Fault Diagnosis in the Field of Additive Manufacturing (3D Printing) Using Bayesian Networks

Abstract

In this work, a new approach for fault diagnosis in the field of additive manufacturing (3d printing) using artificial intelligence will be given. This approach is based on the marriage of the Bayesian Networks theory and data acquisition techniques. Bayesian Networks are well known for their ability to infer probabilities and to give decisional support under uncertainty. In order to do so, these probability engines must be constructed and maintained by a big amount of data and information using learning algorithms. This work provides a methodology that uses sensors based data acquisition and processing to construct such networks. Some of these sensors are already available in most of the 3d printers available in the market, while other sensors were additionally embedded in a studied 3d printer in order to enrich the number of observational variables to gain a high level of fault diagnosis accuracy and support.