The paper describes the design for manufacturability of a prototype product as part of a manufacturing engineering capstone course. The product chosen was a vertically launched unmanned aerial vehicle (UAV)-the "Flying Eye." The Flying Eye is an autonomous parafoil surveillance platform quipped with sensors, controllers, mechanical components, and software. Once the autonomous UAV is deployed, it is designed to follow a predetermined flight path down to the ground. The design effort of the prototype device took place over a three-year period as a collaborative effort between the Aerospace Engineering and Industrial and Manufacturing Engineering departments at California Polytechnic State University. This project proved to be an excellent tool for the "project-based learning environment," which is the focus of Cal Poly's hands-on engineering programs. Details of the Flying Eye project and lessons learned during the course of this educational experience are provided.
The manufacturing systems capable of producing several products simultaneously are frequently subject to changes in product types due to demand¯uctuations. In such systems a product¯exible manufacturing planning and control (MPC) strategy is needed to change from one product type to another with minimum deterioration to system performance levels. The objective of this research is to develop a systematic analysis and evaluation approach in order to compare the MRP-push and JIT-pull strategies quantitatively based on a product¯exibility measure. A new product¯exibility measure is developed based on the sensitivity to change concept and presented together with the implementation in a real manufacturing system. Simulation is used to compare the performance of a JIT-pull with an MRP-push strategy based on performance measures, e.g. manufacturing lead time, work-in-process inventory, backorders, machine utilization and throughput. The performances of the two strategies are evaluated in two scenarios: (i) a single product; (ii) a second product is added (the ® rst product being simple and the second being complex in terms of processing). The impacts of adding the second product on the performance measures for the push and pull strategies are then assessed. A multi-attribute evaluation scheme is used to compare the two strategies where the attribute values are the change in performance measures as the second product is added. The proposed product¯exibility measure is utilized in the interpretation of the results.
Abstract. This paper evaluates the tardiness performance of a complex dynamic manufacturing environment. These sampling-based adaptive heuristic in a dynamic manufacturing 'changes' include machine breakdowns, unavailability of environment. A test bed, following a real world manufacturing material and other resources to perform manufacturing system, has been developed. The proposed algorithm has been operations, changes in demand, customer priorities and implemented in this simulated cnvironmenL After fme tuning the algorithm, it has been tested in various shop conditions. required delivery dates. To be able to respond to theseThe results of these simulation studies arc summarized.changes effectively, a scheduling system must be able to develop new feasible schedules in a short time. Further more, the proposed schedule should provide a reasonably 1. Introduction good performance of the system for the selected criteria. Our experience with the feedback heuristic (Kiran and The effective utilization of manufacturing systems Alptekin, 1989) indicates that the feedback heuristic requires efflcient scheduling and control systems. Such is a viable alternative for the scheduling of flexible systems must be capable of responding to changes in a manufacturing systems.
In this paper we describe a hybrid architecture that integrates artificial neural networks and knowledge-based expert systems to generate solutions for the real time scheduling of flexible manufacturing systems. The artificial neural networks perform pattern recognition and, due to their inherent characteristics, support the implementation of automated knowledge acquisition and refinement schemes through a feedback mechanism. The artificial neural network structures enable the system to recognize patterns in the tasks to be solved in order to select the best scheduling rule according to different demands. The knowledge-based expert systems are the higher order elements which drive the inference strategy and interpret the constraints and restrictions imposed by the upper levels of the flexible manufacturing system control hierarchy. The level of self-organization achieved provides a system with a higher probability of success than traditional approaches.
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