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

International journal of online engineering

Abdelkabir Bacha¹ 

[1] Electricity and Mechanics National High School (ENSEM), Hassan II University - Casablanca -Morocco

关键词: fault diagnosis;    3d printing;    additive manufacturing;    Bayesian Networks;    data acquisition;    sensor;   

DOI  :  

学科分类：社会科学、人文和艺术（综合）

来源: International Association of Online Engineering

【 摘 要 】

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.

【 授权许可】

Unknown