Design and development of force and torque measurement setup for real time monitoring of friction stir welding process

Abstract The aim of the present research work is two folded. First, it is intended to describe the development of a low cost force measurement setup for friction stir welding (FSW) process and second, is to demonstrate the potential of force signals combined with artificial intelligence to accurately predict ultimate tensile strength (UTS) and yield strength of the joints. In FSW process, simultaneous rotation, and translation of the tool associated with severe plastic deformation of material around the tool pin brings challenges in measurement of torque and forces experienced during welding. An attempt has been made to design and develop a strain gauge based setup for the measurement of two dimensional forces and torque during FSW process. The force signals acquired using the developed setup is then utilized to develop support vector regression based data driven models for prediction of UTS and yield strength of the welded samples. For monitoring the FSW process a new statistical indicator derived from force signals is presented. The proposed indicator brings appreciable correlation with the UTS of the joints. The prediction performances of the developed models imply that the presented approach can be effectively put forward for real time monitoring of the process.

[1]  Suleyman Yaldiz,et al.  A dynamometer design for measurement the cutting forces on turning , 2006 .

[2]  Zhongyi Hu,et al.  A PSO and pattern search based memetic algorithm for SVMs parameters optimization , 2013, Neurocomputing.

[3]  Zhifen Zhang,et al.  Real-time seam penetration identification in arc welding based on fusion of sound, voltage and spectrum signals , 2017, J. Intell. Manuf..

[4]  Sedat Karabay,et al.  Analysis of drill dynamometer with octagonal ring type transducers for monitoring of cutting forces in drilling and allied process , 2007 .

[5]  Nashwan Younis,et al.  Averaging effects of a strain gage , 2011 .

[6]  Yi Wang,et al.  Fault diagnosis and prognosis using wavelet packet decomposition, Fourier transform and artificial neural network , 2013, J. Intell. Manuf..

[7]  H. W. Ng,et al.  Strain gage evaluation with four-point bending at moderate temperatures , 1997 .

[8]  Surjya K. Pal,et al.  Tool wear monitoring and selection of optimum cutting conditions with progressive tool wear effect and input uncertainties , 2011, J. Intell. Manuf..

[9]  Garret E. O’Donnell,et al.  Force generation during friction stir welding of AA2024-T3 , 2012 .

[10]  Chuansong Wu,et al.  Simultaneous measurement of tool torque, traverse force and axial force in friction stir welding , 2013 .

[11]  İhsan Korkut,et al.  A dynamometer design and its construction for milling operation , 2003 .

[12]  Rajiv S. Mishra,et al.  Friction Stir Welding and Processing , 2007 .

[13]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[14]  K. Chatterjee,et al.  Monitoring torque and traverse force in friction stir welding from input electrical signatures of driving motors , 2013 .

[15]  Sukhomay Pal,et al.  A combined wavelet packet and Hilbert-Huang transform for defect detection and modelling of weld strength in friction stir welding process , 2016 .

[16]  M. Mehta,et al.  Load bearing capacity of tool pin during friction stir welding , 2012 .

[17]  Ming Jian Zuo,et al.  Crack propagation assessment for spur gears using model-based analysis and simulation , 2012, J. Intell. Manuf..

[18]  Sedat Karabay.  Design criteria for electro-mechanical transducers and arrangement for measurement of strains due to metal cutting forces acting on dynamometers , 2007 .

[19]  Antonino Squillace,et al.  Effect of welding parameters on mechanical and microstructural properties of AA6056 joints produced by Friction Stir Welding , 2006 .

[20]  Sukhomay Pal,et al.  Design and development of fixture and force measuring system for friction stir welding process using strain gauges , 2015 .

[21]  Chao Li,et al.  Machinery condition prediction based on wavelet and support vector machine , 2013, 2013 International Conference on Quality, Reliability, Risk, Maintenance, and Safety Engineering (QR2MSE).

[22]  M. N. James,et al.  Friction stir weld process evaluation by multi-axial transducer , 2008 .

[23]  Somkiat Tangjitsitcharoen,et al.  Advance in chatter detection in ball end milling process by utilizing wavelet transform , 2015, J. Intell. Manuf..

[24]  Abdullah Kurt,et al.  Design and construction of a dynamometer for measurement of cutting forces during machining with linear motion , 2002 .

[25]  Antonino Squillace,et al.  Effect of welding parameters on mechanical and microstructural properties of dissimilar AA6082–AA2024 joints produced by friction stir welding , 2009 .

[26]  Xiaowen Zhang,et al.  Cutting tool operational reliability prediction based on acoustic emission and logistic regression model , 2015, J. Intell. Manuf..

[27]  Suleyman Yaldiz,et al.  Design, development and testing of a four-component milling dynamometer for the measurement of cutting force and torque , 2007 .

[28]  Ramakant A. Gayakwad.  Op-Amps And Linear Integrated Circuits , 2008 .

[29]  Yoshua Bengio,et al.  Random Search for Hyper-Parameter Optimization , 2012, J. Mach. Learn. Res..

[30]  Sunil Pandey,et al.  Effect of process parameters on friction stir welding of aluminum alloy 2219-T87 , 2010 .