Powder Bed Surface Roughness Mapping for In-Process Defect Repair
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Solution Overview
Problem
Existing additive manufacturing techniques cannot accurately determine defect positions during the process, making it impossible to efficiently repair defects in real-time.
Innovation Solution
An information processing apparatus and method that acquires roughness data of the manufacturing surface after melting, divides it into small regions, and compares the data with a threshold to determine defect presence, allowing for precise defect detection and repair during the manufacturing process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If defect detection is performed from the state of molten pool or entire manufacturing surface, then defect detection capability is provided, but defect position determination during manufacturing is impossible
Solution Approach 1:
The manufacturing surface is divided into multiple small regions, allowing defect detection at a granular level. This segmentation enables both defect detection and precise location identification, as each small region can be independently analyzed for defect presence while maintaining spatial context.
Solution Approach 2:
The invention transitions from analyzing the entire manufacturing surface as a single two-dimensional area to examining multiple small regions across different spatial dimensions. This dimensional approach allows simultaneous defect detection and position determination by analyzing roughness characteristics in each discrete small region.
2Reliability
If entire manufacturing surface is analyzed for defect detection, then comprehensive defect coverage is achieved, but repair efficiency is reduced due to inability to locate defects precisely
Solution Approach 1:
By segmenting the manufacturing surface into small regions, the system maintains comprehensive defect detection coverage while enabling precise localization. This allows repair operations to be targeted at specific small regions containing defects, significantly improving repair efficiency compared to analyzing the entire surface without location information.
Solution Approach 2:
The invention applies local quality analysis by examining roughness characteristics in each small region independently. This localized approach ensures that defect detection coverage is maintained across the entire manufacturing surface while enabling precise identification of defect locations for efficient repair.
3Manufacturing precision
If roughness measurement is performed on the entire manufacturing surface, then overall surface quality is assessed, but defect position identification during manufacturing is not possible
Solution Approach 1:
The manufacturing surface is divided into multiple small regions for roughness measurement. This segmentation enables both overall surface quality assessment (by analyzing all small regions collectively) and precise defect position identification (by locating which specific small regions exhibit defect characteristics).
Solution Approach 2:
The invention transforms the approach from measuring the entire manufacturing surface as a single entity to measuring multiple small regions across spatial dimensions. This dimensional breakdown preserves overall quality assessment capability while adding defect position information through spatial localization of roughness anomalies.
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 efficient defect repair by identifying defect positions during additive manufacturing, improving the quality and yield of the final product by detecting and addressing defects embedded under the surface or exposed.
Implementation Method 1
acquiring roughness data indicating a roughness of a manufacturing surface after melting
Data Source
AI summary
An information processing apparatus for controlling additive manufacturing of a powder bed method includes an acquirer that acquires roughness data indicating a roughness of a manufacturing surface after melting, and defect determiner that divides the manufacturing surface into small regions each having a predetermined size, and compares the roughness data with a predetermined threshold for each small region, thereby determining whether a defect exists in the small region. If an unmolten region is included in the small region, the defect determiner replaces data of the manufacturing surface in the unmolten region using data of the manufacturing surface in the small region, and determines whether a defect exists in the small region including the unmolten region. Also, the manufacturing defect detection method further includes a defect repair instructor that instructs remelting of a region that is determined by the defect determiner to have a defect.