IPA--concepts and applications in engineering by Jerzy Pokojski

By Jerzy Pokojski

In specialist perform, many designers gather and preserve own notes as guidance approximately reviews and insights for dealing with technical difficulties and layout events. An clever own assistant (IPA) can act as a database for those notes, making the total layout method extra effective. in accordance with genuine commercial approaches, this booklet comprises useful examples for execs and scholars attracted to genuine implementations of data dependent structures in engineering. It integrates significant principles: a working laptop or computer method integrating desktop layout instruments and a working laptop or computer method satisfying the function of an clever own assistant. This hassle-free method of the most principles, techniques and methods exhibits how an IPA can function an important and fruitful wisdom dependent process in engineering design.

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Different real-life examples are considered. For instance: a book contains information and when a human reads this book he gains knowledge [17]. Different people gain different knowledge. Thus we conclude that this process cannot be directly steered nor controlled externally. , to knowledge which is not uniform). Therefore knowledge is more than only recorded information. In knowledge methodologies we often see the classification as formal information and informal information [17–19, 36, 57]. Formal information [36] is information through which all individuals infer the same knowledge.

It is possible to build message handlers for each class. Each message handler has to take a specific role. Message handlers receive messages and act appropriately on them. A plurality of message handlers connected with object functioning can be created. We determine when particular message handlers have to act. We can use an object representation for structuring the product. In this case we build for each geometrical primitive a corresponding object-oriented model. Our model can be developed in various directions.

The generator of the fluid flow dynamics model is a knowledge-based system. When the geometry of the piping system is given first, the user can later add some physical data and then get the pressure distribution in the piping system. The knowledge-based system was filled with the knowledge of how to transform a geometrical model and its physical data into a fluid flow dynamics model. Thereby about 60 rules which supported the model transformation were created. Additionally the geometrical model required physical data like pressure, flow or temperature.

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