Near-Infrared Spectral Inspection for Blister Packaging
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Solution Overview
Problem
Existing methods for inspecting objects within Press Through Package (PTP) sheets using spectral analysis face challenges due to variations in spectral data caused by concave and convex structures, mixing state of active ingredients, and specular reflections, leading to inconsistent and inaccurate identification of objects.
Innovation Solution
An inspection device that irradiates objects with near-infrared light, disperses and images the reflected light, selects a dense spectral data group based on highest luminance values for analysis, excluding singular points and normalizing data to enhance accuracy, and performs principal component analysis for precise object identification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If spectral data is averaged at multiple points on the object, then the inspection process is simplified, but the measurement precision deteriorates due to variations caused by surface structures and reflections
Solution Approach 1:
The patent segments the spectral data by dividing the object surface into multiple coordinate points and analyzing spectral characteristics at each point separately. This allows identification and exclusion of abnormal spectral data points caused by surface variations, while maintaining a systematic inspection process.
Solution Approach 2:
The patent applies local quality analysis by evaluating spectral data at specific coordinate points on the object surface. By examining local spectral characteristics and comparing them against reference data, the system can identify points affected by surface structures or reflections and exclude them from the final analysis, thereby improving overall measurement precision.
2Loss of information
If spectral data from all coordinate points is used for analysis, then data completeness is improved, but reliability deteriorates due to inclusion of abnormal spectral data from surface variations
Solution Approach 1:
The patent performs preliminary filtering of spectral data by comparing each coordinate point's spectral data against reference spectral data before final analysis. This preliminary action identifies and excludes abnormal data points caused by surface variations, ensuring that only reliable spectral data is used for the final inspection decision.
Solution Approach 2:
The patent implements a feedback mechanism where spectral data from multiple coordinate points is compared against reference data, and the results are used to determine which points should be excluded. This feedback loop ensures that abnormal spectral data is systematically identified and removed, improving the reliability of the final inspection results while maintaining data completeness.
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 configuration significantly improves the accuracy of object identification by focusing on dense spectral data groups, reducing the impact of surface variations and reflections, resulting in more reliable inspection results compared to averaging spectral data across multiple points.
Implementation Method 1
an irradiation unit configured to irradiate an object with near-infrared light; a spectral unit configured to disperse reflected light that is reflected from the object irradiated with the near-infrared light
Data Source
AI summary
An inspection device includes: an illumination device that irradiates an object with near-infrared light; a spectroscope that disperses reflected light from the object irradiated with the near-infrared light; an imaging device that takes a spectroscopic image of the reflected light dispersed by the spectroscope; and a processor. The processor obtains spectral data at a plurality of points on the object based on the spectroscopic image obtained by the imaging device, selects, from among the spectral data at the plurality of points, a group having a highest density of luminance values of a predetermined wavelength component as a dense spectral data group, and performs a predetermined analysis for the object based on the dense spectral data group and detects a different type of object.