Cyber-physical system for thermal stress prevention in 3D printing process
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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.
Keywords3D printing FDM Machine learning Cyber-physical systems
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Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Texas A&M University, Rockwell Automation, or Taiwan’s Ministry of Education (MOE).
This material was partially supported by a Texas A&M University-CONACYT Collaborative Research Grant (No. 230308), by a gift from Rockwell Automation, and by the Additive Manufacturing Center for Mass Customization Production, which is part of the Featured Areas Research Center Program within the framework of Taiwan’s Ministry of Education (MOE) Higher Education Sprout Project.
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