Output from the 1998 Product Development Value Stream Workshop: A Framework for Understanding Information Flow in the Product Development Process

The descriptions below and the attached diagrams are outputs of the 1998 LAI Product Development Focus Team workshop on the Value Chain in Product Development. A working group at that workshop was asked to model the product development process: in terms of the phases of product development and their interfaces, boundaries and outputs. Their work has proven to be generally useful to LAI researchers and industry members, and so is formalized here.

An Algorithm for Parsing Flow Graphs

This report describes research about flow graphs - labeled, directed, acyclic graphs which abstract representations used in a variety of Artificial Intelligence applications. Flow graphs may be derived from flow grammars much as strings may be derived from string grammars; this derivation process forms a useful model for the stepwise refinement processes used in programming and other engineering domains. The central result of this report is a parsing algorithm for flow graphs. Given a flow grammar and a flow graph, the algorithm determines whether the grammar generates the graph and, if so, finds all possible derivations for it. The author has implemented the algorithm in LISP. The intent of this report is to make flow-graph parsing available as an analytic tool for researchers in Artificial Intelligence. The report explores the intuitions behind the parsing algorithm, contains numerous, extensive examples of its behavior, and provides some guidance for those who wish to customize the algorithm to their own uses.

Qualitative Process Theory

Objects move, collide, flow, bend, heat up, cool down, stretch, compress and boil. These and other things that cause changes in objects over time are intuitively characterized as processes. To understand common sense physical reasoning and make programs that interact with the physical world as well as people do we must understand qualitative reasoning about processes, when they will occur, their effects, and when they will stop. Qualitative Process theory defines a simple notion of physical process that appears useful as a language in which to write dynamical theories. Reasoning about processes also motivates a new qualitative representation for quantity in terms of inequalities, called quantity space. This report describes the basic concepts of Qualitative Process theory, several different kinds of reasoning that can be performed with them, and discusses its impact on other issues in common sense reasoning about the physical world, such as causal reasoning and measurement interpretation. Several extended examples illustrate the utility of the theory, including figuring out that a boiler can blow up, that an oscillator with friction will eventually stop, and how to say that you can pull with a string but not push with it. This report also describes GIZMO, an implemented computer program which uses Qualitative Process theory to make predictions and interpret simple measurements. The represnetations and algorithms used in GIZMO are described in detail, and illustrated using several examples.

Process Variability in Micro-Embossing

A promising technique for the large-scale manufacture of micro-fluidic devices and photonic devices is hot embossing of polymers such as PMMA. Micro-embossing is a deformation process where the workpiece material is heated to permit easier material flow and then forced over a planar patterned tool. While there has been considerable, attention paid to process feasibility very little effort has been put into production issues such as process capability and eventual process control. In this paper, we present initial studies aimed at identifying the origins and magnitude of variability for embossing features at the micron scale in PMMA. Test parts with features ranging from 3.5- 630 µm wide and 0.9 µm deep were formed. Measurements at this scale proved very difficult, and only atomic force microscopy was able to provide resolution sufficient to identify process variations. It was found that standard deviations of widths at the 3-4 µm scale were on the order of 0.5 µm leading to a coefficient of variation as high as 13%. Clearly, the transition from test to manufacturing for this process will require understanding the causes of this variation and devising control methods to minimize its magnitude over all types of parts.