Prediction of self-loosening mechanism and behavior of bolted joints on automotive chassis using artificial intelligence

Abstract

The tightening torque values considered in the assembly of vehicle subparts are of great importance in terms of connection safety. The torque value to be selected is different for each bolted joint type with respect to mechanical features. While the tightening torque value is an important indicator, the bolt preloading value is always a more reliable parameter in terms of whether a secure tightening can be achieved or not. For this reason, when it is desired to create reliable joints, the preloading value that the tightening torque input will create on the connection package should be calculated well. This study presents an integrated approach using Taguchi method (TM) and neural network (NN) to predict the self-loosening mechanism of bolted joints in automotive chassis engine suspension connections. External loading acting on the joints of the engine suspension was collected from bench tests. NN was applied to establish the relationship between controlled factors and loosening rate. The results showed that the proposed approach can be used to predict mechanism of self-loosening and behavior of bolted joints without additional tests, and it is possible to make predictions with very low error rates using artificial intelligence techniques.