Voxel-Based Autozoning for Meltpool-Controlled 3D Printing
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Solution Overview
Problem
Additive manufacturing (3D printing) requires time-consuming and costly processes to determine optimal printing parameters for achieving high-quality 3D printed parts, especially for complex geometries and materials used in industries like aviation and medicine, due to the need for numerous tests and evaluations.
Innovation Solution
The use of in-situ information and a closed-loop control architecture that adjusts process parameters in real-time using sensor data to control meltpool characteristics at the voxel level, enabling faster part development and reducing costs by automating part development and optimizing parameters for desired part quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If traditional trial-and-error methods are used to determine optimal printing parameters, then manufacturing precision can be achieved, but productivity is significantly reduced due to time-consuming tests and evaluations
Solution Approach 1:
The system performs preliminary actions by using sensor data collected during the printing process to predict and determine optimal printing parameters before completing the entire build. This allows the system to proactively adjust parameters for subsequent layers based on real-time monitoring of meltpool characteristics, rather than waiting for post-processing evaluation of completed layers.
Solution Approach 2:
The closed-loop control architecture implements continuous feedback by monitoring meltpool characteristics (temperature, viscosity, flow) using sensors during the printing process. This feedback is used to dynamically adjust printing parameters (laser power, scan speed, hatch spacing) to maintain optimal part quality, replacing the traditional trial-and-error approach with real-time parameter optimization.
2Manufacturing precision
If expert knowledge and manual parameter tuning are used, then manufacturing precision is maintained, but device complexity increases and ease of operation decreases
Solution Approach 1:
The system implements self-service by automatically determining optimal printing parameters using sensor data and closed-loop control algorithms. The system monitors its own printing process in real-time and autonomously adjusts parameters without requiring external expert intervention, thereby simplifying operation while maintaining high part quality.
Solution Approach 2:
The system dynamically changes printing parameters (laser power, scan speed, hatch spacing) based on real-time sensor feedback regarding meltpool characteristics. This automated parameter adjustment replaces manual expert tuning, making the system easier to operate while maintaining manufacturing precision through data-driven parameter optimization.
3Manufacturing precision
If numerous tests and evaluations are conducted to optimize parameters, then manufacturing precision improves, but loss of time and loss of substance increase
Solution Approach 1:
The system maintains continuity of useful action by continuously monitoring meltpool characteristics throughout the printing process using sensors. This continuous data collection and real-time parameter adjustment eliminates the need for discontinuous trial-and-error testing cycles, thereby reducing development time while maintaining part quality through ongoing optimization.
Solution Approach 2:
The system replaces mechanical trial-and-error testing with a sensor-based monitoring and data-driven parameter determination system. By substituting physical testing iterations with real-time sensor feedback and computational analysis of meltpool characteristics, the system significantly reduces the time and material resources required for parameter optimization while maintaining manufacturing precision.
Data Source
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AI summary
Methods and apparatus for sensor-based part development are disclosed. An example apparatus includes at least one memory, instructions in the apparatus, and processor circuitry to execute the instructions to translate at least one user-defined material property selection into a desired process observable, the desired process observable including a meltpool property, perform voxel-based autozoning of an input part geometry, the input part geometry based on a computer-generated design, and output a voxelized reference map for the input part geometry based on the desired process observable and the voxel-based autozoning.