The Experts below are selected from a list of 291 Experts worldwide ranked by ideXlab platform
Kaspar Althoefer - One of the best experts on this subject based on the ideXlab platform.
-
A K-nearest clamping force classifier for Bolt Tightening of wind turbine hubs
2016Co-Authors: Emanuele Lindo Secco, C. Deters, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar AlthoeferAbstract: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, 2015Co-Authors: C. Deters, Lakmal D. Seneviratne, Emanuele Lindo Secco, Helge A. Wurdemann, Kaspar AlthoeferAbstract:"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, 2015Co-Authors: Emanuele Lindo Secco, C. Deters, Helge A. Wurdemann, Atulya Nagar, Kaspar AlthoeferAbstract: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.
-
model free fuzzy Tightening control for Bolt nut joint connections of wind turbine hubs
International Conference on Robotics and Automation, 2013Co-Authors: C. Deters, H. A. Wuerdemann, Lakmal D. Seneviratne, Emanuele Lindo Secco, Kaspar AlthoeferAbstract:In the wind turbine manufacturing industry, the Bolt-nut joint Tightening process is one of the core processes in the full production chain and concerned with assembling the hub body, the pitch system and the bearing unit. This operation is currently executed manually with the aid of different tools and gauges; the main disadvantages are a relatively high degree of variability and the necessity to repeat this task several times during a production run to achieve a satisfactory, final Tightening torque within a specified angle range. Moreover, the Bolt Tightening process includes various uncertainties such as the presence of friction forces and the use of different Bolt sizes with different stiffness values which make it highly nonlinear and uncertain resulting in a challenging control problem. To facilitate the development of an effective control strategy, we study the Bolt Tightening process and propose 4 Tightening stages, namely, Bolt-nut alignment, partial and full engagement and final Bolt Tightening. Based on the characteristics of each stage, a fuzzy controller is designed for each stage to realize the respective control objectives. A fuzzy error detector incorporating the knowledge of each stage is proposed for early error detection, making use of the input from a torque and encoder (angular position) sensor. Errors can be detected in each stage to interrupt the process and prevent any damage to the system.
-
Model-free fuzzy Tightening control for Bolt/nut joint connections of wind turbine hubs
Proceedings - IEEE International Conference on Robotics and Automation, 2013Co-Authors: C. Deters, H. A. Wuerdemann, Lakmal D. Seneviratne, H. K. Lam, Emanuele Lindo Secco, Kaspar AlthoeferAbstract:In the wind turbine manufacturing industry, the Bolt-nut joint Tightening process is one of the core processes in the full production chain and concerned with assembling the hub body, the pitch system and the bearing unit. This operation is currently executed manually with the aid of different tools and gauges; the main disadvantages are a relatively high degree of variability and the necessity to repeat this task several times during a production run to achieve a satisfactory, final Tightening torque within a specified angle range. Moreover, the Bolt Tightening process includes various uncertainties such as the presence of friction forces and the use of different Bolt sizes with different stiffness values which make it highly nonlinear and uncertain resulting in a challenging control problem. To facilitate the development of an effective control strategy, we study the Bolt Tightening process and propose 4 Tightening stages, namely, Bolt-nut alignment, partial and full engagement and final Bolt Tightening. Based on the characteristics of each stage, a fuzzy controller is designed for each stage to realize the respective control objectives. A fuzzy error detector incorporating the knowledge of each stage is proposed for early error detection, making use of the input from a torque and encoder (angular position) sensor. Errors can be detected in each stage to interrupt the process and prevent any damage to the system.
C. Deters - One of the best experts on this subject based on the ideXlab platform.
-
A K-nearest clamping force classifier for Bolt Tightening of wind turbine hubs
2016Co-Authors: Emanuele Lindo Secco, C. Deters, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar AlthoeferAbstract: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, 2015Co-Authors: C. Deters, Lakmal D. Seneviratne, Emanuele Lindo Secco, Helge A. Wurdemann, Kaspar AlthoeferAbstract:"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, 2015Co-Authors: Emanuele Lindo Secco, C. Deters, Helge A. Wurdemann, Atulya Nagar, Kaspar AlthoeferAbstract: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.
-
Model-Based Self-Tuning PI Control of Bolt-Nut Tightening for Wind Turbine Bearing Assembly
2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable Autonomic and Secure Co, 2015Co-Authors: C. Deters, Mark Barrett-baxendale, Emanuele Lindo SeccoAbstract:One of the core steps of the assembly of wind turbines is the assembly of the bearings on the wind turbine hub. The hub can contain up to 128 Bolt connections to install the bearing blades: nuts need to be precisely tightened to ensure a uniformly distributed clamping force as well as avoiding assembly errors, e.g. nut misalignments. The Bolt-nut connection is a non-linear system with uncertainties making it difficult to design a numerical model and PI Gains. This paper presents a novel two-stage Proportional-Integral (PI) controller with assembly error detection capability for Bolt Tightening process. It is based on the combination of a numerical model (offline training) and a genetic algorithm (GA) for online training on the physical Bolt system. Since the model does not include all non-linearity and uncertainties of the physical plant (here the Bolt-nut connection), it is used at first to estimate the range of the PI values, followed by a fine tuning of the values online by the GA.
-
model free fuzzy Tightening control for Bolt nut joint connections of wind turbine hubs
International Conference on Robotics and Automation, 2013Co-Authors: C. Deters, H. A. Wuerdemann, Lakmal D. Seneviratne, Emanuele Lindo Secco, Kaspar AlthoeferAbstract:In the wind turbine manufacturing industry, the Bolt-nut joint Tightening process is one of the core processes in the full production chain and concerned with assembling the hub body, the pitch system and the bearing unit. This operation is currently executed manually with the aid of different tools and gauges; the main disadvantages are a relatively high degree of variability and the necessity to repeat this task several times during a production run to achieve a satisfactory, final Tightening torque within a specified angle range. Moreover, the Bolt Tightening process includes various uncertainties such as the presence of friction forces and the use of different Bolt sizes with different stiffness values which make it highly nonlinear and uncertain resulting in a challenging control problem. To facilitate the development of an effective control strategy, we study the Bolt Tightening process and propose 4 Tightening stages, namely, Bolt-nut alignment, partial and full engagement and final Bolt Tightening. Based on the characteristics of each stage, a fuzzy controller is designed for each stage to realize the respective control objectives. A fuzzy error detector incorporating the knowledge of each stage is proposed for early error detection, making use of the input from a torque and encoder (angular position) sensor. Errors can be detected in each stage to interrupt the process and prevent any damage to the system.
Emanuele Lindo Secco - One of the best experts on this subject based on the ideXlab platform.
-
A K-nearest clamping force classifier for Bolt Tightening of wind turbine hubs
2016Co-Authors: Emanuele Lindo Secco, C. Deters, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar AlthoeferAbstract: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, 2015Co-Authors: C. Deters, Lakmal D. Seneviratne, Emanuele Lindo Secco, Helge A. Wurdemann, Kaspar AlthoeferAbstract:"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, 2015Co-Authors: Emanuele Lindo Secco, C. Deters, Helge A. Wurdemann, Atulya Nagar, Kaspar AlthoeferAbstract: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.
-
Model-Based Self-Tuning PI Control of Bolt-Nut Tightening for Wind Turbine Bearing Assembly
2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable Autonomic and Secure Co, 2015Co-Authors: C. Deters, Mark Barrett-baxendale, Emanuele Lindo SeccoAbstract:One of the core steps of the assembly of wind turbines is the assembly of the bearings on the wind turbine hub. The hub can contain up to 128 Bolt connections to install the bearing blades: nuts need to be precisely tightened to ensure a uniformly distributed clamping force as well as avoiding assembly errors, e.g. nut misalignments. The Bolt-nut connection is a non-linear system with uncertainties making it difficult to design a numerical model and PI Gains. This paper presents a novel two-stage Proportional-Integral (PI) controller with assembly error detection capability for Bolt Tightening process. It is based on the combination of a numerical model (offline training) and a genetic algorithm (GA) for online training on the physical Bolt system. Since the model does not include all non-linearity and uncertainties of the physical plant (here the Bolt-nut connection), it is used at first to estimate the range of the PI values, followed by a fine tuning of the values online by the GA.
-
model free fuzzy Tightening control for Bolt nut joint connections of wind turbine hubs
International Conference on Robotics and Automation, 2013Co-Authors: C. Deters, H. A. Wuerdemann, Lakmal D. Seneviratne, Emanuele Lindo Secco, Kaspar AlthoeferAbstract:In the wind turbine manufacturing industry, the Bolt-nut joint Tightening process is one of the core processes in the full production chain and concerned with assembling the hub body, the pitch system and the bearing unit. This operation is currently executed manually with the aid of different tools and gauges; the main disadvantages are a relatively high degree of variability and the necessity to repeat this task several times during a production run to achieve a satisfactory, final Tightening torque within a specified angle range. Moreover, the Bolt Tightening process includes various uncertainties such as the presence of friction forces and the use of different Bolt sizes with different stiffness values which make it highly nonlinear and uncertain resulting in a challenging control problem. To facilitate the development of an effective control strategy, we study the Bolt Tightening process and propose 4 Tightening stages, namely, Bolt-nut alignment, partial and full engagement and final Bolt Tightening. Based on the characteristics of each stage, a fuzzy controller is designed for each stage to realize the respective control objectives. A fuzzy error detector incorporating the knowledge of each stage is proposed for early error detection, making use of the input from a torque and encoder (angular position) sensor. Errors can be detected in each stage to interrupt the process and prevent any damage to the system.
Sayed A. Nassar - One of the best experts on this subject based on the ideXlab platform.
-
Effect of Bolt Tightening and Joint Material on the Strength and Behavior of Composite Joints
Volume 4c: 18th Reliability Stress Analysis and Failure Prevention Conference, 2020Co-Authors: Sayed A. Nassar, Vinayshankar L. VirupakshaAbstract:This experimental study investigates the effect of the various combinations of Bolt Tightening and joint material on the strength and behavior of single lap, double Bolted composite joints. The strength of the Bolted joint is determined from a load-displacement test in which the joint members are pulled in the transverse direction relative to the Bolts axis. Additionally, damage assessment is performed on the bearing surface between the shank of each Bolt and the members of the joint. The bearing surface damage is examined using a Motic Microscope. Four Tightening configurations are used in the testing of each joint. These configurations permit each Bolt to be in either tight or loose conditions. The effect of joint material is also investigated, as well. Tested joints include composite-to-composite and composite-to-aluminum joint combination. The metric M8x1.25 Bolts; Class 8.8 Bolts are used in this study. Experimental results, data analysis, and conclusions are presented.Copyright © 2005 by ASME
-
Viscoelastic Strain Hardening Model for Gasket Creep Relaxation
Journal of Pressure Vessel Technology-transactions of The Asme, 2013Co-Authors: Antoine Abboud, Sayed A. NassarAbstract:This paper proposes a novel strain hardening model for investigating gasket creep relaxation under compressive step-loading at room temperature. A closed form solution is developed for predicting the steady-state gasket pressure. Step-loading of the gasket may be directly achieved and controlled, or indirectly estimated through the Bolt Tightening and reTightening torque. The effect of gasket material, time duration at each stress level, as well as the geometric parameters of the gasket are investigated. An experimental procedure and test setup are used to validate the proposed gasket model.
-
A Novel Optical Method for Real-Time Control of Bolt Tightening
Journal of Pressure Vessel Technology-transactions of The Asme, 2011Co-Authors: Aidong Meng, Sayed A. Nassar, Douglas W. TempletonAbstract:A digital speckle pattern interferometry (DSPI) system is developed for the real-time measurement and monitoring of the out-of-plane surface deformation around a preloaded Bolt head or nut. The proposed system is specifically developed for the dynamic control of the Bolt Tightening process by continuously monitoring the out-of-plane joint surface deformation that will have been independently correlated to the Bolt preload. Spatial phase shifting is employed to quantitatively determine the distribution of phase data by introducing a spatial carrier fringe pattern to the speckle interferogram. This is achieved by leading the object and reference beams through two separate apertures. The configuration is also suitable for collecting the real-time surface deformation during Bolt Tightening. The experimental DSPI system is set-up with optical components on a vibration-isolation table. A MATLAB software is developed for image acquisition and phase data calculations that yield the out-of-plane surface deformation caused by the Bolt preload. The test fixture uses an M12 steel fastener and aluminum joint. For miniature screw applications, however, a plastic joint is used.
-
A novel optical method for real-time control of Bolt Tightening
Journal of Pressure Vessel Technology, Transactions of the ASME, 2011Co-Authors: A.a Meng, Sayed A. Nassar, D.b TempletonAbstract:A digital speckle pattern interferometry (DSPI) system is developed for the real-time measurement and monitoring of the out-of-plane surface deformation around a preloaded Bolt head or nut. The proposed system is specifically developed for the dynamic control of the Bolt Tightening process by continuously monitoring the out-of-plane joint surface deformation that will have been independently correlated to the Bolt preload. Spatial phase shifting is employed to quantitatively determine the distribution of phase data by introducing a spatial carrier fringe pattern to the speckle interferogram. This is achieved by leading the object and reference beams through two separate apertures. The configuration is also suitable for collecting the real-time surface deformation during Bolt Tightening. The experimental DSPI system is set-up with optical components on a vibration-isolation table. A MATLAB software is developed for image acquisition and phase data calculations that yield the out-of-plane surface deformation caused by the Bolt preload. The test fixture uses an M12 steel fastener and aluminum joint. For miniature screw applications, however, a plastic joint is used. © 2011 American Society of Mechanical Engineers.
-
Formulation of a Strain Hardening Model for Gasket Creep Relaxation
ASME 2011 Pressure Vessels and Piping Conference: Volume 2, 2011Co-Authors: Antoine Abboud, Sayed A. NassarAbstract:This paper proposes a novel strain hardening model for investigating gasket creep relaxation under compressive step-loading at room temperature. A closed form solution is developed for predicting the steady-state gasket pressure. Step-loading of the gasket may be directly achieved and controlled, or indirectly estimated through the Bolt Tightening and re-Tightening torque. The effect of gasket material, time duration at each stress level, as well as the geometric parameters of the gasket is investigated. An experimental procedure and test set-up are used to validate the proposed gasket model.Copyright © 2011 by ASME
Lakmal D. Seneviratne - One of the best experts on this subject based on the ideXlab platform.
-
A K-nearest clamping force classifier for Bolt Tightening of wind turbine hubs
2016Co-Authors: Emanuele Lindo Secco, C. Deters, Lakmal D. Seneviratne, Helge A. Wurdemann, Kaspar AlthoeferAbstract: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, 2015Co-Authors: C. Deters, Lakmal D. Seneviratne, Emanuele Lindo Secco, Helge A. Wurdemann, Kaspar AlthoeferAbstract:"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. "
-
model free fuzzy Tightening control for Bolt nut joint connections of wind turbine hubs
International Conference on Robotics and Automation, 2013Co-Authors: C. Deters, H. A. Wuerdemann, Lakmal D. Seneviratne, Emanuele Lindo Secco, Kaspar AlthoeferAbstract:In the wind turbine manufacturing industry, the Bolt-nut joint Tightening process is one of the core processes in the full production chain and concerned with assembling the hub body, the pitch system and the bearing unit. This operation is currently executed manually with the aid of different tools and gauges; the main disadvantages are a relatively high degree of variability and the necessity to repeat this task several times during a production run to achieve a satisfactory, final Tightening torque within a specified angle range. Moreover, the Bolt Tightening process includes various uncertainties such as the presence of friction forces and the use of different Bolt sizes with different stiffness values which make it highly nonlinear and uncertain resulting in a challenging control problem. To facilitate the development of an effective control strategy, we study the Bolt Tightening process and propose 4 Tightening stages, namely, Bolt-nut alignment, partial and full engagement and final Bolt Tightening. Based on the characteristics of each stage, a fuzzy controller is designed for each stage to realize the respective control objectives. A fuzzy error detector incorporating the knowledge of each stage is proposed for early error detection, making use of the input from a torque and encoder (angular position) sensor. Errors can be detected in each stage to interrupt the process and prevent any damage to the system.
-
Model-free fuzzy Tightening control for Bolt/nut joint connections of wind turbine hubs
Proceedings - IEEE International Conference on Robotics and Automation, 2013Co-Authors: C. Deters, H. A. Wuerdemann, Lakmal D. Seneviratne, H. K. Lam, Emanuele Lindo Secco, Kaspar AlthoeferAbstract:In the wind turbine manufacturing industry, the Bolt-nut joint Tightening process is one of the core processes in the full production chain and concerned with assembling the hub body, the pitch system and the bearing unit. This operation is currently executed manually with the aid of different tools and gauges; the main disadvantages are a relatively high degree of variability and the necessity to repeat this task several times during a production run to achieve a satisfactory, final Tightening torque within a specified angle range. Moreover, the Bolt Tightening process includes various uncertainties such as the presence of friction forces and the use of different Bolt sizes with different stiffness values which make it highly nonlinear and uncertain resulting in a challenging control problem. To facilitate the development of an effective control strategy, we study the Bolt Tightening process and propose 4 Tightening stages, namely, Bolt-nut alignment, partial and full engagement and final Bolt Tightening. Based on the characteristics of each stage, a fuzzy controller is designed for each stage to realize the respective control objectives. A fuzzy error detector incorporating the knowledge of each stage is proposed for early error detection, making use of the input from a torque and encoder (angular position) sensor. Errors can be detected in each stage to interrupt the process and prevent any damage to the system.
