SLA (Stereolithography) printing technology can achieve high precision and accuracy compared to other 3D printing methods. However, the single laser movement mechanism has the disadvantage of increasing processing time during the printing process and causing problems such as incomplete curing and premature gelation. In this study, we address these challenges by using Acoustic Emission (AE)-based detection of curing defects during the printing process in SLA, which utilizes UV-curable resin as its primary material. PVDF (Polyvinylidene Fluoride) sensors are used to detect AE during resin curing. Signals were analyzed based on the presence or absence of specific events to avoid signal ambiguity caused by internal voids during resin curing. Several AE parameters were evaluated experimentally. The most suitable parameters were identified for the detection of thermal cure signals. Among these parameters, AE RMS and AE Count showed the most significant variations in response to thermal curing signals. The proposed method can be used for smart monitoring in SLA printing to detect defects, such as incomplete curing, early in the process, contributing to precision manufacturing.
Selective laser melting (SLM) is a combined process of melting and stacking three-dimensional products by fusing micro-metal powder using a laser. It has the advantage of manufacturing parts with complex structures with reduced production time. However, in the case of aluminum, the disadvantages of poor laser formability due to its high thermal conductivity, diffusivity, and reflectivity result in process defects such as bowling, pore, and poor surface quality. This study aims to develop a surface defect removal methodology during aluminum melting by laser processing and to enhance process automation capabilities by introducing a sensor monitoring scheme. In the laser experiments, aluminum specimens (AL-7075) with mechanical scratches were used and the level of surface defect removal during processing was classified depending on surface conditions. In addition, a PVDF-type acoustic emission (AE) sensor monitoring system was implemented to collect characteristic signals during the laser polishing of the machined surface(scratch). It was shown that the degree of surface defects removal and surface state could be effectively classified through a convolution neural network (CNN) utilizing the collected signals as input vectors.
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