This Special Issue was motivated in part by the success of the 2001 American Association for Artificial Intelligence's Special Interest Group on Manufacturing (SIGMAN) workshop. SIGMAN has organized a series of these workshops that highlight significant advances in academic research and industrial practice. New manufacturing concepts such as agile/lean manufacturing and virtual manufacturing place increasing emphasis on the need for more intelligent manufacturing systems. It is no longer a question of whether artificial intelligence (AI) technologies will have an impact on manufacturing but one of better understanding and exploiting the broad potential of AI in this domain.

In this paper we are interested in the decision problem faced by an agent when requesting bids for collections of tasks with complex time constraints and interdependencies. In particular, we study the problem of specifying an appropriate schedule for the tasks in the request for bids. We expect bids to require resource commitments, so we expect different settings of time windows to solicit different bids and different costs. The agent is interested in soliciting “desirable” bids, where desirable means bids that can be feasibly combined in a low-cost combination that covers the entire collection of tasks. Since the request for bids has to be issued before the agent can see any bids, in this decision process there is a probability of loss as well as a probability of gain. This requires the decision process to deal with the risk posture of the person or organization on whose behalf the agent is acting. We describe a model based on Expected Utility Theory and show how an agent can attempt to maximize its profits while managing its financial risk exposure. We illustrate the operation and properties of the model and discuss what assumptions are required for its successful integration in multiagent contracting applications.

The design of sheet metal components is perhaps one of the more challenging concurrent activities for design and manufacturing engineers. To aid this design process, a method is developed to encapsulate the constraints of sheet metal that make designing such components a tedious and iterative procedure. This project involves the implementation and testing of a geometric representation scheme for building feasible sheet metal components through the use of 17 grammar rules that capture manufacturing operations like cutting and bending. The implemented system has benefits both as a user interaction tool and as the basis for a computational design synthesis approach for designing sheet metal components. An example of a constructed sheet metal component is shown along with the method for invoking the sheet metal grammar to create this component.

Four bar mechanisms are basic components of many important mechanical devices. The kinematic synthesis of four bar mechanisms is a difficult design problem. A novel method that combines the genetic programming and decision tree learning methods is presented. We give a structural description for the class of mechanisms that produce desired coupler curves. Constructive induction is used to find and characterize feasible regions of the design space. Decision trees constitute the learning engine, and the new features are created by genetic programming.

This paper presents a novel approach, which is based on integrated (automatic/interactive) knowledge acquisition, to rapidly develop knowledge-based systems. Linguistic rules compatible with heuristic expert knowledge are used to construct the knowledge base. A fuzzy inference mechanism is used to query the knowledge base for problem solving. Compared with the traditional interview-based knowledge acquisition, our approach is more flexible and requires a shorter development cycle. The traditional approach requires several rounds of interviews (both structured and unstructured). However, our method involves an optional initial interview, followed by data collection, automatic rule generation, and an optional final interview/rule verification process. The effectiveness of our approach is demonstrated through a benchmark case study and a real-life manufacturing application.

Case-based reasoning (CBR) is a promising methodology for solving many complex engineering design problems. CBR employs past problem-solving experiences when solving new problems. This paper presents a case study of how to apply CBR to a specific engineering problem: mechanical bearing design. A system is developed that retrieves previous design cases from a case repository and uses adaptation techniques to modify them to satisfy the current problem requirements. The approach combines both parametric and constraint satisfaction adaptations. Parametric adaptation considers not only parameter substitution but also the interrelationships between the problem definition and its solution. Constraint satisfaction provides a method to globally check the design requirements to assess case adaptability. Currently, our system has been implemented and tested in the domain of rolling bearings. This work serves as a template for application of CBR techniques to realistic engineering problems.

Collaborative development of engineered products in a business to business (B2B) environment requires more than the selection of components from an on-line catalogue. It involves the electronic exchange of product, process, and production engineering information during both design and manufacturing. Although the state of the practice does include a variety of ways to exchange product data electronically, it does not extend to the exchange of manufacturing process data. The reason is simple: process data are usually tied to specific manufacturing resources. These resources are not known typically at product development time. This paper proposes an approach called integrated product and process data, in which manufacturing process data are considered during product development. This approach replaces traditional process plans, which are resource specific, with a resource-independent process representation. Such a representation will allow a much wider collaboration among business partners and provide the necessary base for collaborative product development.