Manhart ¿Cómo manejar valores atípicos en el proceso de medición de alta precisión de una máquina dobladora CNC de eje torcido?

It is crucial to protect the integrity and accuracy of data when dealing with outliers in the high-precision measurement process of torsion axis CNC bending machines. Here are some suggestions and methods that can help ensure the integrity and accuracy of data:
###1、 Clarify the principles of handling outliers
1. Objectivity: The handling of outliers should be based on objective data analysis rather than subjective speculation.
2. * * Scientificity * *: Using scientific statistical methods and tools to identify and handle outliers.
3. Rationality: The handling of outliers should be in line with the actual situation and measurement requirements, avoiding excessive processing or ignoring important information.
###2、 Standardize the process of handling outliers
1. Data collection and organization: Firstly, ensure that the collected data is complete and accurate, and conduct preliminary organization for subsequent analysis.
2. Outlier Identification: Using statistical methods (such as boxplots, Z-scores, IQR, etc.) or professional knowledge to identify outliers. Note that different methods may produce different results, and the selection should be based on the actual situation.
3. Outlier analysis: Conduct in-depth analysis of identified outliers to determine their causes. Possible reasons include measurement errors, equipment malfunctions, material abnormalities, environmental or human factors, etc.
4. * * Exception handling * *:
-* * Exclusion * *: If the outlier is caused by non systematic reasons such as measurement errors, equipment failures, or human factors, and has a small impact on the overall data, it can be excluded.
-* * Retain and explain * *: If the outlier is caused by systematic reasons (such as material properties, environmental factors) and is of significant importance to data analysis, it should be retained and explained in detail in the report.
-Correction: In rare cases, if outliers can be corrected through scientific methods (such as using averages, trend lines, etc.) and the corrected data is more in line with the actual situation, correction can be considered. But this method should be used with caution to avoid introducing new errors.
5. * * Records and Reports * *: Regardless of how outliers are handled, the handling process and results should be detailed in the measurement records or reports for subsequent traceability and review.
###3、 Strengthen data quality control
1. * * Improve measurement accuracy * *: By selecting high-precision measurement tools, optimizing measurement methods, and strengthening equipment maintenance, measures can be taken to improve measurement accuracy and reduce the occurrence of outliers.
2. * * Standardized operating procedures * *: Develop and strictly implement measurement operating procedures and standards to reduce the impact of human factors on measurement results.
3. * * Monitoring environmental factors * *: Monitor and control the measurement environment to ensure that it fluctuates within an appropriate range, in order to reduce the impact of environmental factors on the measurement results.
###4、 Regular review and evaluation
1. * * Regular review of data * *: Regularly review and analyze measurement data to promptly identify and handle outliers.
2. * * Evaluate the measurement system * *: Regularly evaluate and improve the measurement system, including equipment calibration, method optimization, and other aspects, to improve the stability and reliability of the measurement system.
In summary, when dealing with abnormal values during high-precision measurement of torsion axis CNC bending machines, the principles of objectivity, scientificity, and rationality should be followed, the processing flow should be standardized, data quality control should be strengthened, and the measurement system should be regularly reviewed and evaluated. By implementing these measures, the integrity and accuracy of data can be effectively protected, and the reliability and credibility of measurement results can be improved.