Monitoring of Weld Quality in Friction Stir Welding Based on Spindle Speed and Motor Current Signals

2015 ◽  
pp. 233-253
Author(s):  
Bipul Das ◽  
Sukhomay Pal ◽  
Swarup Bag
Keyword(s):  
Friction Stir Welding ◽  
Spindle Speed ◽  
Weld Quality ◽  
Friction Stir ◽  
Motor Current

Toward Automation of Friction Stir Welding Through Temperature Measurement and Closed-Loop Control

2011 ◽  
Vol 133 (5) ◽  
Author(s):  
Axel Fehrenbacher ◽  
Neil A. Duffie ◽  
Nicola J. Ferrier ◽  
Frank E. Pfefferkorn ◽  
Michael R. Zinn
Keyword(s):  
Control System ◽  
Friction Stir Welding ◽  
Temperature Measurement ◽  
Closed Loop ◽  
Weld Zone ◽  
Spindle Speed ◽  
Weld Quality ◽  
Friction Stir ◽  
Integral Controller ◽  
Zone Temperature

The objectives of this work are to determine an accurate temperature feedback strategy and to develop a closed-loop feedback control system for temperature in friction stir welding (FSW). FSW is a novel joining technology enabling welds with excellent metallurgical and mechanical properties, as well as significant energy consumption and cost savings. However, numerous parameter and condition variations are present in the FSW production environment that can adversely affect weld quality, which has made extensive automation of this process impossible to date. To enable large scale automation while maintaining weld quality, techniques to control the FSW process in the presence of unknown disturbances must be developed. One process variable that must be controlled to maintain uniform weld quality under the inherent workpiece variability (thermal constraints, material properties, geometry, etc.) is the weld zone temperature. Our hypothesis is that the weld zone temperature can be controlled, which can help in controlling the weld quality. A wireless data acquisition system was built to measure temperatures at the tool-workpiece interface. A thermocouple was placed in a through hole right at the interface of tool and workpiece so that the tip is in contact with the workpiece material. This measurement strategy reveals temperature variations within a single rotation of the tool in real time. In order to automate the system, a first order process model with transport delay was experimentally developed that captures the physics between spindle speed and measured interface temperature. The model has a time constant of 110 ms and a delay time of 85 ms. Using this temperature measurement technique, a closed-loop temperature control system with a bandwidth of 0.3 Hz was developed. Interface temperatures in the range from 555 °C to 575 °C were commanded to an integral controller, which regulated the spindle speed between 850 rpm and 1250 rpm to adjust the heat generation and achieve the desired interface temperatures in 6061-T6 aluminum. To simulate changes in thermal boundary conditions, backing plates of different thermal diffusivities were found to effectively alter the heat flow, hence, weld zone temperature. The integral controller that manipulates spindle speed is applied when welding during these intentionally introduced weld disturbances. The measured temperature stayed within ±5 °C after introducing the disturbance, compared to a 50 °C change in temperature when no control was applied.

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

Superplastic Behavior and Microstructure of Titanium (Ti-6Al-4V) Friction Stir Welds Made under a Variety of Processing Conditions

2010 ◽  
Vol 433 ◽  
pp. 169-176 ◽  
Author(s):  
Paul Edwards ◽  
Mamidala Ramulu ◽  
Daniel G. Sanders
Keyword(s):  
Friction Stir Welding ◽  
Base Material ◽  
Superplastic Forming ◽  
Spindle Speed ◽  
Processing Conditions ◽  
Superplastic Behavior ◽  
Friction Stir ◽  
Fine Grained ◽  
Weld Parameters

Friction Stir Welding of Ti-6Al-4V was performed on 5 mm thickness plate in order to assess the affect of welding conditions on the resulting microstructure and superplastic forming behavior of the joints. A variety of welding conditions were tested and all welds were subsequently Superplastically formed. It was found that the weld parameters do influence the microstructure and degree of superplastic performance of the joints. Spindle speed was found to have the most dominant affect on the resulting microstructure and superplastic forming behavior. Low spindle speed welds lead to fine grained microstructures and highly superplastic welds, relative to the base material, while high spindle speed welds larger grained microstructures and less superplastic welds.

scholarly journals Backing plate effect on temperature controlled FSW process

2018 ◽  
Vol 224 ◽  
pp. 01084 ◽  
Author(s):  
Igor Zybin ◽  
Konstantin Trukhanov ◽  
Andrey Tsarkov ◽  
Sergey Kheylo
Keyword(s):  
Boundary Condition ◽  
Friction Stir Welding ◽  
Welding Speed ◽  
Rotational Speed ◽  
Thermal Boundary Condition ◽  
Weld Quality ◽  
Plate Material ◽  
Friction Stir ◽  
Backing Plate ◽  
Back Plate

Friction stir welding (FSW) has become an important application in modern industries. Friction stir welding is a widely used solid state joining process for soft materials such as aluminum alloys as it avoids/minimizes common problems of fusion welding processes, i.e. distortion, porosity, solidification and liquation cracking etc. Improper selection of parameters such as welding speed, rotational speed, forge force, back plate material etc. affects the weld quality. Thermal boundary condition at the bottom of the work pieces to be joined is important in determining the result of weld quality and its properties, for a given alloy type, tool geometry and selected process parameters (welding speed, rotational speed etc), These thermal boundary conditions are governed by the back plate material used. By using backing plates made out of materials with widely varying thermal diffusivity this work seeks to elucidate the effects of the root side thermal boundary condition on weld process variables and resulting joint properties. Welds were made in 5-mm-thick AMг5 (AA 5056) using siliceous coating, stainless steel, mild steel, and aluminum as backing plate (BP) material. Effects of backing plate material on the tensile strength and elongation were obtained for a particular case.

Effect of friction stir welding tool on temperature, applied forces and weld quality

2015 ◽  
Author(s):  
Hossein Papahn ◽  
Pouya Bahemmat ◽  
Mohammad Haghpanahi ◽  
Iman Pour Aminaie
Keyword(s):  
Friction Stir Welding ◽  
Weld Quality ◽  
Friction Stir ◽  
Applied Forces
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