VP printer monitoring using PVDF-based AE sensor

Abstract

VP(Vat Photopolymerization) technology, an additive manufacturing method, produces high-precision and high-quality surface products, making it essential across various industries. However, due to the manufacturing method of selectively curing resin using a UV light source, ambient light conditions and environmental factors can lead to defects. The degree of curing can cause defects, including settlement failures, shape distortions, and interlayer delamination. This degrades the precision and su rface quality of VP products and reduces productivity, necessitating the development of technology to monitor the light-curing process. To address this, our study aims to detect these defects in real-time using PVDF-based AE(Acoustic Emission) sensors. AE represents elastic waves under 1 MHz, generated as internally accumulated stresses are released when a material undergoes plastic deformation and fracture, propagate through the surface of the material. AE sensors can therefore detect elastic waves, such as separation signals between the output and the bed due to incomplete curing. Unlike conventional AE sensors, which are challenging to attach to the bed due to geometric limitations, the flexible and flat design of PVDF-based AE sensors allows for easy adaptation. Signals from the PVDF-based AE sensor can be analyzed across AE RMS, Energy, and Count parameters to identify the type of defect. In addition, by monitoring the parameters, it is possible to check the progress of the entire process where the transition between layers is made, and the time of occurrence of defects during the process can be checked in real time. As a result, the AE monitoring method used in the study can minimize the influence of equipment noise generated during the process by setting a voltage threshold and by not recording noise below the threshold, thereby enhancing the accuracy of defect detection in VP printing. It can also be used for smart monitoring of VP printers to reduce the incidence of defects and has the potential to improve the productivity of the precision manufacturing industry.