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

Contents 4 Introduction 4 Exemplary problem 4 Testing and Training events 4 Exemplary rule set obtained 4 Empirical errors of learned rule set 4 Degree of Confidence 4 Conclusion

Introduction 4 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 4 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

Example Problem 4 Design of Bearing arrangement 4 Design Process

Training and Testing Events 4 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 4 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 – possible events –need more events from design experts

Domains of Attributes 4 Domains of Attributes

Exemplary Training Events 4 training events of the class ‘deep groove ball bearing’

Exemplary rules 4 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 4 overall empirical error rate 4 Empirical omission error rate 4 Empirical comission error rate

Testing Results 4 Testing results using ‘leave-one-out’ method

Evaluation of Training Example 4 Evaluation of training example

Exemplary Degree of Confidence 4 exemplary Degree of confidence

Conclusion 4 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