Browsing by Subject "Manufactures -- Defects"
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Item Knowledge Integration Strategies in Defect Diagnosis(Texas Tech University, 1995-08) Hsieh, Sheng-JenDefect diagnosis—defined here as the process of evaluating and locating the true cause of a defect type—has been an island of automation and a time consuming and non-productive task. Needed are efficient and cost-effective methods which facilitate the task. The purpose of this research is to develop hybrid mathematical/simulation models and algorithms to diagnose defects with (I) multiple causes, (2) unknown cause probability, and (3) uncertain knowledge, with the objective of minimizing cost and number of trials. The research problem is tackled in three phases. First, a diagnostic tree structure is proposed to (I) categorize diagnostic knowledge into sets of cause-effect relationships; and (2) simultaneously incorporate both testing costs and production loss. Then propositions for knowledge integration are developed to integrate initial and current knowledge, which correspond to the strength values for each edge within the diagnostic tree. Through the integration process, initial uncertain knowledge will be gradually pruned with newly arriving certain knowledge as the diagnosis task continues. Finally, primary elements of the conceptual decision process for troubleshooting defects are represented in a flow chart. Based on these ingredients, a linear multi-stage mathematic model is formulated, and a variety of knowledge integration strategies proposed. Second, the problem of searching for the cause of a defect is formulated as a search problem where the estimated cause variable resembles a sensor function, and the true cause variable represents the target function. Therefore, the problem becomes a mapping of one function to the other. Several learning algorithms are created based on these developments. Then the algorithms are transformed into a probabilistic learning model where Monte-Carlo simulation is utilized to assess the performance of each algorithm. Primary propositions, lemmas and analytic properties are developed in this phase. Third, a variety of experiments are used to investigate and compare the algorithms' (I) learning and fault-tolerant properties, (2) cost and trials performance, and (3) computational efficiency. Experimental results indicate that the proposed methods are superior to general techniques such as sequential and random searches in minimizing number of trials and costs. In addition, the proposed methods also contain learning and fault-tolerant properties.Item Monitoring and detecting shifts in the mean in quality levels for a production environment with properties found in the geometric Poissin process(Texas Tech University, 1999-05) Chen, Ching-WenFor statistical process control, an important property is the underlying statistical model that is assumed to govern the defect generation process. For control of defects, the assumption is made that the defects follow a Poisson distribution. However, frequently the process is more complex and the distributions of defects are more appropriately modeled by the compound Poisson distribution. A defective item may have more than one defect that cause the item to be defective. The occurrence of defective items may follow a Poisson distribution. If an item is defective, the number of defects per item will follow another distribution. In this research, it will be assumed that given an item is defective, the number of defects follows the geometric distribution. Thus, the distribution of defects over time is the Poisson compounded with the geometric. From the viewpoint of quality control, process quality can be improved by moving special causes. Two broad types of special causes, transient and persistent special causes, are reported in the literature. Two proposed methods, the empirical Bayes control chart for the geometric Poisson random variables and the geometric Poisson CUSUM control scheme, aim at removing both transient and persistent special causes. Both proposed approaches utilize the historical information concerning the process. The former can detect shifts in much wider situations. The latter would be more sensitive to small sustained shifts caused by persistent special causes. Using the simulated data, the performance of these proposed quality control methods and classical Poisson-based control methods is compared. The test results show an underestimation of Type I error and the number of false alarms generated, if the underlying defect distribution is wrongly assumed. Although two alternatives in detecting mean shift or structure change for the geometric Poisson random variables are proposed, the relationship between these two is complementary.Item The integrated economic production quantity model for inventory and quality(Texas Tech University, 2004-12) Ittharat, TharatDetermining the optimal production lot sizing has been widely used by the classical economic production quantity (EPQ) model. However, the analysis for finding an EPQ has several weaknesses which lead many researchers to make extensions in several aspects on the original EPQ model. The cost of quality is one of good aspects to be added to the EPQ model since there are a lot of costs incurred such as prevention, appraisal, failure, warranty (products returned from customer), inspection, and rework costs. The integration of cost of quality and EPQ should be able to link and classify each cost of quality in practical way of inventory management. This paper deals with the finite production inventory model integrated with quality costs for a single product imperfect manufacturing system. This problem assumes that the product quality is not always perfect unlike the traditional EPQ model. The defect rate is considered as a proportion of the production rate, and defective items are reworked at some cost either before, or after sales (products returned by the customer). The prevention, appraisal, and inspection costs have somewhat inverse relationships to the defective rate. The replacement rate from products returned by the customer is also considered to be another random variable with known failure rate in the field. The purpose of this research is to investigate the quality cost factors in the economic production quantity inventory model in order to find the optimal lot size. The objective is to develop mathematical models in order to minimize the annual total cost of inventory and quality.