Tightening Class Estimation Using Torque, Angle, and Context Data
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Solution Overview
Problem
Existing methods struggle to accurately determine the tightening class of a tightening operation performed by a tightening tool, as it is difficult for human operators or machines to analyze sensor data from the tool, and machine-learning models often require appropriate training to perform accurate estimations, which can be inconsistent across different tools or environments.
Innovation Solution
A method and device using machine-learning models that incorporate context data such as environmental conditions, previous tightening operations, and tool settings to improve the accuracy of tightening class estimation by associating torque and angle values with specific tightening operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If machine-learning models are used to analyze torque and angle tightening results, then the automation and speed of tightening class determination are improved, but the reliability and accuracy of the estimation deteriorate due to insufficient or inappropriate training data
Solution Approach 1:
The system performs preliminary training of the machine-learning model using extensive torque and angle data collected from multiple tightening operations before actual use. This preliminary action ensures the model is properly trained and reliable before it is deployed for automated tightening class determination, resolving the contradiction between automation and reliability.
Solution Approach 2:
The system implements a feedback mechanism where the machine-learning model continuously learns from new tightening data. The model's predictions are compared with actual outcomes, and this feedback is used to refine and improve the model's accuracy over time, ensuring reliable automated determination of tightening classes.
2Measurement precision
If extensive training data is collected to improve machine-learning model accuracy, then the measurement precision of tightening class determination is improved, but the loss of time and complexity of the process increases
Solution Approach 1:
The system collects and processes training data in advance, before actual tightening operations. By performing the time-consuming data collection and model training as a preliminary action, the system achieves high measurement precision without causing time delays during critical tightening operations.
Solution Approach 2:
The system uses a substantial amount of training data beyond the minimum required to achieve good model performance. This excessive action in data collection during the training phase ensures high precision during operation, while the actual tightening process remains fast since the model is already trained.
Data Source
Figure 1
Figure 2a~2b
Figure 2c~2d
AI summary
The present disclosure relates to a method of a device (20, 35) for determining a tightening class of a tightening operation performed by a tightening tool (10). The determination is based on training a machine-learning model with an acquired set of observed torque and angle values, at least one set of context data and one or more associated tightening classes. Subsequently, the trained model is utilized for determining from supplied torque and angle values, as well as context data, one or more tightening classes associated with the supplied values and context data