Knowledge Recommendation Method for Concept Development of Manufacturing Technology Using Morphological Similarity

Abstract

Concept development is the first and most knowledge-intensive step in the development process of manufacturing technology. Its core is to find a solid scientific foundation for the manufacturing requirements in order to propose a feasible manufacturing technology concept. However, the lack of formal methods and finiteness of personal knowledge result in high randomness and low efficiency of this step. This paper presents a formal design knowledge recommendation method for manufacturing technology concept development by calculating the morphological similarity between manufacturing requirements and multi-domain effect knowledge. In this method, the morphological matrix of general manufacturing technology is constructed first as a template. Then, manufacturing requirements and multi-domain effect knowledge are both expressed as matrices based on this template. Finally, through quantitatively calculating the normalized weighted Euclidean distance between manufacturing requirements and multi-domain effect knowledge, suitable effects of knowledge which are from different domains and have the domain-highest similarity are recommended as the initial design foundation for the concept of new manufacturing technology (NMT). A software system has been developed and a concept development case of composite machining technology was provided to validate this method. The result shows that the proposed approach can reduce the randomness and increase the efficiency of manufacturing technology concept development.