Localized Fracture vs. Generalized Fracture in Auto Stamping

Localized Fracture vs. Generalized Fracture in Auto Stamping

To understand the difference between localized and global fractures, you must first understand strain gradients (see the article in our blog, AHSS Strain Hardening and Gradients). Gradients can result in highly concentrated strains (peak strain condition) that typically occurs in an embossment or character line where the deformation mode is in plane strain. Peak strains can develop rapidly in a very localized area (Figure 1). Under additional loads, this can result in the onset of localized necking, which means the material has reached its tensile strength and will fail at its weakest point or highest strain. When a slight increase in strain is applied, the material will fracture, sometimes at deformation levels less than predicted. This condition can be found in AHSS products, where multiple phases exist within the steel’s microstructure, each with different properties. A global fracture also typically occurs in plane strain, but more commonly down a sidewall or other area with more moderate geometry complexity.

Figure 1: Peak strain in the localized area or embossment

Figure 1: Peak strain in the localized area or embossment

Peak (concentrated) strains are susceptible to localized fractures when even slight variation exists in the forming process. Examples of variation include lubrication pattern and volume, die recipe including blank position, press conditions, and material characteristics.

A localized neck and/or fracture (Figure 2) reduces the sheet metal’s thickness, reducing part strength, and compromising functional performance such as fatigue life, crash worthiness, and stamping stiffness. There are a number of formability analysis tools that can differentiate localized and global fractures and enable die makers to implement die and process improvements that minimize fracture susceptibility. The result is a more robust stamping process.

Figure 2: Schematic of Localized Necking and Fracture

Figure 2: Schematic of Localized Necking and Fracture

Process control is critical; die recipe discipline is needed to minimize tinkering with die recipe, press settings, and lubrication settings. Mechanical properties of the sheet metal should be tracked to identify trends or variations in the material, and establish the material forming window. Typical mechanical properties that are available from the steel supplier are yield strength, tensile strength, n-value, total and uniformed elongation, and sheet thickness. Additional properties that should be determined include hole expansion and deep cup draw ratios. Failure to identify strain levels, process variables and variation will lead to a reactionary approach to controlling the output. This will lead to an increase in scrap, die-related downtime, and of course, costs.

 

Contributions made by Phoenix Group.

AI Expulsion Prediction In Resistance Spot Welding Of Advanced High-Strength Steels

AI Expulsion Prediction In Resistance Spot Welding Of Advanced High-Strength Steels

Resistance spot welding (RSW) is the most utilized joining process in car body assembly with exceptionally high demands on quality and reproducibility. Expulsion in RSW leads to ejection of metal from the weld and can cause equipment deterioration and re-working.

As the RSW process has many variables both from the process itself and from the to-be-welded component, prediction and avoidance of expulsion is challenging. This creates a large demand for new technologies that allow for RSW of steels with improved expulsion control. 

Recent developments aim to predict – and ultimately avoid – expulsion using artificial intelligence data analysis. Process data on current, voltage and dynamic resistance are readily available in RSW and can be augmented with simulation data for non-measurable phenomena such as the nugget growth rate to create predictive algorithms that eventually aim to avoid expulsion altogether. 

 

An Introduction to AI Expulsion Prediction

This work focuses on utilizing artificial intelligence modeling for the prediction of expulsion during resistance spot welding (see Figure 1 for an image of expulsion on the left and metal residue on the surface after expulsion on the right).  The primary objective is to forecast the formation of expulsion before it occurs, with the aim of improving the quality of the welding process. This effort is supported by a dataset of 500 welded spots of 2-sheet stack-ups made from one advanced high-strength steel (AHSS) and one mild steel joining partner. Two sequential AI models are trained: one for nugget growth prediction and another for spatter prediction.

 

Figure 1: Left: Expulsion leads to the ejection of molten metal. Right: Residues may form on the surface after expulsion.

 

Feature Engineering

A key aspect is the integration of multi-source data: The models leverage both static and dynamic inputs. Static inputs include input process parameters such as current, force, and weld time, which are set at the machine in accordance with a pre-determined weld lobe.  Dynamic inputs encompass transient process signals like dynamic resistance and electrode force. In resistance spot welding, this data is usually readily available, because the welding power source measures electric flow and weld gun opening and forces. A further step is to include non-measurable data from simulation results concerning nugget growth rate. 

To give time for process intervention, the predictive quality of the models was determined after only 30 ms of welding time, depicted in Figure 2.

A chart of dymanic resistance measurements

                                             Figure 2: Only 30 ms of dynamic resistance measurements are used as input for the AI models.

 

Data evaluation and feature engineering are critical components of the modeling process. A welding and data-science expert needs to identify significant features from sensor time series data as input for the neural network AI models. These features can be physically meaningful, such as the minimum resistance during the process, or purely statistical with values such as longest continuous time above the average resistance.

The simulation of resistance spot welding allows for the extraction of the non-measurable nugget growth rate. It is well documented that the nugget growth rate strongly correlates with expulsion formation and conducting simulations for different welding cases (gaps, misalignments, slightly changed contact resistances…) can add this physical behavior to the data-driven AI model. Figure 3 depicts different nugget growths extracted from the simulation with different welding currents. High currents lead to significantly faster growth rates and facilitate expulsion.

Weld nugget growth behavior for different welding currents extracted from a numerical welding simulation. Solid lines are simulation results; dashed lines depict a polynomial fit used to reduce data for AI input.

Figure 3: Weld nugget growth behavior for different welding currents extracted from a numerical welding simulation. Solid lines are simulation results; dashed lines depict a polynomial fit used to reduce data for AI input.

 

Model Accuracy

Figure 4 depicts the confusion matrices for the three different models. The bottom left and top right fields are the desired predictions, and the other fields depict false results of the models. The first model stage, which relies solely on static inputs, yields moderate prediction quality. However, when dynamic data from experiments and simulations are included, the prediction quality jumps to approximately 96%. The analysis focusing on the first 30 milliseconds to give time for subsequent intervention in the process, achieves a reduced, but still good, result quality of around 90%. Further improvements in prediction accuracy are expected with additional data.

Confusion matrices for static, static and dynamic and static and dynamic: only 30 ms inputs. A reliable prediction is possible using dynamic process and simulation data. If only 30 ms of dynamic data are utilized the predictive quality decreases.


Figure 4: Confusion matrices for static, static and dynamic and static and dynamic: only 30 ms inputs. A reliable prediction is possible using dynamic process and simulation data. If only 30 ms of dynamic data are utilized the predictive quality decreases.

 

The presented approach uses a data-driven AI model with different levels of input data to predict resistance spot welding expulsion. The data-gathering and feature engineering procedures are highlighted, explaining a need for in-depth knowledge of the welding process as well as data engineering. The approach yields a neural network with excellent predictive quality, if dynamic data is included in the model. Even if only 30 ms of transient data are used to allow for a subsequent process intervention, the result quality is still good. It is expected that the predictive quality improves when the data set is increased and additional data sources both from measurements and simulations are included. This approach can improve the control of automotive welding systems and ultimately avoid excessive re-working and equipment wear due to expulsion in resistance spot welding of advanced high-strength steels.

 

Thanks go to Dr.-Ing Max Biegler, AHSS Application Guidelines Technical Editor and Group Lead, Joining & Coating Technology at Fraunhofer Institute for Production Systems and Design Technology IPK
Digitalization In Tailor Welded Blanking: Combining Numerical Simulations To Process Increasing Material Strengths

Digitalization In Tailor Welded Blanking: Combining Numerical Simulations To Process Increasing Material Strengths

Efficient energy and resource use in automotive engineering is a major challenge that can only be overcome with innovative solutions. A cost-effective and resource-saving approach is the use of digital methods in production.

 

 

In demanding production chains like body-in-white components from tailor-welded blanks (TWBs), it is crucial to digitally simulate the product before actual production to prevent tool adjustments and unnecessary trials. A new digital twin for the tailor welded blanking process chain links numerical simulations for welding and forming steps. Figure 1 shows a typical application for a tailor welded blank component in the body in white: front longitudinal member.

 

Photo of Front Longitudinal

Figure 1: A typical application for TWBs in the automotive industry is the longitudinal member shown here at the front. The TWB shown consists of micro-alloyed CR300 LA (2.3 mm) and high-strength dual phase steel with 600 MPa (2.5 mm). The welded sheet metal blanks were deep-drawn into the final shape of the TWB.

 

The process chain of laser beam welding and deep drawing faces challenges when pushing to stronger advanced high-strength steels. Areas around the weld seam are susceptible to softening due to heat input during welding, leading to changes in material properties, such as decreased strength in the heat-affected zone (HAZ) and hardening of the weld metal, influencing forming limit behavior. 

Adapted welding process control can optimize material properties, ensuring laser beam welding’s applicability for AHSS grades. Consistent digital simulation of manufacturing processes is one of the most promising approaches for enabling low-emission, lightweight construction and maximizing material efficiency. To replace material-intensive experiments, simulations using the finite element analysis (FEA) create a virtual 3D model of a component. 

Welding structure simulation validation is performed using thermocouple measurements and metallographic sections. The core of the forming simulation consists of material cards for high-strength steels. Yield curves, stress-strain curves, and forming limit diagrams are incorporated into the simulation. Validation experiments complete the forming simulation setup, comparing the deep-drawing press force-displacement curve with the modeled curve. The deep-drawn component is then digitized using a 3D scanner, allowing comparison of the real component with the simulated one in terms of geometry, defects, and sheet thickness. Figure 2 shows a simulated S-rail as demonstrator component after deep-drawing simulation.

 

 

Simulation Result Image

Figure 2: Simulation result of an S-rail formed from TWBs for the probability of failure (max. failure). Both the weld seam and the heat-affected zone are greatly oversized in the illustration for better visibility. The base materials are two high-strength steels of different sheet thicknesses, which were welded together using a butt joint. The heat-affected zone is represented as two areas with different properties.

 

Today’s forming simulation tools cannot readily account for the small geometric areas of weld metal and heat affected zones, hence the welding simulation results cannot be directly used as input for another software. In addition, it is difficult to measure material behavior of the welding zones to correctly model them in forming simulations. New interfaces were developed for a digital data management platform to bridge this gap. Intermediate steps are required to transfer welding results to forming simulation, including determining the heat-affected zone and deriving weld seam geometry. The analysis chain of the digital twin involves extracting welding simulation results, creating input for forming simulation and adjusting welding simulations based on forming results in an iterative loop. Figure 3 illustrates the digital process chain.

Imagery of Digital and Traditional Processes for Comparison

Figure 3: Scheme of the digital (top) and conventional (bottom) process chain of tailor welded blanking: main process steps from material to final product.

 

Linking welding and forming simulations (Figure 4) enable TWBs made of advanced high-strength steels reach higher strength levels, ultimately saving resources in car body construction and making production more sustainable. The development of TWBs for automotive construction serves as a starting point for expanding lightweight construction potential across the transport sector. 

Welding Simulation Sample Images

Figure 4: Bidirectional digital twin: How adapting welding simulation closes the loop. The welding simulation displays its results using three-dimensional volume elements. With the help of the digital platform, a simplified two-dimensional representation is generated that contains information about the position of the weld seam and heat-affected zone, which can then be modelled in shell elements. This data is used for the forming simulation and the result is fed back into the digital platform. The parameters of the best simulation results are highlighted and assist in the planning of new welding simulations.

 

Photo of Josefine Lemke

A special thank you to our author, Josefine Lemke, M. Sc. She is a research associate at Fraunhofer IPK in Berlin, Department Joining and Coating Technology. She specialized in additive manufacturing and welding simulation. The focus is on the correlation of component quality and powder properties, particularly in the context of industry and SME environments. She is also working on the qualification of ultra-high-strength steels in car body construction (integration of laser welding and forming simulation in tailor-welded blanking).

 

WorldAutoSteel Releases Latest AHSS Application Guidelines

WorldAutoSteel Releases Latest AHSS Application Guidelines

Smarter Engineering for Tomorrow’s Vehicles

In the fast-evolving world of automotive engineering, the ability to innovate safely and efficiently is paramount. Advanced High-Strength Steels (AHSS) have become the backbone of modern vehicle design. In less than a decade, the AHSS portfolio has nearly doubled—from 38 commercially available grades in 2017 to nearly 70 today—giving automakers unprecedented flexibility to design lighter, stronger, and more sustainable vehicles.

WorldAutoSteel’s newly updated 2025 AHSS Application Guidelines give engineers and designers the knowledge and process confidence they need to master these advanced steel grades and apply them with precision.

 

What’s New in the AHSS Application Guidelines?

The updated resource reflects a global shift toward smarter, data-driven engineering, offering practical insights into metallurgy, forming, and joining techniques that optimize performance while reducing mass, risk, and production time. Key highlights include:

Smarter materials: AHSS grades and coatings are engineered, characterised, and applied as systems. Chemistry, microstructure, coating, forming route, and joining method are designed together to deliver exactly the performance needed with less mass, lower risk, and faster industrialisation.

Smarter forming: Expanded guidance on simulation and testing support an increasingly digital, “first-time-right” culture in body-in-white engineering, where forming processes are validated virtually before any physical tooling is committed, to reduce costly trial-and-error on the shop floor. New techniques and testing explanations help engineers understand the failure mechanisms that may limit the application of advanced grades.

Smarter joining: New recommendations on techniques such as paint-bake hardening, crash-rate testing, and MIG brazing echo a more data-driven, coatings-aware, and crash-validated approach to joining, ensuring each joint delivers the required strength, ductility, and durability with minimal mass, heat, and risk. For example, new methods for assessing spot weld strength and understanding the effects of bake hardening help manufacturers optimise lightweighting and crash performance. The expanded section on arc welding offers quick, practical tools for engineers, especially those new to AHSS, to avoid common mistakes and ensure robust joints.

The Guidelines also address the unique challenges posed by the shift to electric vehicles (EVs), such as protecting batteries while maintaining lightweight structures and stiffness.

Press hardening steels (PHS) are highlighted as a key tool in automotive body structure design, with new applications and strategies for tailored parts that combine high and low strength in a single stamping.

By curating the latest evidence and technical best practice for AHSS into a single, accessible resource, WorldAutoSteel is supporting the industry’s drive for continuous improvement. As new challenges and opportunities emerge, the Guidelines put lighter structures, outstanding crash performance, faster industrialisation, and lower lifecycle emissions well within reach.

Access the latest AHSS Application Guidelines

Join technical editors Dr.-Ing. Max Biegler and Dr. Danny Schaeffler for a free steelTalk webinar on 2 December, offering an overview of the latest updates and best practices. Register here.

Component Scale Avoidance of LME

Component Scale Avoidance of LME

This article explores the challenges of liquid metal embrittlement (LME) in resistance spot welding (RSW) of automotive components, particularly focusing on a component-scale S-Rail made from advanced high-strength steel (AHSS). The study aims to identify the occurrence of LME during the welding process and to propose effective strategies for its mitigation. This article is an excerpt from the “LME component study” conducted by WorldAutoSteel. The full study can be downloaded here.

Experimental and Simulative Setup

The experiments utilized an electrogalvanized RA1180 AHSS joined to hot-dip galvanized mild steel. Two stack-up configurations were tested: similar (both sheets made of RA1180) and dissimilar (RA1180 on top of mild steel). The resistance spot welding process was monitored using sensors to record current, voltage, and force. Different welding parameters, such as hold time and electrode geometry, were varied to observe their effects on LME.

Figure 1: Top-view of the S-Rail component during welding. The clamping points S1-S4 as well as the welding points F1-F10 are highlighted.

A simulation-based risk criterion for LME was established based on local stresses in the components. Both experimental and numerical analyses were conducted to assess the influence of various parameters on LME formation. Specifically, the study evaluated how springback, a phenomenon occurring during deep drawing, affects LME risk. Correct clamping can effectively suppress springback, consequently reducing LME occurrences.

Figure 2: Experimentally observed cracks with 5° tilted electrodes and doubled welding time of 760 ms.

Findings

  1. Influence of Springback: Springback contributed to LME formation. When clamping was employed to counteract springback, LME was effectively eliminated from the welded samples.
  2. Electrode Geometry and Hold Time: Adjustments to the electrode geometry and increasing hold time after welding further mitigated LME risks. Specifically, larger electrode tip diameters and longer hold times reduced the likelihood of cracks.
  3. Material Stack-up Effects: The experiments indicated that the configuration of the material stack-up influenced LME occurrences. Only stack-ups with thick joining partners showed occurrence of LME in the trials.

Figure 3: All 10 resistance spot welds on the S-rail are crack-free after optimizing either springback, electrode working plane diameter or post-weld hold time

Simulation Results

Finite element simulations were used to evaluate the risk of LME by analyzing local stresses and temperature distributions during welding. The results showed that the springback-affected samples presented a higher LME risk compared to idealized, straightened models. This finding aligns with experimental observations that cracks occurred where excessive springback influenced the welding process. Even in the case of springback, LME could be effectively prevented by using electrode caps with larger working planes as well as slightly extending the hold time after welding.

The developed simulation approach allows comparing the LME conditions for different welding setups and can therefore optimize the LME occurrence for geometry, material and welding conditions.

Conclusion

Effective mitigation strategies, such as clamping to suppress springback and adjustments in welding parameters, can prevent LME on a component-scale. It can also be highlighted that today’s AHSS grades are far less sensitive to LME by-default so that few RSW joints in a whole body-in-white are at all susceptible for cracking: To produce cracks for this study, welding parameters with increased energy input had to be used; no LME was observed under “standard” industrial conditions.