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 February 2024 was 29%. Grant share is based on the ratio of number of grants to total number of patents.
Quality prediction for 3d printed objects using printing data
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A recently granted patent (Publication Number: US11853033B1) discloses a method and system for determining the quality of a three-dimensional (3D) object. 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 parameters during or after printing, processing these measured parameters against predicted parameters to determine the quality, 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 method steps, including collecting measured parameters and outputting the quality measure.
The method and system outlined in the patent aim to enhance the quality control process for 3D printing by utilizing a combination of printing parameters, predicted states, and measured parameters to determine the quality of the printed object. By analyzing factors such as voltage, current, temperature, and stress during the printing process, the system can provide insights into the strength, durability, electrical conductivity, and thermal conductivity of the 3D object. Additionally, the system can predict properties such as tensile strength, yield strength, and resistance to creep based on the collected data. Overall, this innovation offers a comprehensive approach to assessing and improving the quality of 3D printed objects, potentially leading to more reliable and efficient manufacturing processes in the future.
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