Online Monitoring and Control of Polymer Additive Manufacturing Processes

Abstract

Additive manufacturing (AM) is a revolutionary technology that fabricates parts layerwise and provides many advantages. This article discusses polymer AM processes such as material extrusion, vat photopolymerization (VPP), powder-bed fusion (PBF), binder jetting (BJ), material jetting (MJ), and sheet lamination (SL). It presents the benefits of online monitoring and process control for polymer AM. It also introduces the respective monitoring devices used, including the models and algorithms designed for polymer AM online monitoring and control.

FFF print defect characterization through in-situ electrical resistance monitoring

“…Various review articles give an overview of existing in-situ monitoring methods, e.g. thermal, optical, acoustic, vibration and force/pressure monitoring, as well as the current challenges 4,6,9,10 . These reviews show that thermal and optical methods, the ones most commonly used, have already been successfully demonstrated for defect detection and closed-loop control purposes.…”

A Systematic Method for Assessing the Machine Performance of Material Extrusion Printers

“…The main advantage of this method is its lower cost since it needs only height measurements performed using a laser triangulation sensor, while other methods require many more sensors [56]. However, even if vision tools for AM are advancing, many approaches still rely on lower-level process parameters [57]. They monitor the process variables, detect their anomalies, and compensate for their deviations during the part's construction using a closed-loop controller, such as the common proportional-integral derivative (PID) [57,58].…”

“…However, even if vision tools for AM are advancing, many approaches still rely on lower-level process parameters [57]. They monitor the process variables, detect their anomalies, and compensate for their deviations during the part's construction using a closed-loop controller, such as the common proportional-integral derivative (PID) [57,58]. However, especially in polymer AM, the defects' root causes may be very complex to establish since many factors influence them [57].…”

“…They monitor the process variables, detect their anomalies, and compensate for their deviations during the part's construction using a closed-loop controller, such as the common proportional-integral derivative (PID) [57,58]. However, especially in polymer AM, the defects' root causes may be very complex to establish since many factors influence them [57]. New and emerging decision-making tools such as artificial intelligence (AI) may be promising solutions to better dimension and tune the controllers and adequately compensate for the process deviations [57].…”

“…However, especially in polymer AM, the defects' root causes may be very complex to establish since many factors influence them [57]. New and emerging decision-making tools such as artificial intelligence (AI) may be promising solutions to better dimension and tune the controllers and adequately compensate for the process deviations [57]. This is still a hot topic that will require extended research in the coming years.…”