Accurate Bolt Tightening using Model-Free Fuzzy Control for Wind Turbine Hub Bearing Assembly

"In the modern wind turbine industry, one of the core processes is the assembly of the bolt-nut connections of the hub, which requires tightening bolts and nuts to obtain well-distributed clamping force all over the hub. This force deals with nonlinear uncertainties due to the mechanical properties and it depends on the final torque and relative angular position of the bolt/nut connection. This paper handles the control problem of automated bolt tightening processes. To develop a controller, the process is divided into four stages, according to the mechanical characteristics of the bolt/nut connection: a Fuzzy Logic Controller (FLC) with expert knowledge of tightening process and error detection capability is proposed. For each one of the four stages, an individual FLC is designed to address the highly non-linearity of the system and the error scenarios related to that stage, to promptly prevent and avoid mechanical damage. The FLC is implemented and real time executed on an industrial PC and finally validated. Experimental results show the performance of the controller to reach precise torque and angle levels as well as desired clamping force. The capability of error detection is also validated.

Screwing process analysis using multivariate statistical process control

Screws are widely used for parts joining in industry. The definition of effective monitoring strategies for screwing processes can help to prevent or significantly reduce ineffective procedures, defective screwing and downtime. Monitoring several correlated variables simultaneously in order to detect relevant changes in manufacturing processes is an increasingly frequent practice furthered by advanced data acquisition systems. However, the monitoring approaches currently used do not consider the multivariate nature of the screwing processes. This paper presents the results of a study performed in an automotive electronics assembly line. Screwing process data concerning torque and rotation angle were analyzed using multivariate statistical process control based on principal component analysis (MSPC-PCA). The main purpose was to extract relevant information from a high number of correlated variables in order to early detect undesirable changes in the process performance. A PCA model was defined based on three principal components. The physical meaning of each component was identified, and underlying causes were inferred based on technical knowledge about the process. Monitoring tools, such as score plots and multivariate control charts allowed to detect the defective screwing cases included in the analyzed data set. Furthermore, eight periods of instability were identified. Considering that the out-of-control signals detected in these periods mainly correspond to delays at the beginning of the tightening operation, four potential causes to explain this behavior were ascertained and analyzed. This research allowed to acquire a deeper understanding on the screwing process behavior and about the causes with higher impact on its stability. Due to its flexibility and versatility, it is considered that this approach can be applied to effectively monitor screwing pCEC - Clinical Excellence Commission(undefined