Coordinate Measuring Machine Testing With AI Signal Assessment
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Solution Overview
Problem
Existing methods for evaluating the functionality of coordinate measuring machines rely heavily on human expertise, which can be subjective and time-consuming, and struggle to accurately assess specific temporal data patterns indicative of machine health.
Innovation Solution
Employing artificial intelligence (AI) to evaluate data sets obtained during functional testing, utilizing training data sets with additional information to derive objective and timely assessments of a coordinate measuring machine's functionality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human expertise is used to evaluate functionality test data, then subjective assessment can be performed, but time consumption increases and objectivity decreases
Solution Approach 1:
The patent replaces the manual evaluation process (human expertise) with an automated evaluation device that uses machine learning models and algorithms to assess functionality test data. This substitution eliminates subjective human judgment and significantly reduces evaluation time while maintaining or improving assessment accuracy through consistent, data-driven analysis.
Solution Approach 2:
The evaluation device performs self-learning through machine learning algorithms that automatically analyze test data patterns and improve assessment capabilities over time. The system serves itself by continuously refining its evaluation models without requiring manual programming or human intervention for each assessment task.
2Reliability
If functionality testing is performed frequently, then machine health can be monitored proactively, but time and resource consumption increase
Solution Approach 1:
The system performs preliminary automated evaluation of functionality test data using pre-trained machine learning models. By having the evaluation device ready with established assessment algorithms, the system can quickly process test data without requiring extensive manual analysis, enabling frequent testing while minimizing time investment.
Solution Approach 2:
Automated evaluation replaces manual assessment, allowing frequent functionality tests to be conducted without proportionally increasing time consumption. The machine learning-based system processes test data rapidly and consistently, enabling proactive monitoring at high frequency.
3Productivity
If automated evaluation is implemented, then time consumption is reduced, but complexity of the evaluation system increases
Solution Approach 1:
The evaluation device is divided into modular functional components: data acquisition modules, machine learning model processing units, analysis algorithms, and output generation systems. This segmentation allows the complex automated evaluation system to be built from manageable, independent modules that can be developed, tested, and maintained separately, reducing overall system complexity while maintaining high productivity.
Data Source
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AI summary
The invention relates to a method for testing the functionality of a coordinate measuring machine (211), comprising the following steps: - operating the coordinate measuring machine (211) and automatically acquiring operating signals generated during operation by at least one first generating unit, which is part of the coordinate measuring machine (211) or is an additional generating unit, and which correspond to the values of at least one physical quantity characteristic of the operation of the first generating unit and/or the coordinate measuring machine (211), - automatically generating a test data set from the acquired operating signals by a second generating unit (5) and automatically transferring the test data set to an evaluation unit (7) comprising an artificial intelligence (8), - evaluating the test data set by the evaluation unit (7) using the artificial intelligence (8).wherein the artificial intelligence (8) is in an operational state based on training using training datasets with associated additional information, each containing at least one piece of information about a functionality of the coordinate measuring machine (211) or a comparable coordinate measuring machine (211) that can be determined from the respective training dataset, and wherein the artificial intelligence (8) derives a statement concerning the functionality of the coordinate measuring machine (211) from the test dataset, - outputting the statement concerning the functionality of the coordinate measuring machine (211).