LAUSR

ABSTRACT Accurate prediction of the deviations/deformations is highly necessary for the new product development process. It is a tedious multi-tasking activity, in which various aspects need to be considered in the early phase of design. This paper proposes a new constitutive and collaborative framework to model the functional assembly geometry. For complex industrial applications in the multi-plant scenario, several departments work together for a common goal. Often the department’s goal is different and cannot achieve the feat of right for the first time. Hence this paper aimed to integrate the vital departments of the manufacturing industry. Initially, the functional assembly was predicted through Finite Element Analysis (FEA). A multi-layer perceptron type (MLP) artificial neural network was employed to learn the FEA behavior of the assembly. Further, the assembly prototype was practically tested to validate the FEA results, and the obtained data were used to verify the MLP network model. The best trained and tested network model was simulated to predict the near-net geometry considering the functional behavior of the assembly with external loads. The proposed method provides affirmative knowledge while integrating the finite element analysis, testing methods, and neural networks.