Relativity Space‘s patent involves systems and methods for predicting material properties and quality measures of 3D printed objects using printing process data. The method includes obtaining printing parameters, printing the object using resistive or Joule printing, measuring parameters during printing, and analyzing to determine quality. GlobalData’s report on Relativity Space gives a 360-degree view of the company including its patenting strategy. Buy the report here.
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According to GlobalData’s company profile on Relativity Space, Smart factory systems was a key innovation area identified from patents. Relativity Space's grant share as of January 2024 was 39%. Grant share is based on the ratio of number of grants to total number of patents.
Quality prediction for 3d printed objects using printing process data
A recently granted patent (Publication Number: US11853033B1) discloses a method and system for determining the quality of a three-dimensional (3D) object during the printing process. The method involves obtaining a set of printing parameters and a predicted state of the 3D object, printing the object based on these parameters, measuring specific parameters during or after printing, processing these measured parameters against predicted parameters to determine the quality of the object, and electronically outputting the quality measure. The system includes computer memory to store parameters, predicted states, and measured parameters, along with processors to carry out the steps of obtaining, printing, processing, and outputting the quality measure.
The patent claims cover various aspects of the method and system, including the specific printing parameters such as force, temperature, voltage, and current, as well as the measured parameters like voltage, current, resistance, and temperature. The quality measure can encompass strength, durability, electrical and thermal conductivity, warp, or dimensional instability. The system also includes sensors like force meters, thermometers, and X-ray units to collect measured parameters. Additionally, the processors can predict properties of the 3D object based on the measured and predicted parameters, such as tensile strength, yield strength, elongation at failure, resistance to creep, or fatigue properties. Overall, the patent aims to enhance the quality control process in 3D printing by utilizing predictive modeling and real-time monitoring to ensure the production of high-quality objects.
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