[mod.ai] Seminar - Expert Systems in Manufacturing

ashutosh%euler.Berkeley.EDU@UCBVAX.BERKELEY.EDU.UUCP (02/03/87)

			CS 298 Seminar 

	Expert Systems for Diagnostic and Control in Manufacturing

			Prof. Alice M. Agogino 

		Dept. of Mechanical Engineering, UC Berkeley
			
		   608-7 Evans, Tuesday Feb.3, 5 - 6 pm.


Abtract : An architecture for the hierarchical integration of sensors and
diagnostic reasoning in expert systems for automated manufacturing and 
process control is described. The system architecture uses influence diagrams
to provide a symbolic representation of the knowledge obtained from experts
with varying degrees of technical proficiency and from diverse domains of
expertise. The symbolic representation also maps to a functional level of
knowledge which can be used by the knowledge acquistion system to obtain a 
more detailed numerical level of information from experts , maintenance
records, statistical data bases or sensor signals. The diagnostic 
implementation uses probailistic inference to answer questions concerning
possible failures in an automated manufacturing or process system based on
observable sensor readings. A search through the influence diagram network
provides the topological solution or calculation sequence to answer any
such diagnostic query. Once the topological and numerical solution to the
influence diagram has been determined, qualitative and quantitative advice
can be relayed to the controller , operator or diagnostician. A description
of an implementation of such an architecture will  be provided.

SECAM@SIERRA.STANFORD.EDU.UUCP (02/26/87)

Jane Frederick					Friday 27 February
G.E. Industrial Automation Systems		Terman 556
						1:30-3:00pm

	"Expert Systems in Electronic Manufacturing"

Manufacturing appears to be one of the fertile fields for expert system
applications.  The tasks are bounded and repetitive in nature.  There
exists a set of experts which regularly perform the tasks.  These tasks
can be defined in process steps and last but not least, manufacturing is a
direct pay point.  The payback for quality and productivity improvements
can be specifically determined.  This last issue is very important and
often overlooked, but expert systems development and implementation is an
expensive and ongoing process.  Therefore, one of the challenges for expert
systems in manufacturing is selecting the correct application and the one
with the greatest payback.

Refreshments will be served.
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