ABSTRACT
The panic buying during Covid-19 caused farmers to amped-up production. However, farm equipment is costly to purchase. Therefore, some farmers utilized Additive Manufacturing (AM) to manufacture farming tools at low cost. However, the lack of in-situ monitoring in AM to stop printing failed parts can waste materials and time. Thus, this research aims to deploy a low-cost smart remote monitoring system using OctoPrint and Node-red to integrate a 3D printer and Teachable Machine and train a model to pre-emptively detect print errors. The result was satisfactory as the 3D printer stopped when the camera detected a defect with 75% accuracy. Furthermore, the user can easily customize the model to enhance the system versatility via the developed code-free platform. © 2021 IEEE.