Crankshaft Counterweight Measurement Using Dual-Threshold Point Cloud Filtering
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Solution Overview
Problem
Conventional crankshaft shape inspection methods struggle with accurately calculating the side dimensions and longitudinal positions of counterweights due to noise caused by the metallic luster on the surface of crankshafts after shot blasting, leading to degradation in measurement accuracy.
Innovation Solution
A method involving the acquisition of three-dimensional point cloud data, followed by isolated point removal processing to generate first and second point cloud data, which are then used to extract counterweight point cloud data and calculate side dimensions and longitudinal positions, utilizing optical three-dimensional shape measuring devices and an arithmetic unit to perform these operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If three-dimensional shape measuring device measures surface shape of crankshaft, then three-dimensional point cloud data is acquired, but measurement accuracy deteriorates due to noise caused by metallic luster on surface
Solution Approach 1:
The point cloud data is segmented into multiple groups based on spatial distribution and characteristics. By dividing the data into different segments, the method can process and analyze specific regions (such as counterweight areas) separately, reducing the impact of noise from metallic luster on overall measurement accuracy
Solution Approach 2:
The invention extracts and removes isolated noise points from the point cloud data through statistical analysis and distance-based filtering. By identifying and eliminating these outlier points caused by metallic luster reflection, the measurement accuracy is restored without affecting the valid surface data
2Measurement precision
If isolated point removal processing is performed to remove noise, then measurement accuracy is improved, but data processing complexity increases
Solution Approach 1:
The method changes processing parameters dynamically based on local point density and spatial distribution characteristics. By adjusting filtering thresholds and processing intensity according to different regions of the crankshaft surface, the system achieves effective noise removal while minimizing unnecessary processing complexity
Solution Approach 2:
The point cloud data itself provides the information needed for noise identification through its own spatial distribution patterns and statistical properties. The method uses inherent data characteristics (distance between points, density variations) to automatically distinguish noise from valid surface data, reducing the need for external complex processing
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 enables accurate calculation of side dimensions and longitudinal positions of counterweights, improving measurement accuracy and overcoming the noise issues associated with the metallic luster on crankshafts.
Implementation Method 1
a three-dimensional shape measuring device measuring a surface shape of the crankshaft
Data Source
AI summary
A crankshaft shape inspection method includes: acquisition step that acquires three-dimensional point cloud data of a surface of a crankshaft S; generation step that generates, while using the three-dimensional point cloud data, first point cloud data based on point cloud data generated by performing isolated point removal processing that removes data points whose distance to the nearest neighbor data point is equal to or more than first threshold value Th1 and generates second point cloud data based on point cloud data generated by performing isolated point removal processing that removes data points whose distance to the nearest neighbor data point is equal to or more than second threshold value Th2 (>the first threshold value Th1); and a calculation step that calculates a side dimension of a counterweight SC using the first point cloud data and calculates a longitudinal position of the counterweight SC using the second point cloud data.