Assembly line workers use torque tools to fasten parts together. These tools collect a wealth of data about the process, but what can engineers learn from it? With simple statistical analysis, engineers can obtain valuable and important insight into the fastening process.
No matter the tool’s accuracy, there will always be variations in the amount of torque applied to a fastener. A multitude of factors influences this process. Fortunately, statistical analysis can tell engineers when such variation is unacceptable.
This article will examine the importance of recording torque data on assembly lines and why it influences the assembly process. By understanding the torque data, engineers can improve the efficiency and quality of the assembled products.
What Engineers Can Learn From Torque Data
Torque data is essential for understanding how the assembly process works. By recording and analyzing torque data, engineers can improve the efficiency of the assembly line, the quality of the products being assembled, and the safety of the assembly process.
Many factors can affect torque data, including:
- The type of torque tool being used
- The skill of the operator
- The condition of the parts being assembled
- The dimensions of the parts being assembled
- Environmental factors, such as humidity and temperature
- Friction, the difference in air pressure, part dimensions, and other factors
Each of these factors can affect the torque data in different ways. For example, a skilled operator will likely produce more consistent torque data than an unskilled operator. In contrast, humidity can significantly affect torque data, as it can cause parts to expand.
With torque data, engineers can better prepare for these issues by understanding how they will affect the assembly process. Recording torque data guarantees engineers that they can improve the assembly process in many ways, including:
- Determining the best torque tool for the job
- Developing training programs for operators
- Identifying and correcting issues with parts
- Adjusting the assembly process to account for environmental factors
Finding the Standard Deviation
The goal of torque data analysis is to find the standard deviation of a torque tool. The standard deviation measures how much torque data varies from the mean, or average, torque. Engineers first need to find the mean torque to find the standard deviation.
Next, they need to subtract the mean torque from each torque reading. This difference is called the deviation. Finally, engineers need to square each deviation and add them all together to determine the sum of squares.
The standard deviation is found by taking the square root of the sum of squares and dividing it by the number of torque readings. This number gives engineers a measure of how much torque data varies from the mean torque.
Why Standard Deviation Matters
The standard deviation is important because it allows engineers to set limits on torque data. These limits help ensure that parts are fastened properly.
Torque data that falls within limits is considered to be acceptable. Torque data that falls outside of the limits is considered to be unacceptable. Unacceptable torque data can be caused by many factors, including operator error, environmental factors, and issues with parts.
Engineers need to investigate the cause when torque data falls outside the limits. Doing so can determine if there is an issue with the torque tool, the operator, the environment, or the parts. Once the cause is determined, engineers can take steps to correct the issue and prevent it from happening again.
X-Bar and R-Charts
To effectively analyze torque data, engineers need to use X-bar and E-charts. X-bar charts help engineers track the mean torque over time. R-charts help engineers track the range of torque readings over time.
Both X-bar and R-charts are important because they allow engineers to identify trends in torque data. These trends can be caused by many factors, including changes in the torque tool, the operator, the environment, or the parts.
By identifying trends in torque data, engineers can take steps to correct them. This helps ensure that parts are fastened correctly and consistently.
Creating an X-Bar Chart
To create an X-bar chart, engineers need to follow these steps:
- Collect torque data for a period of time.
- Calculate the mean torque for each group of readings.
- Plot the mean torque on the X-axis and the number of readings on the Y-axis.
Creating an R-Chart
To create an R-chart, engineers need to follow these steps:
- Collect torque data for a period of time.
- Calculate the range of torque readings for each group of readings.
- Plot the range of torque readings on the x-axis and the number of readings on the y-axis.
Interpreting X-Bar and R-Charts
Once X-bar and R-charts are created, engineers need to interpret them. To do this, they need to compare the charts to control limits.
Control limits are set by engineers and indicate the acceptable range of torque readings. When torque data falls outside the control limits, it is considered unacceptable.
Why Use DATAMYTE?
The statistical process control of torque data on assembly lines is important for engineers to understand the fastening process. Using DATAMYTE, engineers can collect torque data and create X-bar and R-charts that help them identify trends and set control limits. Doing so ensures that parts are fastened the right way.
The DataMyte Digital Clipboard is a workflow automation software that lets you collect torque data on assembly lines. The software is designed to help engineers improve the fastening process by providing valuable insight into torque data.
In addition, the DataMyte Digital Clipboard also lets you create checklists and other relevant forms for other essential tasks such as torque audits, maintenance tasks, and torque tool calibration. These checklists and forms help ensure that all tasks are completed correctly and consistently.
Book a demo with us today to learn more about the DataMyte Digital Clipboard. We’ll show you how the software can help you improve the fastening process on your assembly line.
Statistical process control of torque data on assembly lines is important for engineers to understand the fastening process. Engineers can learn about trends and set control limits by looking at torque data. This helps ensure that parts are fastened in the correct measurements.
The DataMyte Digital Clipboard is a workflow automation software that lets you collect torque data on assembly lines. The software is designed to help engineers improve the fastening process by providing valuable insight into torque data. Get started now!