3D-Object Inspection With Parallel Classification and Quality Assessment
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Solution Overview
Problem
In modern manufacturing facilities, efficiently identifying and assessing the quality of semi-finished products with different specifications is crucial for proper redirection and packaging, but existing methods often delay quality assessment until classification, affecting overall production efficiency.
Innovation Solution
A 3D-object identification and quality assessment system that provides simultaneous classification and quality evaluation using a facility with sensing devices, a database of reference feature vectors, and a comparator to generate correspondence indications, enabling parallel identification and quality assessment of 3D objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If quality assessment is performed after classification, then classification accuracy can be maintained, but production efficiency deteriorates due to sequential processing delays
Solution Approach 1:
The system extracts quality assessment features and performs preliminary quality evaluation simultaneously with classification features extraction, rather than waiting until after classification. The quality assessment module processes objects in parallel with the classification module, enabling quality decisions to be made without delaying the classification workflow.
Solution Approach 2:
The patent combines classification and quality assessment into a single integrated processing pipeline. Both functions share common feature extraction components and operate on the same input data simultaneously, merging two previously sequential operations into one parallel process that improves throughput without sacrificing either function's accuracy.
2Productivity
If multiple sensing devices and processing modules are added for parallel processing, then production efficiency improves, but device complexity increases
Solution Approach 1:
The system employs multi-functional modules that can perform both classification and quality assessment tasks. The feature extraction modules and processing units are designed to serve dual purposes, reducing the need for completely separate dedicated hardware for each function and thereby limiting the increase in overall system complexity.
Solution Approach 2:
The system is divided into modular functional blocks (sensing devices, feature extraction modules, classification module, quality assessment module) that can be independently configured and processed. This segmentation allows for scalable implementation where only necessary components are activated based on specific production needs, managing complexity through modularity.
Data Source
AI summary
A 3D-object identification and quality assessment system is described that includes an evaluation module that receives respective correspondence indications for each of object class and performs in a parallel manner: generating a class indication signal indicative for a most probable object class identified for the inspected 3D object; and generating a quality assessment signal indicating a value for an extent to which the inspected 3D object meets the quality requirements for the most probable one of the object classes. A 3D-object identification and quality assessment method, a 3D-object manufacturing system comprising the 3D-object identification and quality assessment system and a method of training a 3D-object identification and quality assessment system are also described.