Crimp Cross-Section Image Evaluation for Reliable Quality Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for evaluating the quality of crimp connections in cable production are labor-intensive, non-reproducible, and lead to inconsistent results due to manual handling, with manual documentation complicating traceability and retrievability of evaluation results.
Innovation Solution
An image processing device using a deep neural network for semantic segmentation of crimp connection cross-sectional images, converting raster images to vector contours, and generating output signals for qualitative and quantitative quality parameter determination, enabling robust and automated evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual evaluation methods are used to assess crimp connection quality, then flexibility and adaptability are maintained, but labor intensity increases and measurement precision decreases
Solution Approach 1:
The patent replaces manual mechanical evaluation with an automated image processing system that captures cross-sectional images of crimp connections and uses algorithms to automatically assess quality parameters, eliminating human labor while maintaining or improving measurement precision
Solution Approach 2:
The system enables self-service evaluation where the crimp connection quality is automatically determined through image capture and processing without requiring manual intervention, allowing the system to assess its own output objectively
2Reliability
If manual documentation of evaluation results is implemented, then flexibility in handling diverse cases is maintained, but traceability and retrievability of results deteriorate
Solution Approach 1:
The system creates digital copies of evaluation results in structured formats that are easily stored, traced, and retrieved, replacing manual documentation with automated digital record-keeping that ensures reliability without increasing complexity
3Measurement precision
If classical image evaluation algorithms are used, then device complexity is kept low, but measurement precision and reliability of automated assessment decrease
Solution Approach 1:
The patent replaces simple classical algorithms with advanced deep learning-based image processing systems that provide higher measurement precision, accepting increased complexity as necessary to achieve reliable automated quality assessment
Data Source
AI summary
The disclosure relates to an image processing device for supporting a qualitative and/or quantitative evaluation of the quality of a crimp connection, an image evaluation device, and a manufacturing release system for a crimping device with an image evaluation device according to any of claims 7 to 9, with a data interface to a database in which production order-dependent target values for crimp connections are stored, with a release unit designed to provide a release or a refusal of release for manufacturing the classified crimp connection after a comparison between at least one qualitative and/or quantitative quality parameter and a corresponding target value. This solves the task of making the determination of quality parameters of crimp connections more robust and reliable and making the production of corresponding crimp connections more reliable and less labor-intensive.