The process of reviewing test data for anomalies after a firing of the Space Shuttle Main Engine (SSME) is a complex, time-consuming task. A project is under way to provide the team of SSME experts with a knowledge-based system to assist in the review and diagnosis task. A model-based approach was chosen because it can be adapted to changes in engine design, is easier to maintain, and can be explained more easily. A complex thermodynamic fluid system like the SSME introduces problems during modeling, analysis, and diagnosis which have as yet been insufficiently studied. We developed a qualitative constraint-based diagnostic system inspired by existing qualitative modeling and constraint-based reasoning methods which addresses these difficulties explicitly. Our approach combines various diagnostic paradigms seamlessly, such as the model-based and heuristic association-based paradigms, in order to better approximate the reasoning process of the domain experts. The end-user interface allows expert users to actively participate in the reasoning process, both by adding their own expertise and by guiding the diagnostic search performed by the system.

Inspection planning is a process of reasoning about inspection activities. As a result, a sequence of inspection actions is formulated, which, when performed, will achieve the desired measurements. In manufacturing, automated inspection technologies, such as Computer-Aided Inspection (CAI) or Co-ordinate Measuring Machines (CMMs), will be facilitated by inspection planning. Inspection planning involves the following four aspects: representation of inspection features; process formalization; modeling of inspection activities; and, finally, plan synthesis. This paper discusses an approach to knowledge-based inspection planning. Accordingly, a prototype inspection planning system has been developed, which is also described in this paper.

In wood engineering design, an important task is the derivation of a construct from site specific data. Human experts perform the task in two phases, first qualitatively and then quantitatively in a hierarchical fashion. COWEN (Computer Wood ENgineer) is a fully implemented research prototype expert system that performs the qualitative phase and makes two contributions to the technology of expert systems. The first contribution is a Knowledge Level specification of the task prior to considering Symbol Level implementation. This is important because expert systems have been defined as mostly symbolic processors in the literature. The second contribution is that this Knowledge Level specification has led to the conclusion that additional qualitative sciences, besides physics and geometry, are needed for an engineering task. This is an interesting discovery because qualitative reasoning research in Artificial Intelligence (AI) has approached engineering design from the viewpoints of physics and geometry only.

Process planning is the function that converts an engineering design into a manufacturing plan. One of the problems in feature-based process planning is the sequencing of features. Features must be given an order for removal. This order, or sequence, is partially dependent on the geometric relationships between the features. If the geometric relationships between features are such that they dictate a particular sequence, the features are said to have an interaction. Identifying these interactions is an important first step in creating the process plan. An approach to solve this problem using constructive solid geometry operations and the Episodal Associative Memory (EAM) is demonstrated. The EAM is an associative memory that integrates dynamic memory organization and neural computing techniques. The geometric feature relationships can be represented by a pattern. This pattern captures very qualitative information about the geometric positions fo the features. The EAM can organize these patterns into groups of similar geometric relationships. A method for dealing with exceptions, and for retrieving and storing general machining problems associated with interacting features will be described. The system implemented is shown to correctly sequence several types of feature interactions.

This paper presents a knowledge-based problem solving environment for numerical simulation of problems described by partial differential equations (PDEs). The system aims to facilitate the simulation requirements of different user groups that include engineers, mathematicians and numerical analysts. To attain this objective, a flexible multi-perspective modelling environment is proposed which incorporates three natural modelling platforms, namely; a physical model, a mathematical model and a numerical model. The modelling environment is integrated with a sophisticated numerical solver. We believe that combination of an open modelling system with a basic numerical simulator provides a powerful problem solving environment capable of meeting the needs of these different user groups. The overall system architecture is based on automatic transformation using mathematical and numerical knowledge bases between the three identified models. The knowledge bases are organized in a frame based manner to reflect the hierarchical nature of the knowledge in PDEs and numerical algorithms. The object oriented paradigm is used to bind local rule bases to each frame and for implementing a global inference mechanism which works over the hierarchical knowledge structures. Evaluation of the modelling environment has indicated that engineers can tackle PDE based engineering problems without the necessity for detailed knowledge of mathematics or numerical techniques and mathematicians can examine the mathematical properties of PDEs without the requirement of numerical expertise.