Additive Manufacturing Control Data for Variable Wall Thickness
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Solution Overview
Problem
Existing drop-based additive manufacturing processes struggle to produce components with variable layer thicknesses, especially those with overhang features, requiring significant effort or compromising quality.
Innovation Solution
A computer-implemented method for generating control data sets that dynamically adjust drop spacing, line spacing, and layer thickness to ensure gap-free and high-quality printing of components with variable wall thickness, using a 'dynamic skin' approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional drop-based additive manufacturing processes are used for components with variable layer thicknesses and overhang features, then manufacturing capability is achieved, but manufacturing precision and quality deteriorate
Solution Approach 1:
The patent applies dynamics by making the drop spacing dynamic rather than constant. The control data set calculates and adjusts drop spacing for each individual drop based on the local geometry and layer thickness requirements, enabling the system to adapt to variable wall thicknesses and overhang features while maintaining high manufacturing precision throughout the component
Solution Approach 2:
The patent implements local quality by calculating specific drop spacing values for each drop position rather than applying a uniform spacing throughout. This allows different regions of the component (e.g., overhang areas vs. vertical walls) to have optimized drop spacing tailored to their specific geometric requirements, thereby maintaining high precision across the entire component
2Adaptability or versatility
If support structures are used to manufacture components with overhang features, then manufacturing capability is improved, but device complexity and manufacturing effort increase
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the drop spacing parameter based on the local geometry and layer thickness. By changing this critical process parameter adaptively, the system can manufacture overhang features and variable thickness components directly without requiring support structures, thereby reducing device complexity and manufacturing effort
3Ease of manufacture
If conventional fixed parameter printing is used, then process simplicity is maintained, but manufacturing precision deteriorates for variable thickness components
Solution Approach 1:
The patent applies preliminary action by pre-calculating the optimal drop spacing for each drop position before the actual printing process. The control data set is generated in advance with all necessary spacing information, allowing the printing process itself to remain simple and straightforward while achieving high precision through the pre-planned parameter optimization
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
Enables high-quality, cost-effective production of components with variable layer thicknesses and overhang features by optimizing drop and line spacing, ensuring accurate deposition and maintaining component integrity.
Implementation Method 1
drop-based additive manufacturing with a drop diameter D
Implementation Method 2
ensuring accurate deposition
Data Source
AI summary
A method for generating a control data set for additive manufacturing components whose wall thickness can vary over the component height using a drop-based method, in which for each layer, 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 rounded up to the next higher natural number and rounded down to the next lower natural number, then data sets with the rounded-up and rounded-down numbers are generated, and these data sets for the rounded-down number and the rounded-up number are compared with predetermined target values depending on the component. One of the data sets is then selected as the control data set based on this comparison.


