Piston Deposit 3D Visualization for Objective Quantification
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Solution Overview
Problem
Existing methods for evaluating piston deposits in engines are subjective and lack quantifiable results, making it difficult to accurately assess lubricant performance, especially for small pistons like those in motorcycle engines.
Innovation Solution
A method involving 3D scanning and digital microscopy to create a 3D model of pistons, followed by digital image processing to quantify and visualize deposits, providing a precise and objective assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If an expert rating system is used to evaluate piston deposits, then the evaluation process is simple and established, but the results are subjective and lack quantifiable values
Solution Approach 1:
The patent replaces the mechanical/manual expert rating system with an automated optical measurement system. A 3D scanner creates a digital model of the piston, and a microscope with image processing algorithms automatically quantifies deposit characteristics, eliminating subjective human assessment while providing precise numerical data.
Solution Approach 2:
The patent creates a digital 3D copy of the piston surface that can be analyzed repeatedly without physical contact. This digital model allows for precise measurement of deposit thickness and distribution, providing quantifiable data that replaces the subjective expert evaluation while maintaining simplicity through software-based analysis.
2Measurement precision
If laser scanning is used to create a 3D model of a piston, then a visual representation is provided, but the resolution is insufficient for accurate quantification of fine deposits on small pistons
Solution Approach 1:
The patent divides the measurement task into two distinct stages: first, a 3D scanner captures the overall piston geometry and deposit distribution; second, a high-resolution microscope with image processing analyzes specific regions of interest in detail. This segmentation allows each method to operate at its optimal resolution level, achieving accurate quantification of fine deposits on small pistons.
Solution Approach 2:
The patent transitions from purely 3D spatial modeling to 2D high-resolution optical imaging for detailed analysis. By projecting the 3D model to identify regions of interest and then capturing detailed 2D microscope images of those specific areas, the system achieves the resolution needed for fine deposit quantification while managing complexity through targeted analysis.
3Area of stationary object
If the whole external surface is imaged by rotating the machine part, then complete coverage is achieved, but the image processing becomes complex due to overlapping sections
Solution Approach 1:
The patent employs automated image stitching software that automatically detects overlapping regions between consecutive images, aligns them based on feature recognition, and merges them into a single seamless panorama. This self-service approach handles the complexity of image registration and blending, providing complete surface coverage while keeping the user interface simple.
Solution Approach 2:
The patent introduces an intermediate computational processing stage that acts as a mediator between image capture and final analysis. The image stitching algorithm processes overlapping images by identifying common features, calculating transformations, and blending images with appropriate weighting, thereby simplifying the overall workflow while achieving complete surface coverage.
Data Source
AI summary
A method includes for i) creating a 3D model of the machine part after use: ii) mounting the machine part on a means for rotation; iii) obtaining an image of an initial section of an external surface of said machine part: iv) rotating the machine part by a specific amount; v) obtaining an image of a further section of the external surface that overlaps with the initial section; vi) repeating steps iii) to v) until the whole external surface has been imaged; vii) removing the overlapping sections of the images and creating a single continuous image of the external surface; viii) assigning a value to each pixel in the image related to the presence of deposits therein; and ix) applying the dataset obtained in step viii) to the 3D model created in step i) to produce an accurate 3D representation for visualisation and quantification of the deposits on the machine part.