Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks

Abstract

The application of Bayesian networks for monitoring and diagnosis of a multistage manufacturing process is described. Bayesian network “part models” were designed to represent individual parts in-process. These were combined to form a “process model,” a Bayesian network model of the entire manufacturing process. An efficient procedure is designed for managing the “process network.” Simulated data is used to test the validity of diagnosis made from this method. In addition, a critical analysis of this method is given, including computation speed concerns, accuracy of results, and ease of implementation. Finally, a discussion on future research in the area is given.

Complex networks in advanced manufacturing systems

“…A system framework and a weighted-coupled network-based dynamic quality control method are proposed to improve the machining errors of one key feature in production process [46][47]. In order to monitoring and estimating the status of quality features, a general approach for quality monitoring and diagnosing in MMPs utilizing Bayesian networks is presented [48]. Furthermore, a quality control fractal network is established for extended enterprises by considering the constraint relationship among nodes [49].…”

“…Finding: In the 12 papers on this topic (8% of the selected papers) [38][39][40][41][42][43][44][45][46][47][48][49], the network models of multistage machining processes are proposed by combining the real network characteristics with complex networks theory. The variation propagation and quality control for multistage machining processes based on a number of measurement indicators are analyzed.…”

Model-Based Engineering for the Integration of Manufacturing Systems with Advanced Analytics

“…In [12], the authors apply analytics to detect faults in the alignment of a cap to the base part of a product. In [13], [14], and [15], the authors predict product quality using three DA algorithms: Bayesian networks (BNs), linear regression, and NNs.…”

A Bayesian network approach to root cause diagnosis of process variations

“…The network is initialized and then used to diagnose a pressure die casting process. Wolbrecht et al [49] describe a real time monitoring and diagnosis system that identifies component failures quickly in a multi-stage process. Prateepasen et al [50] demonstrates the ability to combine signals from acoustic emission and vibration sensors for tool wear monitoring.…”