XRF Material Sample Classification for Real-Time Quality Control
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Solution Overview
Problem
Existing quality control systems for material samples require lengthy sample transport times, delayed analysis due to limited laboratory access or trained personnel, and extensive user training, leading to inefficiencies in sample analysis.
Innovation Solution
A computer-implemented system using XRF spectra data and machine learning techniques to classify material samples into acceptable and unacceptable subsets, with a predictive model determining sample quality and allowing real-time analysis and visualization on a user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If samples are shipped to a distant laboratory for analysis, then analysis can be performed in a controlled environment by trained personnel, but transport time and delays increase significantly
Solution Approach 1:
The patent creates a digital copy of the sample data through XRF spectra analysis performed at the site, eliminating the need to physically transport the actual sample to a distant laboratory. The spectral data is transmitted and analyzed remotely, maintaining analysis quality while eliminating transport time delays.
Solution Approach 2:
The patent replaces the mechanical sample transport system with a digital data transmission system. Instead of physically shipping samples through complex logistics, the system transmits XRF spectral data electronically, significantly reducing time loss while maintaining analytical reliability.
2Reliability
If traditional quality control systems are used, then analysis can be performed by specially trained personnel, but user training requirements and system complexity increase
Solution Approach 1:
The system performs automated quality control through machine learning algorithms that self-evaluate sample data without requiring manual intervention from trained personnel. The automated classification system independently determines sample quality, eliminating the need for complex user training while maintaining high analysis accuracy.
Solution Approach 2:
The patent implements automated feedback mechanisms where the machine learning model continuously evaluates XRF spectral data against established criteria and provides immediate quality assessments. This automated feedback loop eliminates the need for manual review by trained personnel, reducing system complexity while maintaining reliability.
3Measurement precision
If detailed analysis and reporting are performed manually, then analysis thoroughness is maintained, but processing speed and productivity decrease
Solution Approach 1:
The patent replaces manual analysis processes with automated machine learning algorithms that process XRF spectral data at high speed. The computational system performs detailed quality control assessments automatically, maintaining thoroughness while dramatically increasing processing speed and productivity.
Solution Approach 2:
The system transforms the analysis process from manual parameter evaluation to automated computational processing. By changing the fundamental approach from human-based to machine-based analysis, the system maintains measurement precision while achieving high-speed processing that manual methods cannot achieve.
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, real-time quality control of material samples by reducing transport delays and user training requirements, improving analysis speed and accuracy through automated classification and visualization.
Implementation Method 1
The sample data may be x-ray fluorescence (XRF) spectra data associated with the plurality of material samples
Data Source
AI summary
Provided herein are methods and systems for improved material sample analysis and quality control. A computing device may receive sample data associated with a plurality of material samples. The computing device may determine a first subset of the plurality of material samples and a second subset of the plurality of material samples. The computing device may determine the first subset based on a plurality of reference values and a plurality of analysis thresholds. The first subset may include samples associated with acceptable XRF spectra. The second subset may include samples associated with unacceptable XRF spectra. The computing device may generate and manipulate charts, graphs, or other visual displays of the data underlying the first subset and/or the second subset.