Cyber-physical system for thermal stress prevention in 3D printing process

Fused deposition modeling (FDM) has been widely applied in the automotive, aerospace, industrial, and medical fields in recent years. However, residual stress and part deformation caused by thermal effects are still significant issues that limit the development of the technology. Improving the temperature control system has been shown to be an efficient way to reduce thermal effects. In this work, nozzle temperature and platform temperature were first studied to provide experimental data for modifying a control system. Identical parts were printed using different settings and distortion was measured for each part. Then, based on the distortion data, a cyber-model was built to predict deformation based on printing settings. The prediction test results show that a linear regression model outperformed an artificial neural network model and a support vector machine model. Based on the linear regression model, a cyber-physical system (CPS) was built to adjust nozzle temperature settings automatically. A performance evaluation experiment showed that this CPS system can reduce the distortion significantly. In future research, printing speed control and platform temperature control will also be included in the CPS system to further decrease the distortion.