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Bolt Tightening

The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform

Kaspar Althoefer – 1st expert on this subject based on the ideXlab platform

  • A K-nearest clamping force classifier for Bolt Tightening of wind turbine hubs
    , 2016
    Co-Authors: Emanuele Lindo Secco, C. Deters, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar Althoefer

    Abstract:

    A fuzzy-logic controller supporting the manufacturing of wind turbines and the Bolt Tightening of their hubs has been designed. The controller embeds assembly error recognition capability and detects Tightening faults like misalignment, different threads, cross threads and wrong or small nuts. According to this capability, K-nearest classifiers have been implemented to cluster the output controllers into the diverse fault scenarios. Classifiers make use of the time of execution of the Tightening process, the final angular position of and applied torque of the Tightening tool, the resultant clamping force and possible combinations of those parameters. Two classes and five classes configurations are considered: classifiers are initially asked to discriminate between fault and no fault scenarios (e.g. two classes); then, five classes are considered according to five different fault situations (i.e. regular Tightening, Bolt misalignment, dissimilar threads of Bolt and nut, missing nut and small Bolt). Classifiers performances are estimated in terms of resubstitution and cross-validation loss. Confusion matrixes of actual and predicted classification are also evaluated for each classifier. The low computational cost of the proposed classifiers suggests directly implementing these algorithms on micro-controller and physical computing, which may be straight integrated within the Tightening tool.

  • accurate Bolt Tightening using model free fuzzy control for wind turbine hub bearing assembly
    IEEE Transactions on Control Systems and Technology, 2015
    Co-Authors: C. Deters, Emanuele Lindo Secco, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar Althoefer

    Abstract:

    “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.

  • A Neural Network Clamping Force Model for Bolt Tightening of Wind Turbine Hubs
    2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable Autonomic and Secure Co, 2015
    Co-Authors: Emanuele Lindo Secco, C. Deters, Helge A. Wurdemann, Atulya Nagar, Kaspar Althoefer

    Abstract:

    Industrial manufacturing of large-scale wind turbines requires the accurate Tightening of multiple Bolts and nuts, which connect the ball bearings – supporting wind turbine blades – with the hub, a huge mechanical component supporting blades pitch motion. An accurate Tightening of Bolts and nuts requires uniformly distributed clamping forces along flanges and surfaces of contact between hub and bearings. Due to the role of friction forces and the dynamics of the phenomenon, this process is nonlinear and currently performed manually, it is also time consuming, requiring high-cost equipment and expert operators. This paper proposes a set of neural networks, which infer the clamping force achievable with a Tightening tool while fastening M24 nuts on Bolts. The tool embeds a torque sensor and shaft encoder, therefore two types of inputs of the neural networks are considered in order to fit the clamping force output: the time signals of (a) the applied torque of the tool and (b) the combination of the torque and of the angular speed of the tool. According to results, neural networks properly model the clamping force, both during the training stage and when exposed to unseen testing data. This approach could be generalized to other industrial processes and specifically to those requiring repetitive Tightening tasks and involving highly nonlinear aspects, such as friction forces.

C. Deters – 2nd expert on this subject based on the ideXlab platform

  • A K-nearest clamping force classifier for Bolt Tightening of wind turbine hubs
    , 2016
    Co-Authors: Emanuele Lindo Secco, C. Deters, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar Althoefer

    Abstract:

    A fuzzy-logic controller supporting the manufacturing of wind turbines and the Bolt Tightening of their hubs has been designed. The controller embeds assembly error recognition capability and detects Tightening faults like misalignment, different threads, cross threads and wrong or small nuts. According to this capability, K-nearest classifiers have been implemented to cluster the output controllers into the diverse fault scenarios. Classifiers make use of the time of execution of the Tightening process, the final angular position of and applied torque of the Tightening tool, the resultant clamping force and possible combinations of those parameters. Two classes and five classes configurations are considered: classifiers are initially asked to discriminate between fault and no fault scenarios (e.g. two classes); then, five classes are considered according to five different fault situations (i.e. regular Tightening, Bolt misalignment, dissimilar threads of Bolt and nut, missing nut and small Bolt). Classifiers performances are estimated in terms of resubstitution and cross-validation loss. Confusion matrixes of actual and predicted classification are also evaluated for each classifier. The low computational cost of the proposed classifiers suggests directly implementing these algorithms on micro-controller and physical computing, which may be straight integrated within the Tightening tool.

  • accurate Bolt Tightening using model free fuzzy control for wind turbine hub bearing assembly
    IEEE Transactions on Control Systems and Technology, 2015
    Co-Authors: C. Deters, Emanuele Lindo Secco, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar Althoefer

    Abstract:

    “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.

  • A Neural Network Clamping Force Model for Bolt Tightening of Wind Turbine Hubs
    2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable Autonomic and Secure Co, 2015
    Co-Authors: Emanuele Lindo Secco, C. Deters, Helge A. Wurdemann, Atulya Nagar, Kaspar Althoefer

    Abstract:

    Industrial manufacturing of large-scale wind turbines requires the accurate Tightening of multiple Bolts and nuts, which connect the ball bearings – supporting wind turbine blades – with the hub, a huge mechanical component supporting blades pitch motion. An accurate Tightening of Bolts and nuts requires uniformly distributed clamping forces along flanges and surfaces of contact between hub and bearings. Due to the role of friction forces and the dynamics of the phenomenon, this process is nonlinear and currently performed manually, it is also time consuming, requiring high-cost equipment and expert operators. This paper proposes a set of neural networks, which infer the clamping force achievable with a Tightening tool while fastening M24 nuts on Bolts. The tool embeds a torque sensor and shaft encoder, therefore two types of inputs of the neural networks are considered in order to fit the clamping force output: the time signals of (a) the applied torque of the tool and (b) the combination of the torque and of the angular speed of the tool. According to results, neural networks properly model the clamping force, both during the training stage and when exposed to unseen testing data. This approach could be generalized to other industrial processes and specifically to those requiring repetitive Tightening tasks and involving highly nonlinear aspects, such as friction forces.

Emanuele Lindo Secco – 3rd expert on this subject based on the ideXlab platform

  • A K-nearest clamping force classifier for Bolt Tightening of wind turbine hubs
    , 2016
    Co-Authors: Emanuele Lindo Secco, C. Deters, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar Althoefer

    Abstract:

    A fuzzy-logic controller supporting the manufacturing of wind turbines and the Bolt Tightening of their hubs has been designed. The controller embeds assembly error recognition capability and detects Tightening faults like misalignment, different threads, cross threads and wrong or small nuts. According to this capability, K-nearest classifiers have been implemented to cluster the output controllers into the diverse fault scenarios. Classifiers make use of the time of execution of the Tightening process, the final angular position of and applied torque of the Tightening tool, the resultant clamping force and possible combinations of those parameters. Two classes and five classes configurations are considered: classifiers are initially asked to discriminate between fault and no fault scenarios (e.g. two classes); then, five classes are considered according to five different fault situations (i.e. regular Tightening, Bolt misalignment, dissimilar threads of Bolt and nut, missing nut and small Bolt). Classifiers performances are estimated in terms of resubstitution and cross-validation loss. Confusion matrixes of actual and predicted classification are also evaluated for each classifier. The low computational cost of the proposed classifiers suggests directly implementing these algorithms on micro-controller and physical computing, which may be straight integrated within the Tightening tool.

  • accurate Bolt Tightening using model free fuzzy control for wind turbine hub bearing assembly
    IEEE Transactions on Control Systems and Technology, 2015
    Co-Authors: C. Deters, Emanuele Lindo Secco, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar Althoefer

    Abstract:

    “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.

  • A Neural Network Clamping Force Model for Bolt Tightening of Wind Turbine Hubs
    2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable Autonomic and Secure Co, 2015
    Co-Authors: Emanuele Lindo Secco, C. Deters, Helge A. Wurdemann, Atulya Nagar, Kaspar Althoefer

    Abstract:

    Industrial manufacturing of large-scale wind turbines requires the accurate Tightening of multiple Bolts and nuts, which connect the ball bearings – supporting wind turbine blades – with the hub, a huge mechanical component supporting blades pitch motion. An accurate Tightening of Bolts and nuts requires uniformly distributed clamping forces along flanges and surfaces of contact between hub and bearings. Due to the role of friction forces and the dynamics of the phenomenon, this process is nonlinear and currently performed manually, it is also time consuming, requiring high-cost equipment and expert operators. This paper proposes a set of neural networks, which infer the clamping force achievable with a Tightening tool while fastening M24 nuts on Bolts. The tool embeds a torque sensor and shaft encoder, therefore two types of inputs of the neural networks are considered in order to fit the clamping force output: the time signals of (a) the applied torque of the tool and (b) the combination of the torque and of the angular speed of the tool. According to results, neural networks properly model the clamping force, both during the training stage and when exposed to unseen testing data. This approach could be generalized to other industrial processes and specifically to those requiring repetitive Tightening tasks and involving highly nonlinear aspects, such as friction forces.