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Improvement of Tightening Reliability of Bolted Joints Using Elliptical Confidence Limit in Calibrated Wrench Method
ISSN: 0148-7191, e-ISSN: 2688-3627
Published April 14, 2020 by SAE International in United States
This content contains downloadable datasetsAnnotation ability available
The calibrated wrench method is used in the tightening of bolts in manufacturing industries in the case of a large amount of tightening work. It is important to apply a large initial clamping force to ensure tightening reliability and prevent self-loosening, fatigue breakage, and so forth. In this method, the clamping force of bolted joints is controlled using a torque wrench. However, since the clamping force is indirectly applied by a wrench, it varies greatly in the case of a large amount of tightening in a factory. Therefore, the calibrated wrench method is not so accurate from the viewpoint of clamping force control. It is conventionally thought that the distribution of the clamping force has the shape of a rhombus. When tightening torque and clamping force are considered to be two independent random variables, the clamping force is distributed within an elliptical confidence limit. Here, we show that the distribution of equivalent stress also has an elliptical confidence limit. Considering the permitted limit for working load stress on a bolted joint, the elliptical distribution has a larger margin to the yield point than the conventional rhombic distribution. Using this feature, we can set a higher target tightening torque than before. We show that a higher tightening torque and initial clamping force can be obtained with smaller variation than before. Finally, we establish a method for maintaining the tightening reliability that involves applying a large clamping force by increasing the target tightening torque using the elliptical confidence limit.
CitationHareyama, S., Manabe, K., and Kobayashi, S., "Improvement of Tightening Reliability of Bolted Joints Using Elliptical Confidence Limit in Calibrated Wrench Method," SAE Technical Paper 2020-01-0218, 2020, https://doi.org/10.4271/2020-01-0218.
Data Sets - Support Documents
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