Automated Sheet Metal Tool Evaluation via Dye Spotting Analysis
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Solution Overview
Problem
The existing methods for evaluating sheet metal forming tools using dye spotting images are time-consuming and unreliable due to manual analysis, and current technologies fail to accurately detect partial contact areas, leading to non-optimal tool adjustments.
Innovation Solution
A computer-based system and method utilizing optical sensing and advanced image processing techniques, such as machine learning, clustering, and stochastic algorithms, to segment and analyze dye spotting images, enabling precise detection of contact areas and automatic tool evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of dye spotting images is performed by skilled persons, then the analysis can be completed, but the process is time-consuming and possibly less reliable
Solution Approach 1:
The patent replaces manual mechanical analysis by skilled persons with an automated computer-based image processing system. The system captures dye spotting images and automatically processes them through segmentation, feature extraction, and classification algorithms to evaluate contact areas between tool and workpiece, eliminating the need for manual inspection while improving both reliability and efficiency
Solution Approach 2:
The system enables self-service evaluation where the computer-based system autonomously performs the complete analysis workflow including image capture, processing, and tool evaluation without requiring skilled persons. The automated system serves itself by integrating all necessary functions from image acquisition to final evaluation report generation
2Measurement precision
If histograms are used for image processing to detect contact areas, then the full contact can be sensed, but partial contacts are overlooked leading to non-optimal tools
Solution Approach 1:
The patent applies segmentation to divide the dye spotting image into distinct regions representing different contact conditions. The system segments the image to identify full contact areas, partial contact areas, and no contact areas separately, allowing for precise detection of partial contacts that histograms would miss. This segmentation enables differentiated evaluation and adjustment of tool surfaces in various contact zones
Solution Approach 2:
The system implements local quality analysis by evaluating different regions of the tool surface individually based on their specific contact characteristics. Instead of treating the entire contact area uniformly, the system analyzes local contact patterns in segmented regions and provides targeted adjustment recommendations for each area, ensuring optimal tool performance across the entire surface
3Manufacturing precision
If additional processing of the tool surface is performed repeatedly to achieve homogenous contact, then the tool quality improves, but the process becomes time-consuming and expensive
Solution Approach 1:
The patent applies preliminary action by using the automated computer-based system to accurately identify and map all areas requiring tool surface adjustment before actual processing begins. The system performs complete image analysis, segmentation, and generates a comprehensive adjustment plan in advance, allowing manufacturers to prepare the tool systematically with minimal trial-and-error iterations, thereby reducing both time and cost
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 approach significantly reduces the time and cost associated with tool evaluation by providing accurate, automated analysis of contact areas, ensuring homogenous contact and improving the efficiency of the reshaping process.
Implementation Method 1
capturing of the dye spotting image to obtain a digital image
Data Source
Figure 1a~1f
AI summary
The method for evaluation of sheet metal forming tool on the basis of dye spotting images comprises the following steps: - segmentation of the image, wherein each segment comprises all image points, which are similar in their colour and/or location, - determination of brightness level of each segment, wherein the number of classes is 2 or more, - performing a process to obtain at least a 2D grid with at least triangular grid elements for allocation of points that are in the same place below and above the pre-product and/or the tool, - determination of brightness level of each grid element, wherein the grid element belongs to the brightness level into which an average point or triangle of the grid element (modus, average, median) belongs, - performing and optionally repeating previous steps to obtain data for at least two surfaces, - generating a global grid with Boolean operators AND, OR, NOT, AND NOT, UNION and INTERSECT, which are used between grid elements in the same position, wherein the global grid represents surfaces where a direct contact between the surface of the pre-product and the tool has occurred, surfaces with no contact and/or surfaces with partial contact.