inductive learning in design: a method and case study concerning design of antifriction bearing...
TRANSCRIPT
Inductive Learning in Design:A Method and Case Study Concerning Design of A
ntifriction Bearing Systems
Machine Learning and Data Mining : Methods and Applications
1999 년 6 월 19 일 토요일99406-810 산업공학과
허 원 창
Contents
Introduction Exemplary problem Testing and Training events Exemplary rule set obtained Empirical errors of learned rule set Degree of Confidence Conclusion
Introduction
A Method for Learning Design Rule– in design process - design knowledge is important but
ambiguous, and there are many solutions in design problem
– in applying Inductive Learning Method - recognizing design knowledge and representing it in the for of rule is important
– in this chapter - learning rules for selecting anti-friction bearing systems
Global Steps– defines attributes used for characterizing design examples
– describe design examples with selected attributes– determining training and testing examples– learning through AQ15c and obtaining rule set
Training and Testing Events
Design Knowledge Source– catalogues of rolling bearing, text books on machine design,
special publications issued by producers of bearing.....– Conversions of quantitative data to qualitative data
Database Examples– bearing types : deep grove ball bearing, angular contact ball
bearing, self-aligning ball bearing, cylindrical roller bearings..– 10-26 events for each bearings– 101088 possible events– need more events from design experts
Exemplary rules
exemplary rule concerning ‘deep groove ball bearing’
# of unique events that support rule
total # of events that support rule
Empirical Error of learned rule sets
overall empirical error rate
Empirical omission error rate
Empirical comission error rate
eventstestingofnumber
errorsofnumberEov
n
k
kom
komom kclassforexamplespositiveofnumber
kclassforerrorsomissionofnumberEE
nE
1
,1
n
k
kcm
kcmcm kclassforexamplesnegativeofnumber
kclassforerrorscomissionofnumberEE
nE
1
,1
Conclusion
In problems of deriving useful design knowledge in order to aid designer in routine design task– The feasibility of the application of machine learning in case
of selecting the type of bearing.– can suggests several solution to designers.– The ruleset obtained features high degree of accuracy.– Further verification of results require cooperation with skilled
designers