The objectives of this research are to (1) develop the relationships between the metrics of quality, cost, lead time, and innovation and the traditional bottom line functions of return on investment and market share; (2) to generate a fundamental and encompassing definition of quality that includes considerations of value and cost and apply it to the entire product realization process; and (3) to explore the role of organization structure and corporate culture on manufacturing effectiveness. Initial results, based upon a market model that incorporates value as well as the more traditional elements of cost and price, show that a single universal metric governs manufacturing effectiveness. The new quality function being developed yields the traditional Taguchi formalism as a limiting case.

This project aims at development of a CAD environment in which a designer can conveniently design, analyze, and generate manufacturing instructions (NC tape) for producing ducts and manifolds. Such a system finds application in the design of aerospace components, where the designer might have to test the design (in a wind tunnel) and modify it based on the results. The software has been tested in the design and manufacture of a complicated engine intake. Currently, the system is being enhanced to allow the design of any sheet metal parts.

During the design of a component, the designer can tighten up his specifications and ensure the proper functioning of the produced component. Considering the exponential relation of production costs and accuracy, this could be an expensive strategy. In this project we develop an approach for the designer to arrive at tolerance levels that minimize the total production costs (cost of processing and cost of failure). Reliability analysis is used to arrive at the optimal permissible variability on the dimensions of components to be mechanically assembled.

In this project, Petri-nets are being used to coordinate the activities in a flexible manufacturing system. Petri-nets are capable of characterizing the concurrency (flexibility) in an FMS, allow analysis to be performed for identifying incongruities, such as deadlocks that might occur during operation, and can be easily reconfigured to reflect physical changes made to the FMS. An initial Petri-net-based controller has been built and tested on a model manufacturing cell. An architecture for a colored Petri-net-based controller has also been developed and software to implement it is being written.

Process planning is the activity that determines the appropriate procedures to transform a raw material into a finished product. Our purpose is to develop a conceptual model for implementation of an automated process planning system for machined parts. The basic categories of activities that must be included are identified, the relationships between activities and the various subtasks within them are analyzed, and the conceptual structure emerging from such detailed analysis and the interaction with other systems are addressed. The development of a software environment to support the construction of process planning systems and an inspection planning system are being developed.

Feature recognition has shown to be a promising tool to fill the gap between the design and manufacturing stages of a product. However, traditional feature recognition systems are based on a generic algorithm. Part one of this project will develop a system that offers a high-level language for the definition of a feature and the specification of the algorithm to be used in its recognition. Part two attempts to couple the reconfigurable feature recognition system with an integrated simulation of different aspects of machining together with a means of checking whether the generated surface actually conforms to tolerance specifications made on it.

The recycling of glass, steel, and plastic containers provides a means of drastically reducing the demand for raw materials in addition to extending landfill life spans. Past automation attempts have focused on bulk property separation techniques and have strived for complete removal of selected material types. In this project, the problem is addressed from a more marketable perspective whereby material removal efficiency is measured relative to decision accuracy. The overall goal of this work is to move machine learning technology from the laboratory into an on-line process control scenario, providing a foundation for intelligent automation.

The goal of this research is to develop a dynamic system for control of material handling systems, such as automated guided vehicles or overhead cranes, to avoid collisions of the oversized loads moved within precast concrete plants. A labeling algorithm has been developed that focuses on finding the shortest time path with multiple time window constraints on tracks and at decision points for the automated guided vehicles. The algorithm is being generalized to control multiple cooperating overhead cranes carrying oversized loads in building construction.

A fractional factorial design methodology is being developed for identifying key cost drivers of a process and for developing empirical manufacturing cost models. Access to manufacturing cost data is particularly important during the early nonlinear and cyclic development of a design. It is during this time that the overall product structure is cast and a large percentage of the cost is effectively committed. The empirical cost models are used to make rapid ballpark cost estimates of both recurring and nonrecurring manufacturing costs during the initial design phases, as an integral part of the iterative design for manufacture (DFM) process.

Front wheel drive cars require constant velocity (CV) joints between the drive axle and each wheel. CV joints are prone to wear and often need to be replaced. In the CV driveline industry, there is no ``standard'' for wear measurement and serviceability evaluation. The first phase of this project has resulted in the design and development of a patented metrology device for quantifying wear pro files. Current research focuses on correlating performance parameters with these wear profiles. The CV joint rebuilder will then be able to measure serviceability against a supplied ``standard.''

John Deere Harvester wishes to develop and implement a central preventive and predictive maintenance system, using recent developments in plant monitoring, fault diagnosis, and information management. The aim of the project is to develop and specify an advanced generic factory-wide automated preventive and predictive maintenance system concept for the company. The need for configurability over a wide range of machines implies the need for a generic solution.

This project researches contemporary tools for competitive product realization in the biomedical product development arena. Key tools and methodologies include quality function deployment, design-to-cost, and computer integrated design for manufacture and assembly. Parametric feature-base solid modeling is used to comprehensively model and analyze, for function and manufacturability, the new product as it evolves. The research explores how functional prototype iterations may be rapidly completed using in-house rapid prototyping methods including stereolithography, RTV molding, and spray-metal molding.

Government sector pollution-prevention initiatives and heightened consumer focus has placed increased pressure on industry, particularly machine tool builders and their customers, to evaluate the ultimate disposability of their products and their manufacturing waste streams. Even more attention is turning to such issues as machine tool energy efficiency, lubricant flows, and process waste. This project addresses three fundamental areas: (1) develop- ment of life cycle analysis techniques and data that support the development of green products and processes, (2) design of environmentally conscious machine tool products, and (3) design of environmentally conscious machining processes.