Condition-Based Maintenance Model for the Optimization of Smart Manufacturing Processes

The research focused on the elaboration of the Condition-Based Maintenance (CBM) for the optimization of Smart Manufacturing Processes (SMP) using three types of condition monitoring (CM). The paper presents some considerations regarding the Smart Manufacturing Processes (SMP), especially focused on the Additive Manufacturing (AM) process, by Fused Deposition Modelling (FDM) technology and the way in which the CM and mathematical and probabilistic models can be used, for achieving CBM. The experiments focused on testing three different types of CM: vibration (by vibrometers), sound (by sound level meters) and temperature (by thermal camera and infrared thermography). The experiments and results were analysed, data were processed and a probabilistic model was designed, based on statistical methods, using features/key indicators and their thresholds values. Furthermore, a specific model based on Bayesian network was developed, for achieving CBM of the SMP, specifically to the AM/FDM process. This technique and approach can represent a successful integration of a large number of data monitoring sets and complex modelling and analysis capabilities which can lead in the end to an optimisation of the SMP.