PEN: Process Estimator neural Network for root cause analysis using graph convolution

Root cause analysis in modern multistage assembly lines is a challenging, yet widely used technique to increase the product quality. Improvements - due to Industry 4.0 - aim for near-zero-defects manufacturing. Thus, we propose a novel root cause analysis: the Process Estimator neural Network (PEN) to solve the sparse, nonlinear problem of the state-space model empowering a graph convolution neural network. The contributions of this paper are: (1) study a novel problem of utilizing nonlinear deep neural networks to solve the state-space model; (2) elaborating the use of a graph convolution neural network to scope with the current limitations of linear approaches, which cannot process dense 3D point cloud data of the outer skin of the product; (3) how to analyze the trained network for fine tuning. We showed through a realistic experiment how PEN performs on huge 3D point clouds (188.000 points or higher) in form of meshed CAD models of first-order shell elements. These ex-periments set an example on how to overcome the fundamental performance limitations of current linear approaches.

Automated summary

In this paper, a novel root cause analysis method called Process Estimator neural Network (PEN) is proposed to solve the sparse, nonlinear problem of the state-space model. The paper discusses the application and advantages of PEN, and validates its performance in handling large-scale 3D point cloud data through experiments.