Condition-based Predictive Maintenance as an Efficient Strategy for Industrializing Additive Manufacturing Technology

Abstract

Objective: Traditional manufacturing has several issues to determine a general maintenance strategy. Additive Manufacturing (AM) is a special type of fabrication, which possesses different issues, especially uncertainty failure cases. This forms a big obstacle to becoming an industrialized production strategy. The objective of this work is to provide the most suitable maintenance strategy in order to reduce the likelihood of failure that can help in industrializing the AM technology. Methods: Among the different maintenance strategies, Predictive Maintenance (PdM) became widely treated in academia and industry. According to the methods used for detecting the failure signs, it can be classified into two types: Condition-Based Predictive Maintenance (CBPdM) and Statistical-Based Predictive Maintenance (SBPdM). The use of the last type highly depends on available and accessible data. However, CBPdM depends on only periodic or continuous condition monitoring tools for detecting the failure signs. Results: The existence of various types of failure cases yields to a big difficulty to predict failures. In addition, because of insufficient data, SBPdM cannot be selected as a suitable maintenance strategy for additive manufacturing. According to the presented examples, the CBPdM can be considered here as the best maintenance strategy for AM. Conclusion: CBPdM strategy is the best compromise between cost and applicability where there is not enough data. This selected maintenance approach represents an efficient tool in industrializing AM technology.