The increase in computing power over the last decade has led to an increase in the use of simulation programs to model real world optimization problems as well as the complexity with which these problems can be modeled. Once a model has been built, an experimental design is often used to determine the effects certain parameters have on the problem trying to determine the good settings that optimize a set of outputs. However, these problems often have a large number of variables or parameters that can be changed with wide value ranges and as these simulation models become increasingly more complex they become computationally expensive to run. Most of these problems are non-linear and may not have a true optimal solution based on the inherent variability in real-world applications and the stochastic simulation model. Evolutionary algorithms are a class of computational optimization techniques that harness the power of the computer to solve a problem. The application of evolutionary search techniques as a simulation optimization technique has yielded reasonable results. However, the algorithm can take a long time evaluating just one set of decision variables owing to replications and computational time of one simulation run and not to mention the sheer number of different sets that have to be evaluated to find good solutions for these complex problems. Linear regression and neural-network meta-models can be used to generate a surface model of the simulation. Evaluating the meta-model is very fast as compared to the simulation model. Therefore, this thesis combines the use of evolution algorithms, simulation models and meta-models to produce a more efficient simulation optimization technique. The two types of meta-models are tested to determine their effectiveness as a meta-modeling technique and the overall effectiveness of finding the best solution.

This project set out to determine an alternative laundering procedure to predict the durability of flame retardant fabrics while decreasing the overall cost and time involved for testing. Fabric was washed using conventional methods to be used as standards. These fabrics were characterized by burning characteristics, elemental analysis, fabric weight, percent weight change, shrinkage, color, and strength. Then fabrics were washed using an alternative method and characterized in the same manner. The alternative laundering procedure involved a programmable machine, Quickwash Plus. Parameters on the machine were varied to simulate x number of washings using a conventional industrial wash.

Businesses are constantly striving for a competitive edge in the economy, and data-driven decision making is crucial to achieve this goal. Four data mining tools, principal component analysis, cluster analysis, recursive partitioning, and discriminant analysis, were used to explore the major factors that contribute to ends down in a rotor spinning manufacturing process. Principal component analysis was used to explore the research question about whether the large number of cotton properties used to classify cotton could be reduced to a significant few. Cluster analysis was used to gain insight about whether there were groups of gins, counties, or classing offices that produced better raw material than others and led to less ends down. The important research question of what raw material properties were affecting ends down was explored with both recursive partitioning and discriminant analysis. Additional research investigated the effect of cotton variety and atmospheric conditions on spinning productivity. Each of the four data mining tools used was informative and offered a different perspective to the overall research question. Several significant factors emerged including humidity, temperature, %DP 555, and uniformity in addition to micronaire and the color properties (+b and Rd). With these results the researcher developed an improvement plan for better control and increased spinning productivity in future operations. A designed experiment is necessary to thoroughly investigate the impact of certain factors beyond the exploratory conclusions obtained from this study.

The surface modification of UHMPE fibers by atmospheric pressure plasma treatments was examined. In one study the aging effects of atmospheric plasma treatments were studied. UHMPE fibers were treated for 0.5 and 1 min with He/O2/air gas and for 2 and 4 min with He/air gas by atmospheric pressure plasma on a capacitively coupled device. The samples were tested for fiber/epoxy interfacial shear strength using the microbond technique at time intervals of 0, 3, 15 and 30 days after initial plasma treatment. Interfacial shear strengths (IFSS) for plasma treated fibers were 2 - 3 times as high as that of the control. The IFSS for the plasma treated fibers remained constant up to 15 days and then decreased afterwards. XPS Analysis and SEM photographs characterized the fiber surface modification. In a second study, delamination phenomena was studied by the transverse compression of seven-fiber bundle UHMPE microcomposites, a peeling test of laminated plain weave UHMPE fabric and tensile shear strength testing of a ten-layer plain weave UHMPE / Epoxy flat panel composite. Results showed a 49% increase in yield modulus of the plasma treated sample compared to the control in the transverse compression test. There was an 82.5% increase in bonding strength for the plasma treated sample during the peel test and a 25.7% increase in interlaminar shear strength of the ten-ply UHMPE composite proving that atmospheric plasma treatments are very effective in surface modification on a microscopic fiber level and a full-scale composite production level.

The purpose of the research was to develop an algorithm to permanently reduce human errors in the healthcare industry. The algorithm will be able to be applied to all healthcare organizations and provide a preventative approach to errors. The research involved looking at past methods of error reduction/prevention. Certain methods proved to be useful in generating the algorithm like the Healthcare Failure Modes and Effects Analysis while others like Root Cause Analysis proved to only have limited success. The algorithm takes a three phase approach to reducing errors. Phase One identifies the potential error producing situations. Phase Two uses error proofing principles and known solution directions to generate solutions while Phase Three uses a new method developed called Solution Priority Number to rank and evaluate the solutions. Throughout the algorithm many worksheets have been developed to aid in a team?s progression through the process. Two case studies were performed. The first case study followed a traditional team through the error prevention process while the second case used the algorithm. When comparing the two cases the team using the algorithm finished the process in shorter time, produced more effective failure modes, and generated a richer set of solutions to error proof the process.

<p>With time as a major competitive factor in today's marketplace, it is crucial to bring products to the market and solve problems in existing products in a swift manner. Rapid prototyping methods are very effective ways of streamlining this effort. However, current rapid prototyping methods generally apply after a solution concept has been selected. Many current conceptual design methods were reviewed and it was shown that a need exists for similar acceleration of the problem identification, idea generation, and concept selection portions of the design process. This would bring the conceptual front end of the design process under the umbrella of what is considered "rapid prototyping". The Ideal Final Result (IFR) of each of the three major conceptual steps was developed to be able to formulate a series of steps to accelerate the process by performing only what is necessary to accomplish the IFR's. The existing methods and custom-designed methods provided for filling in what was needed for each step. An "Ideality-based" concept selection method was developed to make the idea selection process more complete. The entire methodology is explained in detail, and a case study to use the new methodology was performed on an existing, real problem situation to show the useful and accelerated nature of the method. <P>

<p>The contents of this paper present information from work conducted by utilizing plasma technology for fabric treatment. Initially, experimentation was done in low-pressure plasma systems to change the hydrophilic properties of denim fabric. From these experiments, data was collected that proved denim fabric, both sized and desized, could obtain hydrophobicity through a fluorocarbon plasma treatment. Using C3F6 fluorocarbon gas provided a greater level of hydrophobicity than using CF4 plasma gas. The desized denim showed a greater amount of hydrophobicity, in both gases, than the sized denim. These results can be found in chapter IV. The remaining work, found in chapters II and III, focuses on the utilization of atmospheric plasmas on the treatment of nylon 6,6 fabric. Atmospheric plasmas could allow continuous treatment of fabric and shorter treatment times for fabric, all of which would be better suited for industrial processing, more specifically in textiles. Nylon 6,6 fabric was treated with air-He plasma as well as air-He-O2 plasma, where the levels of O2 varied. A significant decrease in tensile strength was found in treatments lasting five minutes or longer. However, micrographs of the fiber surface illustrate instances of surface treatment, even at times less than five minutes. Continuing work on the project includes the building of a prototype machine for industry (currently in progress), the treatment of many different kinds of fabrics, and the evaluation of their mechanical, chemical, and physical properties and functionability thereafter.<P>

<p> ABSTRACTKARNIK, POONAM. Use of Cationized Cotton for Textile Effluent Color Reduction.(Under the direction of Dr. Brent Smith and Dr. Peter Hauser) The liquid effluents from the textile industry mainly consist waters colored by thedyes used in the coloring of textile yarns and fabrics. These dyes can be removed by adsorption onto adsorbing materials like cotton. Waste cotton fibers can be cationized using a quaternary ammonium compound like 3-chloro-2-hydroxypropyltrimethylammonium chloride to their cationic form. This cationic form can be used as the adsorption medium for anionic dyes like acid, direct and fiber reactive dyes. <P>

Texte remanié de: Tesi di dottorato--Storia economica e sociale--Milano--Università commerciale Luigi Bocconi. / Notes bibliogr.

Thesis (doctoral)--Lunds universitet, 1980. / Extra t.p. with thesis statement inserted. Summary in English. Includes bibliographical references (p. 211-215).