Drill Bit Cutter Image Mapping for Wear Grading
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Solution Overview
Problem
Drill bit cutters made of super-hard materials like polycrystalline diamond compact (PDC) wear down during drilling operations, leading to inefficiencies and uncertainties in predicting cutter lifespan and maintenance schedules.
Innovation Solution
A machine learning-based system that analyzes images of the drill bit to identify and grade cutters, determining their condition and mapping context-specific information such as covered and uncovered areas, allowing for more accurate predictions of cutter performance and maintenance needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If machine learning-based image analysis is implemented to identify and grade cutters, then measurement precision and prediction accuracy are improved, but device complexity and implementation cost increase
Solution Approach 1:
The patent uses image capture to create a visual copy of the cutter surface, which is then analyzed by machine learning algorithms. This allows non-contact assessment of cutter condition without physically interacting with the cutters, maintaining measurement precision while avoiding the complexity of physical measurement systems
Solution Approach 2:
The patent replaces traditional mechanical or manual inspection methods with an optical imaging system combined with machine learning analysis. The image processing system substitutes for physical measurement devices, reducing mechanical complexity while improving measurement precision through automated image analysis
2Measurement precision
If detailed surface parameter analysis is performed on cutters, then measurement precision is improved, but loss of time for data processing increases
Solution Approach 1:
The patent performs preliminary actions by capturing images of all cutters simultaneously and pre-processing these images to identify surface parameters. This allows parallel processing of multiple cutters, reducing total data processing time while maintaining detailed measurement precision through comprehensive image analysis
Solution Approach 2:
The patent analyzes specific surface parameters that are most critical for cutter performance assessment, rather than measuring every possible parameter. This selective approach to surface parameter measurement maintains measurement precision for key indicators while reducing overall data processing time by focusing only on the most relevant features
Data Source
AI summary
A method comprising identifying a cutter on a drill bit based on a drill bit image, assigning a grading value of the cutter based on a classification model of a machine learning system, wherein the classification model is generated based on a set of training cutter images associated with drill bit characteristics indicators, determining a surface parameter based on a surface of the cutter, generating a comparison value based on the surface parameter, and mapping a set of cutter information to the cutter on the drill bit, wherein the set of cutter information comprises the grading value and the comparison value.