No abstract

No abstract

This paper describes a new approach to automate the classification of solid models using machine learning techniques. Existing approaches, based on group technology, fixed matching algorithms or pre-defined feature sets, impose a priori categorization schemes on engineering data or require significant human labeling of design data. This paper describes a shape learning algorithm and a general technique for "teaching" the algorithm to identify new or hidden classifications that are relevant in many engineering applications. In this way, the core shape learning algorithm can be used to find a wide variety of model classifications based on user input and training data. This allows for great flexibility in search and data mining of engineering data.

This paper describes a new approach to automate the classification of solid models using machine learning techniques. Existing approaches, based on group technology, fixed matching algorithms or pre-defined feature sets, impose a priori categorization schemes on engineering data or require significant human labeling of design data. This paper describes a shape learning algorithm and a general technique for "teaching" the algorithm to identify new or hidden classifications that are relevant in many engineering applications. In this way, the core shape learning algorithm can be used to find a wide variety of model classifications based on user input and training data. This allows for great flexibility in search and data mining of engineering data.