- Technology Readiness Level: Concept Developed
- Technology Validation: The researchers created a machine learning model to determine the printing status for any vat configuration or polymerization process. The researchers trained, validated, and tested their model using the data set collected by printi
Researchers at Purdue University have developed a method of detecting real time failures, defects, and curing area in the vat photopolymerization 3D printing process. Printing failures, defects, and thermal distortion are still challenging all the vat photopolymerization (VPP) technologies, and previous attempts at monitoring the process (separation force sensors, cameras) were unsuccessful. The Purdue researchers’ method utilizes an array of thermistors to monitor and collect in-situ real-time temperature data, without affecting the printing. By using the data collected, this method is able to predict the printing area and shape. This method seeks to overcome feature loss and distortion, which is very common in VPP.
- real-time monitoring
- low cost
- amenable to different types of vat photopolymerization processes
- monitoring of photopolymerization 3D printing
Name: Matthew R Halladay