Neural Network Depth Reconstruction for Additive Fabrication
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Solution Overview
Problem
Current additive fabrication systems using Optical Coherence Tomography (OCT) for volumetric data capture face inefficiencies in processing raw data to produce accurate surface or depth maps, requiring multiple intermediate steps that can be complex and computationally intensive.
Innovation Solution
The integration of a machine learning model represented as a neural network to directly process raw OCT data, replacing traditional methods like peak detection and Fourier transforms, to generate estimated depth data, allowing for automatic training across different scanning processes and materials, thus simplifying the processing pipeline and enhancing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional intermediate processing steps (peak detection, Fourier transforms) are used to convert raw OCT data to depth maps, then the processing pipeline is well-established and interpretable, but the computational complexity increases and processing efficiency decreases
Solution Approach 1:
The patent combines multiple intermediate processing steps (peak detection, Fourier transforms, depth calculation) into a single integrated neural network model. The network processes raw OCT data end-to-end to directly generate depth maps, eliminating the need for separate processing stages and reducing overall computational complexity.
Solution Approach 2:
The patent replaces traditional signal processing algorithms (mechanical/computational methods) with a machine learning-based neural network system. This substitution allows the system to learn optimal processing pathways from data, achieving higher efficiency while maintaining accuracy in depth reconstruction.
2Measurement precision
If traditional processing methods are used, then the method is well-understood and easier to implement, but the depth map accuracy and resolution are limited
Solution Approach 1:
The patent transforms the processing approach by changing from fixed algorithmic parameters to learned parameters through neural network training. The network learns optimal depth calculation parameters from training data, enabling higher accuracy in depth reconstruction while adapting to different scanning conditions and materials.
Solution Approach 2:
The patent uses a neural network to learn and copy the underlying patterns and relationships between raw OCT data and depth maps from training examples. By copying these learned patterns, the system achieves high accuracy in depth reconstruction without requiring explicit programming of complex processing rules.
3Manufacturing precision
If multiple intermediate processing steps are employed, then each step can be optimized independently, but the overall processing time increases and computational resources are consumed
Solution Approach 1:
The patent performs preliminary training of the neural network offline using labeled training data. This preliminary action allows the network to learn optimal processing pathways in advance, so that during actual depth map generation, the system can produce accurate results rapidly without performing multiple intermediate processing steps in real-time.
Solution Approach 2:
The patent merges multiple sequential processing operations into a single parallel neural network computation. By combining peak detection, frequency analysis, and depth calculation into one integrated network, the system reduces processing time while maintaining or improving depth reconstruction accuracy through parallel computation capabilities.
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 results in more accurate, higher-resolution depth data with reduced computational complexity, enabling improved precision in additive manufacturing and part inspection applications.
Implementation Method 1
Certain additive fabrication systems use Optical Coherence Tomography (OCT) to capture volumetric data related to an object under fabrication
Data Source
AI summary
A method for determining estimated depth data for an object includes scanning the object to produce scan data corresponding to a surface region of the object using a first scanning process, configuring an artificial neural network with first configuration data corresponding to the first scanning process, and providing the scan data as an input to the configured artificial neural network to yield the estimated depth data as an output, the estimated depth data representing a location of a part of the object in the surface region.