Digital Fringe Projection Height Error Model
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Solution Overview
Problem
Existing digital fringe projection (DFP) systems for additive manufacturing lack effective methods to quantify and manage measurement uncertainties, particularly due to noise sources like light projector gamma nonlinearity, quantization effects, and pixel intensity noise.
Innovation Solution
A system and method that utilize a processor to capture images of an object's surface, generate pixel intensity data, and determine height error data based on a noise model, allowing for the assessment of measurement uncertainties and decision-making regarding additive manufacturing processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If digital fringe projection is used for surface measurement, then non-contact measurement capability is achieved, but measurement precision deteriorates due to noise sources like gamma nonlinearity, quantization effects, and pixel intensity noise
Solution Approach 1:
The system performs preliminary calibration to determine the height error model before actual measurements. This includes capturing reference images of known surfaces and calculating the relationship between pixel intensity errors and height measurement errors, so that correction can be applied during subsequent measurements
Solution Approach 2:
The system calculates height error data based on the determined height error model and uses this feedback to assess whether measurements satisfy acceptance criteria. The error model provides continuous feedback about measurement reliability, enabling informed decisions about part acceptance or process adjustment
2Productivity
If height measurements are performed without uncertainty quantification, then measurement speed is maintained, but reliability of measurement results deteriorates
Solution Approach 1:
The system automatically determines height error data using the pre-established height error model without requiring manual uncertainty assessment. The model self-evaluates measurement reliability by comparing measured pixel intensities against the calibrated error relationships, enabling rapid automated reliability assessment
Solution Approach 2:
The system transforms the measurement process by adding height error data as a new parameter alongside traditional height measurements. This allows simultaneous output of both measurement value and reliability metric, maintaining productivity while improving reliability assessment capability
3Device complexity
If noise sources like gamma nonlinearity and quantization effects are present, then device complexity is reduced, but manufacturing precision deteriorates
Solution Approach 1:
The system replaces complex hardware correction mechanisms with a software-based height error model. Instead of using additional calibration hardware or complex optical systems to eliminate noise effects, the invention uses computational methods to model and compensate for gamma nonlinearity, quantization effects, and pixel intensity noise
Solution Approach 2:
The height error model acts as an intermediary between the noisy measurement data and the final height measurements. It mediates the effect of noise sources by providing correction factors that translate raw pixel intensities into accurate height values, isolating the measurement system from the harmful effects of device imperfections
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
The proposed solution enables accurate quantification of measurement uncertainties in DFP systems, improving the reliability of height measurements and enabling informed decisions in additive manufacturing, such as rejecting defective parts or adjusting process parameters.
Implementation Method 1
A light source projects structured light, such as a Moiré pattern or a fringe pattern, on to a surface of an object
Implementation Method 2
An image sensor captures an image of the surface of the object and generates, from the captured image, pixel intensity data
Data Source
AI summary
In one aspect, there is provided a system including at least one processor, and at least one memory including program code which when executed by the at least one processor causes operations including capturing an image of at least a portion of a surface of an object; generating, from the captured image, pixel intensity data; in response to generating the pixel intensity data, determining, based on a height error model, height error data, wherein the height error data indicates an uncertainty of at least one height measurement of the object; and determining, based on the height error data, whether the object satisfies a threshold criteria for acceptance of the object. Related system, methods, and articles of manufacture are also disclosed.