3D Printed Part Quality Detection Across Successive Layers
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Solution Overview
Problem
Existing methods for quality assessment in layer-wise additive manufacturing, such as DMLS, often lead to erroneous categorization of defects or complicate the quality determination process, resulting in inaccurate assessments of manufactured parts.
Innovation Solution
A method and device that determine a quality indicator by analyzing the relative frequency of process irregularities across layers, assigning grade indicator values based on these frequencies, and generating a quality indicator that considers the cumulative impact of irregularities on the object's quality, using a process monitoring device to monitor the solidification process and provide data for precise quality assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If process irregularity detection is performed for each position in every layer, then measurement precision of quality assessment is improved, but device complexity and difficulty of detecting and measuring increase
Solution Approach 1:
The quality assessment is segmented into hierarchical levels: individual position quality indicators are calculated first, then aggregated to layer quality indicators, and finally combined to generate overall object quality indicators. This segmentation allows comprehensive monitoring without requiring complex simultaneous analysis of all positions.
Solution Approach 2:
The patent introduces a temporal dimension by analyzing process irregularities across multiple successive layers, not just within single layers. This multi-layer temporal analysis provides deeper quality insights while distributing the computational complexity across time-based processing steps.
2Measurement precision
If cumulative occurrence of process irregularities across multiple layers is analyzed, then measurement precision of quality assessment is improved, but loss of time in data processing increases
Solution Approach 1:
Quality indicators for individual positions and layers are calculated and stored during the manufacturing process itself, before final quality assessment is needed. This preliminary calculation of grade indicator values eliminates the need for time-consuming retrospective analysis of raw process data.
Solution Approach 2:
The patent creates simplified representative datasets (second datasets) that contain aggregated quality information from multiple layers. These copied and condensed datasets enable rapid quality assessment without processing the complete raw process monitoring data from all layers.
3Manufacturing precision
If relative frequency of process irregularities is calculated for multiple successive layers, then manufacturing precision assessment is improved, but device complexity increases
Solution Approach 1:
The patent merges quality information from multiple successive layers by calculating relative frequencies of process irregularities across layers and combining individual position quality indicators into layer quality indicators. This merging approach assesses manufacturing precision through aggregation rather than complex multi-variable analysis.
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
This approach provides more precise quality assessment by considering the relative frequency and spatial distribution of process irregularities, reducing the likelihood of misclassifying parts as defective and allowing for more accurate monitoring of critical positions, thereby improving the reliability of quality evaluation in additive manufacturing.
Implementation Method 1
process radiation emitted from the molten material, meaning the melt pool, is optically detected
Implementation Method 2
a metal powder is melted layer by layer by means of a laser
Implementation Method 3
the object is manufactured layer by layer by a solidification of a building material at the positions corresponding to the cross-section of the object in a layer
Data Source
AI summary
Disclosed is a method of determining a quality indicator of an object that has been manufactured by layer-wise additive manufacturing. The method includes providing a first dataset that is assigned to a process monitoring device, detecting a relative frequency of occurrence of a process irregularity in a layer and of assigning a grade indicator value to the solidified object cross-section in a layer according to the detected relative frequency, generating a second dataset, in which a grade indicator value is assigned to the object cross-section in each of said several layers following upon one another, and determining a quality indicator by using the second dataset (or several further datasets).