The development and evaluation of a customized computer-aided cost model for estimating, analyzing, and selecting an autoclave lay-up tooling fabrication process for the limited production of aerostructure products.

摘要

he primary problem of this study was to develop and evaluate a customized cost model which enabled the selected aerospace company (Martin Marietta Aero and Naval Systems) to estimate, analyze, and select an autoclave lay-up tooling fabrication process for the limited production of aerostructure products.;A systematic procedure was used for conducting this study. This study was conducted in accordance with the methods and strategies of action research as presented by Isaac and Michael (1981, p. 55). The purpose of action research involves the development of new skills and/or new approaches to solving problems with direct application to both the academic and industrial sectors of our society.;The study was divided into five major sections: (1) the definition phase, during which the problem was defined and goals were identified; (2) the selection phase, for the selection of the computer equipment; (3) the design phase, where the cost model was formulated; (4) the training phase, of the participants at the aerospace company, the consultants in the aerostructure autoclave tooling industry, and a professor in aerospace engineering; and (5) the evaluation phase, where the evaluation instrument was designed and administered.;The researcher accepted the research hypothesis, including the ten sub-hypotheses, that the customized computer-aided cost model provided MMA&NS with a means of estimating, analyzing, and selecting an autoclave lay-up tooling fabrication process for the limited production of aerostructure products.;The evaluation team's rating of 3.316, out of a potential maximum of 4.000, allowed the researcher to justify the acceptance of the research hypothesis. Two of the sub-hypotheses rated the highest (3.592 and 3.515) of all were: (1) the ability to accumulate and to reuse data for increased productivity, and (2) the potential to justify the time, efforts, and costs of operating the model.;Based on the fact that the data set was curvilinear, the sample size was small, and the variance among the scores was small, the Pearson r correlation coefficient