Inductive Learning in Design: Case Study on Antifriction Bearing Systems

1. Inductive Learning in Design:A Method and Case Study Concerning Design of Antifriction Bearing Systems Machine Learning and Data Mining : Methods and Applications 1999년 6월 19일 토요일 99406-810 산업공학과 허 원 창

2. Contents • Introduction • Exemplary problem • Testing and Training events • Exemplary rule set obtained • Empirical errors of learned rule set • Degree of Confidence • Conclusion

3. 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

4. Example Problem • Design of Bearing arrangement • Design Process

6. 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

7. Domains of Attributes • Domains of Attributes

8. Exemplary Training Events • training events of the class ‘deep groove ball bearing’

9. Exemplary rules # of unique events that support rule total # of events that support rule • exemplary rule concerning ‘deep groove ball bearing’

10. Empirical Error of learned rule sets • overall empirical error rate • Empirical omission error rate • Empirical comission error rate

11. Testing Results • Testing results using ‘leave-one-out’ method

12. Evaluation of Training Example • Evaluation of training example

13. Exemplary Degree of Confidence • exemplary Degree of confidence

14. 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