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A methodology for the design of operational control aspects of an Automatic Guided Vehicle (AGV) based material handling system is presented. The methodology, which is composed of an integrated model of an AGV based system, was implemented using simulation techniques. The model views a manufacturing function as consisting of machining, queueing, and moving of parts in a shop and that these components of manufacturing must be integrated and coordinated if the production objectives of an enterprise are to be realized. A machining center is modeled as a physical region of a plant and it consists of machines for part processing and capacitated queues in which inbound and outbound parts reside, queueing for machining or handling resources. Automatic guided vehicles provide the transport mechanism required to interface the machining centers. A network approach is employed to represent the layout of the facility, including the location of departments, input and output queues in each department, and the layout of the guidance system on which the AGVs operate. The network approach, along with the coordinate system are employed for modeling the actual translation of vehicles and parts through the shop. The travel time of vehicles and parts between points depends on vehicle speed and the prevailing traffic condition along the path of travel. Several shop control strategies in the application of AGVs have been modeled, implemented, and their effects on shop performance demonstrated. Among these factors are vehicle dispatching, vehicle routing, unit load size selection, job sequencing, shop loading, queue constraints, and capacity constraints due to vehicles and machines. A job in the shop is considered to consist of one or more parts grouped in portable unit load sizes. Therefore, it is unit loads rather than jobs that make the flow transitions. The results of the simulation experiments conducted indicated vital control elements in the design of AGV systems. Through a series of output statistics on system performance, the model provides an easy to use tool to analyze, evaluate, and design of AGV based manufacturing system. / Ph. D.

The overall purpose of this research is to develop a real-time ultrasound based system for controlling robotic weld quality by monitoring the weld pool. The concept of real-time weld quality control is quite broad, and this work focuses on weld penetration depth monitoring and control with laser ultrasonics. The weld penetration depth is one of the most important geometric parameters that define the weld quality, hence remains a key control quantity. This research focuses on the implementation and optimization of the laser phased array generation unit and the development of signal analysis algorithms to extract the weld penetration depth information from the received ultrasonic signals. The system developed is based on using the phased array technique to generate ultrasound, and an Electro-Magnetic Acoustic Transducer (EMAT) as a receiver. The generated ultrasound propagates through the weld pool and is picked up by the EMAT. A transient FE model is built to predict the temperature distribution during welding. An analytical model is developed to understand the propagation of ultrasound during real-time welding and the curved rays are numerically traced. The cross-correlation technique has been applied to estimate the Time-of-Flight (ToF) of the ultrasound. The ToF is then correlated to the measured weld penetration depth. The analytical relationship between the ToF and penetration depth, obtained by a ray-tracing algorithm and geometric analysis, matches the experimental results. The real-time weld sensing technique developed is efficient and can readily be deployed for commercial applications. The successful completion of this research will remove the major obstacle to a fully automated robotic welding process. An on-line welding monitoring and control system will facilitate mass production characterized by consistency, high quality, and low costs. Such a system will increase the precision of the welding process, resulting in quality control of the weld beads. Moreover, in-process control will relieve human operators of tedious, repetitive, and hazardous welding tasks, thus reducing welding-related injures.