a rigorous test of the validity of the mass transfer model in particular and of the source characterization strategy in general. The results of this research contribute to our understanding of the fundamental mechanisms that govern emissions of VOCs from vinyl flooring and provide a sound theoretical foundation for characterization of a wide range of other sources of indoor VOCs. This understanding could facilitate product reformulation strategies aimed at preventing or reducing indoor air contamination. Mass transfer models could also be utilized to develop standards for the environmental performance of indoor materials. The proposed approach will prove useful in conjunction with broader studies on sick building syndrome to identify sources that may have a critical impact on the health and comfort of building occupants.

By the use of high performance solid-state infrared emitting diodes, matched infrared detectors, and a single chip 8-bit microprocessor, an in-process infrared densitometer has been designed and constructed. The device is capable of recording the build-up of optical transmission density on an exposed film sample as it develops, with no effect on the normal development process. The most novel feature of this system is a special development chamber with eleven built-in infrared densitometers. These densitometers are located as to read every other step of a standard Kodak #2 step-tablet exposure, and to allow the passage of developer across the film sample. As an example, every other step of a 21-step exposure can be measured to produce a series of eleven density readings in approximately 22 milliseconds. These measurements can be repeated from once every second to once per minute at any time during the development process. The microprocessor that controls the infrared densitometer has memory capacity to store the data required to generate 187 complete D-Log exposure curves. At the completion of processing, the density readings recorded earlier are available for user viewing on a large seven segment digital display system, or can be reproduced on a line printer. D-Log exposure curves obtained from the device are in good agreement with curves obtained by conventional means. The device has a useful range of 0 to 3.00 density units with an accuracy of +/- .02 density units.