Cut Edge Image Analysis for Cutting Parameter Adjustment
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Solution Overview
Problem
Existing cutting processes fail to predict how cutting parameters affect the appearance of a cut edge, necessitating complex test series to address issues like changed material quality or new processes, which is inefficient and resource-intensive.
Innovation Solution
A method using a neural network to analyze a cut edge recording, employing backpropagation to determine the relevance of individual pixels for cutting parameters, enabling targeted adjustment of these parameters to improve the cut edge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If complex test series are conducted to understand the influence of cutting parameters on cut edge appearance, then the understanding of cutting processes improves, but the time consumption and resource requirements increase significantly
Solution Approach 1:
The patent replaces physical trial-and-error testing with an image processing system that uses algorithms to analyze cut edge images and determine the influence of cutting parameters. The system captures images of cut edges, processes them through algorithms to identify characteristic features, and correlates these features with cutting parameters, thereby substituting mechanical test series with computational analysis.
2Ease of operation
If experienced users manually predict the effect of cutting parameters on cut edge appearance, then no additional equipment is needed, but the prediction accuracy remains insufficient
Solution Approach 1:
The patent introduces an image processing system as an intermediary between the cutting process and the operator. The system captures images of cut edges, processes them through algorithms to extract characteristic features, and provides objective feedback about the influence of cutting parameters. This intermediary translates subjective visual assessment into quantifiable data, improving prediction accuracy while maintaining ease of operation.
3Manufacturing precision
If traditional trial-and-error methods are used to optimize cut edge quality, then no advanced analysis tools are required, but the optimization process becomes inefficient and resource-intensive
Solution Approach 1:
The patent implements a feedback mechanism where cut edge images are captured, analyzed by algorithms to determine characteristic features, and correlated with cutting parameters to provide actionable insights. This feedback loop enables operators to understand the specific influence of each cutting parameter on cut edge quality, allowing for targeted adjustments rather than random trial-and-error, thereby improving both precision and productivity.
Data Source
AI summary
A method for recognizing cutting parameters which are particularly important for specific features of a cut edge. A recording of the cut edge is analyzed by an algorithm having a neural network for determining the cutting parameters. Those recording pixels which play a significant part for ascertaining the cutting parameters are identified by backpropagation of this analysis. An output in the form of a representation of these significant recording pixels, in particular in the form of a heat map, demonstrates to a user of the method which cutting parameters need to be changed in order to improve the cut edge. A computer program product and a device for carrying out the method.