3D Displacement Maps for Print Deformation Compensation
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Solution Overview
Problem
3D manufacturing processes, such as 3D printing, often result in geometrical deformations due to thermal diffusion, thermal change, gravity, and manufacturing errors, leading to uneven heating and cooling, which cause shrinkages and expansions in the printed objects, affecting their geometry.
Innovation Solution
A machine learning model is used to predict object deformation by aligning a 3D object model with a scanned model, calculating distance between surfaces, and generating a compensated point cloud to create a displacement map that encodes geometrical adjustments, which is then mapped to a 2D space and interpolated to produce a displacement map for 3D manufacturing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If 3D manufacturing processes are used to produce objects, then productivity is improved through rapid prototyping and short-run manufacturing, but manufacturing precision deteriorates due to geometrical deformations from thermal diffusion and uneven heating/cooling
Solution Approach 1:
The system performs preliminary deformation prediction using machine learning models before manufacturing, calculates expected geometrical deformations based on thermal diffusion patterns, and pre-compensates the 3D model by adjusting voxel positions in reverse of the predicted deformation direction, thereby ensuring final geometrical accuracy
Solution Approach 2:
The system uses feedback from scanned models of previously manufactured objects to train and improve machine learning models that predict deformation patterns, continuously refining the prediction accuracy by comparing predicted versus actual deformations and updating the models accordingly
2Ease of manufacture
If thermal energy is projected to fuse material in 3D printing, then manufacturing capability is improved through selective voxel fusion, but manufacturing precision deteriorates due to thermal diffusion causing unpredictable deformations
Solution Approach 1:
The system introduces an intermediary computational layer between the 3D model and manufacturing process, using machine learning models to predict thermal diffusion effects and calculate compensation transformations, thereby mediating between the desired geometry and the actual thermal fusion process outcomes
Solution Approach 2:
The system changes parameters of the 3D model by adjusting voxel positions and dimensions based on predicted thermal deformation patterns, modifying the model geometry to compensate for expected expansions and shrinkages during the thermal fusion process
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The displacement map reduces and minimizes geometrical deformations in printed objects by compensating for predicted deformations during manufacturing, resulting in improved object geometry.
Implementation Method 1
geometrical deformations due to thermal diffusion, thermal change, gravity, and manufacturing errors
Implementation Method 2
uneven heating and cooling, which cause shrinkages and expansions in the printed objects
Implementation Method 3
geometrical deformations due to thermal diffusion, thermal change, gravity, and manufacturing errors
Data Source
AI summary
Examples of methods for determining displacement maps are described herein. In some examples of the methods, a method includes determining a displacement map for a three-dimensional (3D) object model based on a compensated point cloud. In some examples, the method includes assembling the displacement map on the 3D object model for 3D manufacturing.