Browsing by Subject "Flexible manufacturing systems"
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Item Evolution of expert systems(Texas Tech University, 1993-12) Culebro, Joaquín Marcos PalaciosExpert systems are computer programs for providing expertise emulative of that which might be expected from human experts in solving complex problems for which analytical solutions are not available. Evolution of an expert system refers to the initial development of the system and its continuing modification in order to improve its performance. Any modifications made to an expert system have the potential of producing undesirable logical errors and side-effects that are difficult to find or prevent. Although much research has focused on facilitating the evolution of expert systems, most of the limitations still exist. This dissertation proposes an approach for structuring and evolving expert systems for applications in which the provision of the desired expertise is beyond the reach of either analytical or traditional heuristic approaches, but in which the knowledge domain is causally connected and the relevant causality can be expressed in procedural form. The research vehicle used is that of a hypothetical manufacturing system in which products of different types use some of the same workstations, and some of the product types loop back to workstations that they have previously used. The expertise sought is that of scheduling starts of products into the first stage of production so as to yield a stream of output that satisfies a user-specified balance among a variety of business performance measures including timeliness of production output.Item Heuristics for flexible flowshop scheduling problems(Texas Tech University, 1993-08) Leung, Cherng-yeeA flexible flowshop consists of a number of work centers, each having one or more parallel machines. A set of immediately available jobs has to be processed through the ordered work centers. A job is processed on any and only one of the parallel machines at each of the work centers?) Structurally, a flexible flowshop represents a generalization of the simple flowshop and the identical parallel machine shop. For the case of having the same number of identical parallel machines at every work center, two approaches are developed: the para-flow approach and the flow-para approach. Two situations regarding the job route are examined. These are the partially flexible job route situation and the completely flexible job route situation. (The objective of this research is to find heuristics that minimize the makespan of the problem in reasonable computation time. A computer experiment verifies that the para-flow approach and the flow-para approach outperform published algorithms. Problem size includes three elements: the number of jobs, the number of work centers, and the number of parallel machines at each work center. By fixing any two of the three elements, the trend caused by the third element can be analyzed. A trend analysis of the proposed algorithms has been conducted.Item Intelligent process quality control and tool monitoring in manufacturing systems(Texas Tech University, 1994-05) Chinnam, Ratna BabuThe work presented is best characterized as an investigation of neural networks for effective process quality control and monitoring in automated manufacturing systems. The research addresses two basic questions. The first question is whether neural networks have the potential to "identify" cause-effect relationships associated with advanced manufacturing systems to achieve real-time quality control? The second question is whether it is possible to use neural networks to develop effective reliability based real-time tool condition monitoring models for manufacturing systems? Both multilayer feedforward perceptron networks and radial basis fiinction networks are used in novel configurations to achieve real-time process parameter design. The models developed are capable of monitoring process performance characteristics of interest by building empirical based relationships to relate the process response characteristics with controllable and uncontrollable parameters, simultaneously. Using these empirical models and the levels of the uncontrollable parameters obtained through sensors, the quality controller provides levels for the controllable parameters that will lead to the desired levels ofthe quality characteristics in real-time. In general, the quality controller models were able to provide levels for the controllable variables that resulted in the desired process quality characteristics. Test results are discussed for several simulated production processes. A validity index neural network based approach was developed to automate the toolwear monitoring problem. In contrast to the contemporary approaches that basically deal with a classification problem, classifying a given tool as either fresh or worn, the model derived from radial basis function networks predicts the conditional probability of tool survival in accordance with the traditional reliability theory, given a critical performance plane, using on-line sensory data. In general, the radial basis fimction networks performed extremely well in time-series prediction, when tested on actual data collected from a drilling process. The validity index neural network is extended to arrive at the desired conditional tool reliability.Item Part selection in a dynamic job-shop type flexible manufacturing system (FMS)(Texas Tech University, 1996-08) He, YuminThe research investigates the part selection problem which is defined as dynamic determination of one part among all available parts in a dynamic job-shop type FMS. A heuristic part selection algorithm, namely the state dependent algorithm, is developed by applying dynamic information in part selection to achieve the objective of increasing productivity. The earliest-due-date part selection algorithm with the objective of meeting due dates is also investigated for the part selection problem. A hypothetical FMS is formulated for the investigation. Computer simulation is used for evaluation. The first-in-first-out part selection algorithm is utilized as a comparative algorithm. Also evaluated is the state dependent algorithm under various scheduhng conditions formed by a set of robot scheduling rules and a set of machine scheduling rules. The analysis of results is conducted by computing absolute and relative improvements and by performing statistical tests for significant difference in absolute improvement for criteria based on job completion times, in-process inventory, and utilization, and criteria based on job due dates. Investigative results verify the effectiveness of the state dependent algorithm and the earliest-due-date algorithm for increasing productivity and for meeting due dates in part selection, respectively, and the effectiveness of the earliest due date scheduling rule for increasing productivity in scheduling. The research is also carried out to investigate the integration of part selection and scheduling in the dynamic job-shop type FMS. A heuristic robot scheduling rule is developed for the integration, which uses dynamic information in robot scheduling. Results reveal the effectiveness of the integration by utilizing the state dependent algorithm, the heuristic robot scheduling rule, and the earliest due date machine scheduling rule in comparison to other integration cases investigated and the comparative non-integration case. A good combination of part selection and scheduling is also identified which gives good FMS performance regarding both increasing productivity and meeting due dates in comparison to the comparative case. Recommendations for practical applications are made based on the investigative results. Future research ideas are also discussed.Item Reactive parallel machine scheduling using hybrid-intelligence to minimize weighted tardiness, makespan, and cost of rescheduling(Texas Tech University, 2004-08) Phonganant, SupphasakNot availableItem Scheduling criteria in flexible manufacturing systems(Texas Tech University, 1986-08) Rajagopalan, RameshNot available