3D Printer Object Model Packing to Prevent Complex Layer Failures
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Solution Overview
Problem
Additive manufacturing processes face challenges with print job failures due to layers of high complexity that cannot be processed within the limited time available for each layer, leading to reduced print quality or failure.
Innovation Solution
A method to analyze the complexity of each layer in a 3D print job and modify the object model to reduce complexity by rearranging the objects within the build volume, using packing optimization functions and seed values to generate a revised object model that can be processed within the available time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the object model includes layers with high complexity, then the design freedom and object functionality are improved, but the print job reliability deteriorates due to processing time constraints
Solution Approach 1:
The system performs preliminary analysis of layer complexity before the print job is executed. By evaluating the complexity parameter of each layer in advance and identifying layers that exceed the threshold, the system can detect potential print failures before they occur, allowing for preventive measures to be taken.
Solution Approach 2:
The system implements a feedback mechanism where the complexity analysis results are used to generate warnings or errors that prevent execution of problematic print jobs. The complexity parameter calculation provides feedback about the print job feasibility, enabling users to modify the design or processing parameters before actual printing.
2Manufacturing precision
If the processor analyzes complex layers in detail, then the print quality control is improved, but the processing time increases beyond the available time for each layer
Solution Approach 1:
The system changes the parameter being measured from detailed processing time to a complexity parameter that can be calculated quickly. By using geometric properties (number of voxels, bounding box dimensions) as proxies for complexity, the system achieves quality control without requiring time-consuming detailed analysis of each layer's processing requirements.
Solution Approach 2:
The system replaces the mechanical/time-based analysis approach with a mathematical calculation approach. Instead of actually processing each layer to measure the time required, the system uses computational geometry to calculate complexity parameters based on voxel counts and bounding box dimensions, which can be computed much faster.
3Productivity
If the print job is executed without complexity analysis, then the processing speed is maintained, but print failures occur due to unprocessed layer information
Solution Approach 1:
The system performs a partial analysis of each layer by calculating only the essential complexity parameters (voxel count, bounding box dimensions) rather than conducting a complete processing simulation. This partial action provides sufficient information to detect problematic layers without the excessive time investment of full analysis, maintaining processing speed while improving reliability.
Data Source
AI summary
Object model data is obtained, representing one or more objects to be generated by an additive manufacturing system. An object model is generated, representing an arrangement of the one or more objects within a build volume, by determining a solution to a packing optimization function. A layer of the object model is analysed to determine a parameter representing the complexity of the layer, and if the determined parameter exceeds a given threshold, the process is repeated by generating a revised object model representing a different arrangement of the one or more objects within the build volume.