This paper describes multi view modeling and data model transformation for the modeling. We have proposed a reference model of CAD system generation, which can be applied to various domain specific languages. Howeve...This paper describes multi view modeling and data model transformation for the modeling. We have proposed a reference model of CAD system generation, which can be applied to various domain specific languages. However, the current CAD system generation cannot integrate data of multiple domains. Generally each domain has its own view of products. For example, in the domain of architectural structure, designers extract the necessary data from the data in architecture design. Domain experts translate one view into another view beyond domains using their own brains.The multi view modeling is a way to integrate product data of multiple domains, and make it possible to translate views among various domains by computers.展开更多
In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ...In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.展开更多
文摘This paper describes multi view modeling and data model transformation for the modeling. We have proposed a reference model of CAD system generation, which can be applied to various domain specific languages. However, the current CAD system generation cannot integrate data of multiple domains. Generally each domain has its own view of products. For example, in the domain of architectural structure, designers extract the necessary data from the data in architecture design. Domain experts translate one view into another view beyond domains using their own brains.The multi view modeling is a way to integrate product data of multiple domains, and make it possible to translate views among various domains by computers.
文摘In order to compete in the global manufacturing mar ke t, agility is the only possible solution to response to the fragmented market se gments and frequently changed customer requirements. However, manufacturing agil ity can only be attained through the deployment of knowledge. To embed knowledge into a CAD system to form a knowledge intensive CAD (KIC) system is one of way to enhance the design compatibility of a manufacturing company. The most difficu lt phase to develop a KIC system is to capitalize a huge amount of legacy data t o form a knowledge database. In the past, such capitalization process could only be done solely manually or semi-automatic. In this paper, a five step model fo r automatic design knowledge capitalization through the use of data mining is pr oposed whilst details of how to select, verify and performance benchmarking an a ppropriate data mining algorithm for a specific design task will also be discuss ed. A case study concerning the design of a plastic toaster casing was used as an illustration for the proposed methodology and it was found that the avera ge absolute error of the predictions for the most appropriate algorithm is withi n 17%.
