A fuzzy logic control system for a friction stir welding process
- Authors: Majara, Khotso Ernest
- Date: 2006
- Subjects: Friction welding , Fuzzy logic , Automatic control , Fuzzy systems
- Language: English
- Type: Thesis , Masters , MTech
- Identifier: vital:9594 , http://hdl.handle.net/10948/405 , Friction welding , Fuzzy logic , Automatic control , Fuzzy systems
- Description: FSW is a welding technique invented and patented by The Welding Institute in 1991. This welding technique utilises the benefits of solid-state welding to materials regarded as difficult to weld by fusion processes. The productivity of the process was not optimised as the real-time dynamics of the material and tool changes were not considered. Furthermore, the process has a plastic weld region where no traditional modelling describing the interaction between the tool and work piece is available. Fuzzy logic technology is one of the artificial intelligent strategies used to improve the control of the dynamics of industrial processes. Fuzzy control was proposed as a viable solution to improve the productivity of the FSW process. The simulations indicated that FLC can use feed rate and welding speed to adaptively regulate the feed force and tool temperature respectively, irrespective of varying tool and material change. The simulations presented fuzzy logic technology to be robust enough to regulate FSW process in the absence of accurate mathematical models.
- Full Text:
- Date Issued: 2006
- Authors: Majara, Khotso Ernest
- Date: 2006
- Subjects: Friction welding , Fuzzy logic , Automatic control , Fuzzy systems
- Language: English
- Type: Thesis , Masters , MTech
- Identifier: vital:9594 , http://hdl.handle.net/10948/405 , Friction welding , Fuzzy logic , Automatic control , Fuzzy systems
- Description: FSW is a welding technique invented and patented by The Welding Institute in 1991. This welding technique utilises the benefits of solid-state welding to materials regarded as difficult to weld by fusion processes. The productivity of the process was not optimised as the real-time dynamics of the material and tool changes were not considered. Furthermore, the process has a plastic weld region where no traditional modelling describing the interaction between the tool and work piece is available. Fuzzy logic technology is one of the artificial intelligent strategies used to improve the control of the dynamics of industrial processes. Fuzzy control was proposed as a viable solution to improve the productivity of the FSW process. The simulations indicated that FLC can use feed rate and welding speed to adaptively regulate the feed force and tool temperature respectively, irrespective of varying tool and material change. The simulations presented fuzzy logic technology to be robust enough to regulate FSW process in the absence of accurate mathematical models.
- Full Text:
- Date Issued: 2006
Monitoring and intelligent control for complex curvature friction stir welding
- Hua, Tao
- Authors: Hua, Tao
- Date: 2006
- Subjects: Friction welding , Fuzzy systems
- Language: English
- Type: Thesis , Doctoral , DTech
- Identifier: vital:9612 , http://hdl.handle.net/10948/420 , Friction welding , Fuzzy systems
- Description: A multi-input multi-output system to implement on-line process monitoring and intelligent control of complex curvature friction stir welding was proposed. An extra rotation axis was added to the existing three translation axes to perform friction stir welding of complex curvature other than straight welding line. A clamping system was designed for locating and holding the workpieces to bear the large force involved in the process between the welding tool and workpieces. Process parameters (feed rate, spindle speed, tilt angle and plunge depth), and process conditions (parent material and curvature), were used as factors for the orthogonal array experiments to collect sensor data of force, torque and tool temperature using multiple sensors and telemetry system. Using statistic analysis of the experimental data, sensitive signal features were selected to train the feed-forward neural networks, which were used for mapping the relationships between process parameters, process conditions and sensor data. A fuzzy controller with initial input/output membership functions and fuzzy rules generated on-line from the trained neural network was applied to perceive process condition changes and make adjustment of process parameters to maintain tool/workpiece contact and energy input. Input/output scaling factors of the fuzzy controller were tuned on-line to improve output response to the amount and trend of control variable deviation from the reference value. Simulation results showed that the presented neuro-fuzzy control scheme has adaptability to process conditions such as parent material and curvature changes, and that the control variables were well regulated. The presented neuro-fuzzy control scheme can be also expected to be applied in other multi-input multi-output machining processes.
- Full Text:
- Date Issued: 2006
- Authors: Hua, Tao
- Date: 2006
- Subjects: Friction welding , Fuzzy systems
- Language: English
- Type: Thesis , Doctoral , DTech
- Identifier: vital:9612 , http://hdl.handle.net/10948/420 , Friction welding , Fuzzy systems
- Description: A multi-input multi-output system to implement on-line process monitoring and intelligent control of complex curvature friction stir welding was proposed. An extra rotation axis was added to the existing three translation axes to perform friction stir welding of complex curvature other than straight welding line. A clamping system was designed for locating and holding the workpieces to bear the large force involved in the process between the welding tool and workpieces. Process parameters (feed rate, spindle speed, tilt angle and plunge depth), and process conditions (parent material and curvature), were used as factors for the orthogonal array experiments to collect sensor data of force, torque and tool temperature using multiple sensors and telemetry system. Using statistic analysis of the experimental data, sensitive signal features were selected to train the feed-forward neural networks, which were used for mapping the relationships between process parameters, process conditions and sensor data. A fuzzy controller with initial input/output membership functions and fuzzy rules generated on-line from the trained neural network was applied to perceive process condition changes and make adjustment of process parameters to maintain tool/workpiece contact and energy input. Input/output scaling factors of the fuzzy controller were tuned on-line to improve output response to the amount and trend of control variable deviation from the reference value. Simulation results showed that the presented neuro-fuzzy control scheme has adaptability to process conditions such as parent material and curvature changes, and that the control variables were well regulated. The presented neuro-fuzzy control scheme can be also expected to be applied in other multi-input multi-output machining processes.
- Full Text:
- Date Issued: 2006
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