AM Part Topology Correction Using Under- and Over-Deposition Analysis
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Solution Overview
Problem
Additive manufacturing (AM) technologies face challenges in characterizing and correcting topological deviations between as-designed and as-manufactured parts, which can affect the structural integrity and performance of complex geometric and topological structures, such as lattices and foams, due to limitations in machine and process parameters.
Innovation Solution
The method involves computing set differences between as-designed and as-manufactured models to identify under-deposition (UD) and over-deposition (OD) features, using topological data analysis to quantify topological persistence and deviations, and adjusting manufacturing parameters to reduce these discrepancies, thereby improving the structural integrity of AM parts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If additive manufacturing is used to create complex geometric and topological structures, then manufacturing capability and design flexibility are improved, but topological deviations between as-designed and as-manufactured parts occur due to machine and process parameter limitations
Solution Approach 1:
The system performs preliminary topological analysis on the as-designed model before manufacturing to identify features that are sensitive to manufacturing deviations. By computing set differences and analyzing topological persistence in advance, the system prepares correction strategies that compensate for expected manufacturing limitations, thereby maintaining topological accuracy despite machine parameter constraints.
Solution Approach 2:
The system establishes a feedback loop where topological deviations between as-designed and as-manufactured models are quantified using persistent homology. This feedback information is then used to iteratively adjust manufacturing parameters or design features, enabling continuous improvement of topological accuracy while maintaining the ability to manufacture complex structures.
2Measurement precision
If topological data analysis is used to quantify deviations, then measurement precision is improved, but computational complexity increases
Solution Approach 1:
The system segments the topological analysis into distinct computational stages: computing set differences between models, identifying UD and OD features, calculating persistent homology invariants, and quantifying deviations. This segmentation allows each computational task to be optimized independently and enables parallel processing, reducing overall computational complexity while maintaining measurement precision.
Solution Approach 2:
The system introduces persistent homology invariants as intermediary mathematical constructs that bridge the gap between complex geometric models and simplified deviation metrics. These invariants serve as computationally efficient representatives of topological features, enabling accurate deviation quantification without requiring full geometric processing of complex models.
3Manufacturing precision
If manufacturing parameters are adjusted to reduce topological differences, then manufacturing precision is improved, but process complexity increases
Solution Approach 1:
The system applies local quality correction by identifying specific under-deposited and over-deposited features through set difference analysis. Instead of uniformly adjusting all manufacturing parameters, the system targets only the local regions and features that exhibit topological deviations, applying corrected parameters selectively. This approach improves topological conformity while minimizing the overall complexity of process parameter management.
Data Source
AI summary
Set differences between an as-designed and an as-manufactured model are computed. Discrepancies between the as-designed model and the as-manufactured model are determined based under-deposition and over-deposition features of the set differences. Based on the discrepancies, an input to a manufacturing instrument is changed to reduce topological differences between the as-manufactured model and the as-designed model.