Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1994. / Vita. Abstract. Includes bibliographical references (leaves 216-220). Also available via the Internet.

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 136-142). Also available via the Internet.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1966. / Typescript. Vita. Description based on print version record. Includes bibliographical references (p. 217-221).

No description available.

In the past, tool life tests have been performed using a conventional Taylor testing technique. This methodology is expensive and time-consuming. It requires wearing a number of tools until the tool failure criterion has been reached. A number of short-term tests designed to replace the Taylor test have been proposed but they suffer from a number of drawbacks. Many of these tests are performed under non- standard cutting conditions or require special workpiece preparation or equipment. As a result, tool life models developed from these tests are of limited usefulness in predicting tool failure times for conventional machining operations. A methodology is required which combines the time and cost advantages of non-conventional tests with statistical validity and robustness. In this research, two short-term tests are presented which are based on the Taylor test. Response surface models are used to develop the parameters of Taylor's tool life equation. The tests are shortened by using regression equations of flank wear data to predict the tool failure time without wearing the tool to failure. The two methods, abbreviated conventional testing and sequential composite testing, are statistically validated and compared with the Hill Taylor test. The results show that these tests can accurately predict tool life and the resulting Taylor models are not significantly different from those estimated by conventional means. / Master of Engineering

Under heterogeneous variance, conventional optimal response surface experimental designs for estimating location models are no longer optimal. To address this deficiency. D and Q criteria appropriate under heterogeneous variance are developed. These criteria are then applied to demonstrate the improved efficiency of a proposed two-stage experimental design procedure. In the proposed procedure the first stage estimates the heterogeneous variance structure and the second stage augments the first stage to produce a total design that is Q or D-optimal for the estimated variance structure. The Q and 0 criteria not only direct the total design, but also suggest optimal designs for estimating dispersion effects in the first stage. The efficiency of the proposed two-stage procedure is further enhanced if certain mild assumptions concerning variance structure are valid. These assumptions are formulated as a prior distribution and effectively stabilize the variance estimation in the first stage through a Bayes estimator. / Ph. D.

No description available.

The estimation of a particular matrix of coefficients of a second-order polynomial model was shown to be important in Response Surface Methodology (RSM). This led naturally to designing RSM experiments for best estimation of these coefficients as a primary goal. A design criterion, D_S-optimality, was applied to several classes of RSM designs to find optimal choices of design parameters. Further, previous results on D-optimal RSM designs were extended. The designs resulting from the use of the two criteria were compared. Two other design criteria were also studied. These were IV, the prediction variance of ŷ integrated over a region R, and IV*, sum of the variances of ∂ŷ/∂<u>α</u> again integrated over R. Three different choices of the region R were used. The object of the study was not only to identify optimal choices of design parameters, but also to compare the resulting designs with those obtained using the determinantal criteria. An extension of a method for constructing D-optimal designs was used to construct D_S-optimal central composite designs. This involved viewing the design points as having continuous weights. D_S-best central composite designs were constructed either analytically or numerically for a fixed axial point distance. The results of previous work by other authors were extended for D-optimality by varying the axial point distance. Other design classes studied were Box-Behnken, equiradial, and some small composite designs. The novel study of IV and the extended IV, called IV*, was done for each of the four design classes mentioned previously. The results of the study were presented graphically, or tabularly. The best designs according to IV and IV* were compared with the D_S-best designs. Composite designs performed well in all criteria, with the central composite designs performing best. The Box-Behnken and equiradial seemed to suffer from a lack of flexibility. The D_S-best designs agreed well with the designs suggested by the IV* criteria. / Ph. D. / incomplete_metadata

The principal objective of this study was to compare the results of a proposed method based upon the response surface model to the Taguchi method. To modify the Taguchi method, the proposed model was developed to encompass the following objectives. The first, with the exception of the Taguchi inner array, was obtain optimal design variable settings with minimum variations, at the same time achieving the target value of the nominal-the best performance quality characteristics. The second was to eliminate the need for the use of a noise matrix (that is, the Taguchi outer array), resulting in the significant reduction of the number of experimental runs required to implement the model. The final objective was to provide a method whereby signal-tonoise ratios could be eliminated as performance statistics. To implement the proposed method, a central composite design (CCD) experiment was selected as a second-order response surface design for the estimation of mean response functions. A Taylor's series expansion was applied to obtain estimated variance expressions for a fitted second-order model. Performance measures, including mean squared error, bias and variance, were obtained by simulations at optimal settings. Nine test problems were developed to test the accuracy of the proposed CCD method. Statistical comparisons of the proposed method to the Taguchi method were performed. Experimental results indicated that the proposed response surface model can be used to provide significant improvement in product quality. Moreover, by the reduction of the number of experimental runs required for use of the Taguchi method, lower cost process design can be achieved by use of the CCD method. / Graduation date: 1993

Thesis (Ph. D.)--Virginia Polytechnic Institute and State University, 1992. / Vita. Abstract. Includes bibliographical references (leaves 181-186). Also available via the Internet.