A holistic method of complex product development based on a neural network-aided technological evolution system

Abstract Product complexity increases along with the increase of product functions. Effective methods are required in the complex product development. Axiomatic design can effectively reduce product complexity, but there are deficiencies in the usage process and the innovation stimulation. This paper proposes a holistic method for the development of complex products by using a neural network-aided technological evolution process to control negative effects of complexity. For the first time, engineering parameters are used as the intermediary between the complexity and technological evolution. Problematic engineering parameters are extracted from a current reality tree model through a natural language process. An artificial neural network is trained to predict the technological evolution law based on existing successful samples. Appropriate analogical objects are formed in an invention principle case library through the similarity analysis of engineering parameters. The optimal scheme is developed objectively by using the coefficient variation method and Dempster combination rule. The proposed method is applied to develop a pipe cutting machine with a granted patent for its feasibility and effectiveness.