Build Part Orientation Simulation for Surface Quality Prediction
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Solution Overview
Problem
Current additive manufacturing techniques lack consideration for surface and sub-surface quality during build part positioning, leading to inefficient post-processing requirements, increased costs, and potential scrap due to inadequate surface quality and dimensional accuracy.
Innovation Solution
An additive manufacturing system that determines geometrical characteristics of build part segments and generates quality scores to optimize positioning, using simulation models with graphic indicators to display predicted surface quality, allowing for adjustments before manufacturing to improve surface and sub-surface quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If build part positioning is determined without considering surface quality factors, then manufacturing setup is simple and quick, but surface quality and dimensional accuracy deteriorate requiring costly post-processing
Solution Approach 1:
The system performs preliminary analysis of surface quality factors before the actual additive manufacturing process by evaluating multiple candidate positions and generating simulation models. This advance assessment allows selection of optimal build part positioning that ensures high surface quality from the start, eliminating the need for post-processing operations.
Solution Approach 2:
The system creates virtual simulation models that copy and represent the expected surface quality outcomes for different build part positions. These digital twins allow evaluation of surface quality characteristics without physical manufacturing, enabling informed positioning decisions before actual production.
2Manufacturing precision
If extensive post-processing is performed to improve surface quality, then surface smoothness improves, but manufacturing time and costs increase
Solution Approach 1:
The system applies preliminary anti-action by predicting and preventing poor surface quality outcomes before they occur. By analyzing geometric characteristics and generating quality scores for different positions, the system selects positioning that inherently produces high surface quality, counteracting the need for subsequent corrective post-processing operations.
3Manufacturing precision
If build part positioning is optimized for surface quality, then quality scores improve and post-processing reduces, but positioning determination becomes more complex
Solution Approach 1:
The system segments the build part into multiple regions and evaluates geometric characteristics for each segment at different candidate positions. By dividing the complex assessment into manageable segment-level analyses, the system can comprehensively evaluate surface quality factors without overwhelming computational complexity.
Solution Approach 2:
The system applies local quality assessment by generating quality scores for specific segments and regions of the build part rather than treating the entire part uniformly. This localized evaluation allows precise identification of positioning factors that affect critical surfaces while simplifying assessment of less critical areas.
Data Source
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AI summary
An additive manufacturing system (100) includes one or more processors (118) configured to determine one or more geometrical characteristics of each of multiple segments (312, 332) of a build part (302) at a candidate position (304) of the build part relative to a platform (102). The one or more processors are configured to generate a quality score for each of the segments at the candidate position based on the one or more geometrical characteristics, The one or more processors are also configured to generate a simulation model (400) of the build part at the candidate position for display. The simulation model includes graphic indicators (402) corresponding to each of the segments. The graphic indicators are representative of the quality scores of the corresponding segments.