# Presentation: 483158

## Integrated Process Modeling of Resistance Spot Welds

***Sudarsanam Suresh Babu***
***Metals & Ceramics Division, Oak Ridge National Laboratory***
***Oak Ridge, TN 37831-6096***
***http://mjndweb.ms.ornl.gov/Babu***
***Co-investigators:***
***M. L. Santella, B. W. Riemer (ORNL)***
***Z. Feng (Engineering Mechanics Corporation of Columbus, Ohio)***

## Designers/Engineers need tools that can relate weldment properties to welding parameters

***How do we achieve that?***
***Empirical correlation or artificial neural network***
***Needs lot of experimental data over wide range of parameters***
***Material specific***
***Fundamental physical models that can describe all the relevant processes that occur during welding***
***Integration of all the physics and metallurgy of the process is necessary***
***General***

**Welding ****Parameters**

**Weld**
**Properties**

**We need this relation**

## Integrated model should describe all physical processes that occur in welding.

***This can be done by coupling individual models for each physical process that occurs during welding.***

**Welding ****Parameters**

**Thermal**
**History**

**Structure-**
**Property**
**Relationship**

**Weld**
**Properties**

**We need this relation**

## Resistance Spot Welding Process Model

## An accurate process model for resistance spot welding must account for complex electrical-mechanical- thermal behavior.

***Critical input data include:***
***Welding parameters***
***Current, force, time***
***Electrical properties***
_***Contact resistance***_*** ***
***Geometry***
***Mechanical & physical properties***
***Temperature dependence***
***Heat transfer boundary conditions***
***Axial & radial conduction, H******2******O***

## Accurate description of electrical contact resistance is critical to describe weld nugget formation in RSW.

***Accurate description of electrical contact resistance is critical to describe weld nugget formation in RSW.***

***Real surfaces are not flat: A******actual****** < A******apparent***
***During welding A******actual****** ************ A******apparent***

***Quantifying the effects of surface roughness is the key to describing electrical contact resistance.***

## Electrical contact resistance model is based on established concepts - Uncoated Sheets

******1,**** ********2*****,****** are bulk resistivities of contacting members (steel)***
******* ******is number density of asperities in actual contact***
**a***** is average radius of contacting asperities***
**2l***** is average in-plane distance between asperities***

**Voggler and Sheppard,  *****Welding Journal*****, 1993**

## Combining asperity parameters and accounting for their mechanical properties results in a ‘new’ RCA relationship.

***How can we obtain the relation for **********?***
***Solution:***
***Measure R******CA******-vs.-P experimentally and fit the measurements to the equation.***

*********1****** and ************2****** = f(Temperature)***

*********YS ******=  f(Temperature, Room Temperature Yield Strength)***

## Experimental Details

***Two steels were investigated. ***
***DQSK (Fe-0.05 C-0.18 Mn, wt.%)***
***DF140 (Fe-0.14 C-1.38 Mn, wt.%)***
***Procedure is based on Thornton et al, Welding Journal, 1996.***
***2-4 mA (ORNL) 1-10 A (EWI) current range.***
***LabView******TM****** was used to track P, I and V across the interface.***
***Loading fixtures were aligned with silicon-nitride anvils.***

## Typical measurement showed decrease in contact resistance with an increase in contact pressure.

***Measurements were made on DF140 steel sheets for fitting purpose.***
***Independent validation of the fitted equation was done with measurements on other samples.***

## Empirical model of RCA fits data very well in pressure range relevant to RSW.

***Except at large contact pressures, the fit of the function with the experimental data was acceptable.***

## Empirical fit lead to a phenomenological variation of contacting asperity density with a ratio of contact pressure to yield strength.

***The fit was then evaluated independently with other experimental  measurements.***

## The model predictions are good for an independent measurement data set from steel sheet.

***This shows the validity of the model for wide range of resistance welding parameter conditions.***

## An accurate process model for resistance spot welding must account for complex electrical-mechanical- thermal behavior.

***An incrementally coupled process model was developed.***

## Typical current variations measured during welding are used.

***First, RMS value is used***
***For detailed simulations, real waveform is used.***

## Measured electrode shapes were used in the simulations.

***The shape of the electrode decides the pressure variations at the interface, which in turn controls the contact resistance.***

**Unconditioned**

**Conditioned**

## Experiments show the difference in weld formation.

## Uncoupled simulations show excessive melting.

***This process assumes constant contact resistance as a function of temperature only.***
***(a) squeeze cycle, ***
***(b) after 3.375 cycles,***
***(c) after 6.125 cycles,***
***(d) after 12 cycles***

## Results from Thermo-Mechanical-Electrical Coupling:

***For flat electrode off-set melting is predicted.***
***A common problem in unconditioned electrodes!***
***(a) 2.125 cycle,***
***(b) 7.875 cycle, ***
***(c) after 12 cycles, and ***
***(d) surface deformations due to coupled analysis***

## Results from coupled model for conditioned electrode:

***Actual current wave form was used.***
***(a) after 2.125 cycles, ***
***(b) after 3.125 cycles,***
***(c) after 12 cycles***
***The results were in agreement with experimental measurements***

## Comparison of predicted weld formation agreed with the measurements for both flat and conditioned electrode.

***The next step is weld microstructure prediction.***

## Steel Weld Microstructure Model

## Microstructure development in a low-carbon steel was characterized.

***DQSK Steel***
***Fe - 0.048 %C - 0.01 % Si - 0.18 % Mn - 0.001 % Ti - 0.003 % Nb (wt.%)***
***Resistance Welding Parameters***
***Welding force = 2224 N***
***Welding Current = 7800 A***
***Number of Cycles = 12***
***Typical Macrostructure:***

## Even with low-carbon concentration, the weld contained bainite and martensite.

***The relative fractions depend on the cooling rate***
***How do we model this microstructure evolution?***

## Several approaches are available to predict carbon steel microstructure

***Carbon equivalence relates the volume fraction of martensite and bainite with time to cool from 800 to 500°C***
***Ion, Ashby and Easterling***
***Very successful but not good for slow cooling rate and low carbon steels***
***Using equations based on TTT/CCT information***
***Work of Kirkaldy et al. 1982, & Watt et al. 1988***
***Not good for low carbon steels***
***Phenomenological models of austenite decomposition to various ferrite phases***
***Bhadeshia et al. 1985***
***Simultaneous transformation kinetics equations***
***Jones and Bhadeshia, 1997***

## TTT/CCT diagram calculations predict the formation of bainite and martensite at cooling rates > 3000 °C/s.

***On-line calculations: http://engm01.ms.ornl.gov***

## Microstructure formation is related to thermal cycle and carbon equivalence: Ion et al, Acta Met, 1984.

***A******C1 ******& A******C3****** were predicted using ThermoCalc software.***
***Microstructure was related to hardness through well known correlation.***

## Hardness gradients were observed near the fusion zone. Cooling rates in the weld metal are expected to be greater than 3000 °C/s.

***We coupled this microstructure model with a simple thermal-microstructure model to validate the feasibility of this approach.***

## Process model provides the means for predicting the spatial distribution of microstructure.

***Node 1***
***Ferrite =   0.01***
***Bainite =   0.02  ***
***Martensite =0.97  ***
***Hardness = 245***
***Node 14***
***Ferrite =   0.00***
***Bainite =   0.01 ***
***Martensite =0.99 ***
***Hardness = 258***
***Node 270***
***Ferrite =   0.50***
***Bainite =   0.00  ***
***Martensite =0.50 ***
***Hardness = 182***

**Steel Composition:**
**Fe - 0.05 wt.% C - 0.1 wt.% Si - 1.0 wt.% Mn**

## Hardness (DPH) gradients were predicted in the weldment.

***Now, how would these welds with property gradients perform in service?***
***Hardness values were converted to yield strength and the structural model was used***

## Other microstructure property models correlation were developed using experimental measurements.

***However, still there is no fundamental model relating the microstructure to property without experimental measurements.***

## Shear test simulations suggest that property gradients degrade structure performance

***Load carrying capability in shear may be diminished by property gradients***
***Important parameter is the geometry of the joint also.***

## Failure modes in resistance spot welds can be done to evaluate the failure mode of spot welds using simple analytical methods.

***This can be tested at http://engm01.ms.ornl.gov.***

## Summary 

***An integrated process model has been developed for resistance spot welding of steels.***
***The model can predict the formation of weld, microstructure evolution and the gradients of microstructure.***
***The model allowed us to evaluate the effect of geometry of weld, process conditions and steel composition on the performance of welded structures.***
***Where do we go from here?***

## Collaborative problem solving environment (PSE) for material process modeling through Internet.

***ORNL has a seed research program on this research.***
***Researchers with different expertise can collaborate on the development of an integrated process model.***