A symbolic calculus for reasoning about process plans is proposed in this paper. The main focus of attention is the selection and sequencing of material removal operations for components in accordance with the design geometry. This is a central issue in automated process planning. The proposed symbolic calculus defines a computational formalism for symbolic manipulation of feature volumes, so that reasoning about volumetric removals can be treated in a logical manner by using well-defined procedures of algorithmic synthesis. This potentially encourages a more generic approach to the automation of outline and detailed process planning. The underlying philosophy is that a properly interpreted object topology upon the feature model allows the logical synthesis of volumetric removal sequences. The number of sequences is constrained by algorithms within the planning system that consider part geometry as expressed by features. This reduces the problem space associated with plan synthesis. Some of the geometrically viable sequences have the potential for further development to form viable machining removal sequences, or outline process plans.

A method is proposed for creative innovative design that is in concordance with the act of knowledge integration in learning; creative innovative design is defined as a guided (creative) process for arriving at an artifact that is socially valuable (practical and needed) and original (innovative). Within the context of models of reasoning, the process of design is interpreted and analyzed with a goal of extracting the stages at which it can be consciously improved by mindful control. A language is proposed for team-oriented intra- or interdisciplinary collaborative, as well as individual design that facilitates communication, mutual understanding, and makes explicit alternative nonverbal, nonquantitative thinking processes that otherwise may remain latent. The role of motivation in innovation is briefly discussed, as well as the role of artifact valuation in a societal context. Although not central to the present discussion, computer models of design are presented as an instance of design practice captured for computer-based design support. A brief discussion highlights the application and implications of the proposed method to education and research.

Numerical simulation of partial differential equations (PDEs) plays a crucial role in predicting the behavior-of physical systems and in modern engineering design. However, to produce reliable results with a PDE simulator, a human expert must typically expend considerable time and effort in setting up the simulation. Most of this effort is spent in generating the grid, the discretization of the spatial domain that the PDE simulator requires as input. To properly design a grid, the gridder must not only consider the characteristics of the spatial domain, but also the physics of the situation and the peculiarities of the numerical simulator. This article describes an intelligent gridder that is capable of analyzing the topology of the spatial domain and of predicting approximate physical behaviors based on the geometry of the spatial domain to automatically generate grids for computational fluid dynamics simulators. Typically, gridding programs are given a partitioning of the spatial domain to assist the gridder. Our gridder is capable of performing this partitioning. This enables the gridder to automatically grid spatial domains with a wide range of configurations.

The ability to reason spatially is an important skill required for engineers, particularly in engineering design and construction. One aspect of spatial reasoning is visualizing and constructing three-dimensional (3D) solid objects from two-dimensional (2D) projections. To assist in teaching this to engineering students, an instructional software system is being developed at the University of Illinois. This instructional software system is comprised of the Visual Sweeper and the Visual Teacher. The Visual Sweeper is a geometric framework for solving missing view problems. In missing view problems, students create 3D solid objects from two 2D projections by applying operations inverse to orthographic projection. The Visual Teacher, which is the focus of this article, is an intelligent critiquing and tutoring module that gives feedback to the student regarding partial solutions to missing view problems. The Visual Teacher is comprised of a Recognizer and a Critiquer. The Recognizer identifies which solution solid the student's partial solution is closest to. Based on the solution solid and a student's partial solution, the Criti-quer gives critique and advice to the student. The Recognizer is based on an algorithm for bipartite graph matching, while the Critiquer uses a rule-based approach. This paper describes the Visual Teacher, gives examples of how it can be used, presents preliminary evaluation results, and discusses the system's assumptions and limitations.