Model Based Monitoring Strategies for Threaded Assembly System

Abstract

Threaded fastenings are a common assembly method, accounting for over a quarter of all assembly operations. Threaded fastenings are popular because they permit easy disassembly for maintenance, repair, relocation and recycling. A model based approach for monitoring screw fastenings is presented in this paper. The proposed strategy can be integrated with a mechatronics based approach for automating threaded assembly systems. The screw insertion process is typically monitored using the torque vs insertion depth signature signals. For a screw insertion with a given set of parameters, the insertion signature signal will have a unique identity and by comparing the on line signature signals with the ideal signature signal, the integrity of the joint can be predicted. The approach adopted in this study is to use an analytical model developed by the authors to predict ideal insertion signals. However, this model requires various fixed process parameters as input, and it is not always possible to know these parameters in advance with sufficient accuracy. Hence the focus of this paper is the on-line parameter estimation during threaded assembly. A methodology for estimating four unknown parameters of a general self-tapping screw insertion is presented. The approach is based on the Newton Raphson Method (NRM). It is shown that up to four parameters required by the model can be reliably estimated in real time, from on-line torque signature signals. Experimental results are presented to validate the estimation procedure.