Fiber Composite QA Verification Using Cross-Sensor Correlation
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Solution Overview
Problem
Current automated quality assurance systems for detecting quality-related material properties of deposited fiber materials in fiber composite components are inefficient due to reliance on manual inspection, which is time-consuming and dependent on human experience, leading to significant system downtime and potential manufacturing errors.
Innovation Solution
An automated method involving a machine-learned correlation between measurement data features from a primary quality assurance system and a secondary detection device, using a fiber material sample with known properties to verify the accuracy and functionality of the quality assurance system, allowing for the determination of quality-related material properties through a machine-learning approach.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inspection is used to detect quality-related material properties of deposited fiber materials, then detection accuracy can be maintained, but system productivity decreases significantly due to time-consuming inspection processes
Solution Approach 1:
The patent replaces manual mechanical inspection with an automated optical measurement system that captures images of the fiber material surface and uses machine learning to detect quality defects. This substitution eliminates the time-consuming manual inspection process while maintaining or improving detection accuracy through automated image analysis.
Solution Approach 2:
The system enables self-service quality inspection by automatically analyzing fiber material defects without human intervention. The machine learning model processes images and identifies quality issues autonomously, allowing the production system to continue operating at full speed without stopping for manual inspection.
2Reliability
If manual inspection is used for quality verification, then detection capability can be maintained, but system downtime increases significantly
Solution Approach 1:
The automated optical inspection system operates continuously alongside the fiber deposition process, capturing and analyzing images in real-time without interrupting production. This eliminates the need to stop the system for manual inspection, maintaining continuous useful action and preventing system downtime.
Solution Approach 2:
The system performs quality inspection concurrently with the deposition process rather than as a subsequent step. By detecting defects during or immediately after deposition, the system eliminates the need for separate post-deposition inspection time, thereby preventing system downtime.
3Productivity
If automated fiber deposition is used to improve productivity, then production speed increases, but the need for quality verification creates bottlenecks
Solution Approach 1:
The optical measurement system serves multiple functions: it captures images for quality inspection, provides real-time feedback for process control, and generates data for process optimization. This multi-functionality integrates quality verification into the existing automated deposition system without adding separate verification equipment or complex procedures.
Solution Approach 2:
The system implements real-time feedback by analyzing images during deposition and providing immediate information about quality defects. This allows for instantaneous process adjustments, eliminating the need for complex post-inspection procedures and maintaining high production speed while ensuring quality.
Data Source
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AI summary
The invention relates to a method for verifying an automated quality assurance system for detecting at least one quality-related material property of deposited fiber materials of a fiber composite material for the production of a fiber composite component, wherein the quality assurance system to be verified detects measurement data of a fiber material surface of the deposited fiber material by means of at least one sensor of a first recording sensor system, determines a plurality of measurement data characteristics from the measurement data by means of a detection unit and can determine the at least one quality-related material property from the measurement data characteristics by means of an evaluation unit, characterized by providing a fiber material sample with a known quality-related material property, performing a first series of measurements with the quality assurance system to be verified on a provided fiber material sample,To obtain a plurality of measurement data characteristics, perform a second series of measurements on the provided fiber material sample using a different acquisition device than the quality assurance system to be verified, in order to obtain a plurality of additional measurement data, provide a machine-learned correlation between measurement data characteristics according to the quality assurance system to be verified and measurement data according to the acquisition device using a verification unit, and verify the quality assurance system to be verified depending on the provided correlation of the first series of measurements and the determined measurement data.