Integrated Product and Process Design of Microwave Modules Using AI Planning and Integer Programming Dana Nau, Michael Ball, John Baras, Abdur Chowdhury, Edward Lin, Jeff Meyer, Ravi Rajamani, John Splain, and Vinai Trichur Dept. of Computer Science and Institute for Systems Research, University of Maryland R. H. Smith School of Business and Institute for Systems Research, University of Maryland Dept. of Electrical & Computer Engr. and Institute for Systems Research, Univ. of Maryland IITRI Institute for Systems Research, University of Maryland GTE/BBN Technologies RWD Technologies Mitretek Systems i2 Technologies Key words: Integrated Product and Process Design (IPPD), Design for Manufacturability (DFM), Artificial Intelligence (AI), Integer Programming (IP), Microwave Modules (MWMs) Abstract: A This paper describes the process planning techniques we developed for use in an Integrated Product and Process Design (IPPD) tool for the design and manufacture of microwave transmit/receive modules. Given a collection of data about the design of a microwave module, the IPPD tool uses a combination of AI planning and optimization-based tradeoff analysis to produce a collection of alternative designs and alternative process plans that have Pareto optimal values for manufacturing and purchasing lead time, process yield, cost, and number of suppliers. The IPPD tool provides facilities to enable the user to generate and examine these Pareto optimal alternatives in real time, in order to provide immediate feedback on how to modify the design to improve its cost and productivity. 1. INTRODUCTION This paper describes an Integrated Product and Process Design (IPPD) tool for the design and manufacture of microwave transmit/receive modules. Microwave modules are complex electronic devices that operate in the 120 GHz range. The IPPD tool was developed as part of a contract with Northrop Grumman Corporations Electronic Sensors and Systems Division (ESSD) division in Baltimore. We designed it to combine high performance, ease of understandability by manufacturing personnel, ease of maintenance, and integration with other systems. The IPPD tool uses a combination of Artificial Intelligence (AI) planning and integer programming (IP) optimization techniques to produce a collection of design alternatives. Each alternative is a collection of design elements (the electronic and mechanical parts to be used in the design) and process-plan elements (the manufacturing processes needed for the parts used in the design). The system considers the following