Droplet Print Path Control for Variable-Thickness 3D Walls
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Solution Overview
Problem
Existing drop-based additive manufacturing processes struggle to efficiently produce components with variable layer thicknesses, especially those with overhangs, often requiring additional support structures or compromising quality.
Innovation Solution
A computer-implemented method for generating control data sets that dynamically adjust droplet spacing, line spacing, and layer thickness to ensure precise droplet placement, allowing for the production of components with variable wall thickness and overhangs without gaps or porosity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional slicers use fixed droplet spacing, line spacing, and layer thickness parameters, then the manufacturing process is simple, but components with variable layer thicknesses and overhangs cannot be produced with high quality
Solution Approach 1:
The patent applies dynamics by making the droplet spacing, line spacing, and layer thickness parameters variable rather than fixed. The control data set generation dynamically adjusts these parameters based on the local geometry of the component, allowing adaptation to different wall thicknesses and overhang conditions at different locations and layers.
Solution Approach 2:
The patent implements local quality by calculating and applying different droplet spacing, line spacing, and layer thickness values for different regions of the component. The method evaluates local geometric conditions (such as overhang angles and wall thickness) and optimizes printing parameters specifically for each local area to achieve high-quality surfaces without support structures.
2Manufacturing precision
If support structures are added to manufacture components with overhangs, then the component geometry can be maintained, but the manufacturing effort and complexity increase significantly
Solution Approach 1:
The patent changes the printing parameters (droplet spacing, line spacing, layer thickness) dynamically based on local geometric conditions. By adjusting these parameters, the method achieves stable droplet placement and surface flatness for overhangs without requiring support structures, thereby maintaining manufacturing precision while improving productivity.
Solution Approach 2:
The method incorporates feedback by evaluating the local geometry (such as overhang angles and wall thickness) and using this information to adjust the printing parameters. This closed-loop approach ensures that the droplet placement is optimized for each local condition, maintaining geometric accuracy without additional support structures.
3Productivity
If droplet spacing and line spacing are increased to reduce printing time, then productivity improves, but manufacturing precision and surface quality deteriorate
Solution Approach 1:
The patent applies local quality by setting different droplet spacing and line spacing values for different regions of the component. In areas requiring high precision (such as regions with small wall thicknesses or complex geometries), the spacing is reduced. In areas where speed is more critical and geometry is simpler, the spacing is increased, thus optimizing the balance between productivity and manufacturing precision locally.
Solution Approach 2:
The method dynamically adjusts droplet spacing and line spacing based on local geometric conditions rather than using fixed values throughout the entire component. This dynamic parameter adjustment allows the system to maintain high printing speed overall while ensuring precision is maintained in critical areas.
Data Source
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AI summary
To enable high-quality, low-effort, drop-based additive manufacturing of components (12) whose wall thickness can vary across the component height, a computer-implemented method for generating a control data set for each layer is proposed. First, a theoretical number xth of paths is calculated from the quotient between the layer width s and a predetermined target line spacing dLo. This theoretical number is then rounded up to the next highest natural number and down to the next lowest natural number. Next, data sets are generated for both the rounded-up and rounded-down numbers. These data sets are then compared with target values predetermined for each component. Finally, one of the data sets is selected as the control data set based on this comparison.