Manhart How to handle abnormal data when evaluating the accuracy of measurement results of a twisted axis CNC bending machine?

Handling abnormal data is a crucial step in evaluating the accuracy of the measurement results of a twisted axis CNC bending machine. Abnormal data may distort the accuracy of the overall measurement results, therefore appropriate methods must be taken to handle it. Here are some suggestions for handling abnormal data:
###1、 Identify abnormal data
1. * * Statistical methods * *:
-Use statistical methods such as boxplots, Z-scores, etc. to identify outliers in measurement data. Box plots can visually display the median, quartiles, and range of outliers of data, while Z-scores can evaluate the degree of deviation between data points and the mean.
2. * * Trend analysis * *:
-Observe the trend of data changes over time or measurement conditions, and identify data points that deviate significantly from the overall trend as outliers.
3. * * Professional judgment * *:
-Based on the operational experience, material characteristics, and process knowledge of the twisted axis CNC bending machine, make professional judgments on the measurement data and identify possible outliers.
###2、 Analyze the reasons for abnormal data
1. * * Equipment malfunction * *:
-Check whether the twisted axis CNC bending machine and its measuring equipment are operating normally, and whether there are any data abnormalities caused by faults or damages.
2. * * Operational error * *:
-Analyze whether the operators followed standardized procedures and whether there were any data anomalies caused by operational errors.
3. * * Environmental factors * *:
-Assess whether the measurement environment (such as temperature, humidity, vibration, etc.) has changed, which may affect the accuracy of the measurement results.
4. * * Data entry error * *:
-Check if there are any errors or omissions during the data entry process that may cause abnormal measurement data.
###3、 Handling abnormal data
1. * * Exclude abnormal data * *:
-If the abnormal data is indeed caused by equipment failure, operational errors, or data entry errors, and its quantity is small, you can choose to directly remove these abnormal data. However, caution should be exercised when removing abnormal data to avoid accidentally deleting useful information.
2. * * Correct abnormal data * *:
-If the specific cause of abnormal data can be determined and corrected through certain methods such as remeasurement, equipment calibration, etc., then it is possible to choose to correct these abnormal data.
3. * * Retain abnormal data and record * *:
-In some cases, abnormal data may contain useful information or indicate potential issues. At this point, abnormal data can be retained and annotated or recorded in the dataset for subsequent analysis and processing.
4. * * Adopting robust statistical methods * *:
-In the data analysis stage, robust statistical methods such as median, absolute deviation median, etc. can be used to handle outlier data. These methods have good robustness against outliers and can reduce the impact of outliers on the overall measurement results.
###4、 Summary and improvement
1. * * Summary of Experience * *:
-Summarize the process of handling abnormal data, analyze the causes of abnormal data generation and the effectiveness of processing methods, and provide experience and reference for future measurement work.
2. * * Improvement Measures * *:
-Develop corresponding improvement measures based on the causes of abnormal data. For example, strengthening equipment maintenance and calibration, optimizing operational processes, improving measurement environments, etc., to reduce the generation of abnormal data in future measurements.
In summary, handling abnormal data in the measurement results of a twisted axis CNC bending machine is a process that requires comprehensive consideration of multiple aspects. By identifying abnormal data, analyzing the causes, adopting appropriate handling methods, and summarizing experience and improvement measures, the accuracy and reliability of measurement results can be improved.