Nowadays it is commonly accepted that knowledge, as a resource has become increasingly important in the drive toward knowledge-driven innovation in the manufacturing industry, and which is especially critical for the research and development of complex equipment. In China, there are several fields of manufacturing, such as aviation, ship, and electronics. Although each of them has its own professional technology, they also take advantage of the basic common manufacturing technology. Thus, it is meant for sharing manufacturing technology knowledge. In order to promote innovation, a manufacturing technology knowledge retrieval system is developed to make more efficient use of manufacturing technology knowledge. Manufacturing technology knowledge is a phrase with vast meanings, which may include knowledge of different manufacturing process means, machine, and process capabilities or the latest technology developments. In this system, the manufacturing technology knowledge is defined and organized. A knowledge retrieval method based on the weighted fusion of ontology-based semantic extension (OSE) and user-based collaborative filtering (UCF) is proposed and described detailed. On this basis, the prototype system of manufacturing technology knowledge retrieval system is developed. Its architecture and main functions are elaborated. The results of this system facilitate the sharing of manufacturing knowledge, and it is expected to accelerate the process of knowledge transfer, to improve the level of the overall manufacturing technology. INDEX TERMS Manufacturing technology knowledge, ontology-based semantic extension, user-based collaborative filtering, knowledge sharing.
With the development of intelligent manufacturing, the key strategic of complex equipment is becoming more and more obvious. How to realize the assembly of complex products has become the focus of intelligent manufacturing. This paper puts forward the improved Taguchi method to dimension chains measures, by using different quality loss function to different dimension chains, the cores are the Nominal-is-best, non-core is measured with the improved Smaller-is-better to improve convergence perusal and increase matching rate; General adopt Smaller-is-better to enhance assembly accuracy, reduce interference fit and assembly cost. Then the dimension chains quantitative model of complicated product assembly by using the signal-to-noise ratio and different weights is built up. The model contains modeling assumption, the objective function and the matching model. And this model is regard as the fitness function of genetic algorithm. Finally, the feasibility and efficiency of the scheme are verified by the case study.
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