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.

Case Studies in AHSS Alloy Selection

Case Studies in AHSS Alloy Selection

How Advanced High-Strength Steels Solve Cold Stamping Challenges in Automotive Body Structures

 

Introduction

In the automotive industry, manufacturing complex, high-strength deep-drawn parts using cold forming processes can create new challenges not previously encountered with body structures made from historically available conventional steels.

Compared with even a decade ago, automotive engineers today have significantly more steel grades to choose from in their quest to balance properties, performance, manufacturability, sustainability, and cost. The use of modern, 3rd-generation Advanced High-Strength Steel (AHSS) grades, for example, offers a combination of high strength and ductility that presents manufacturers with an alternative to Press Hardening Steels (PHS).

 

 

Advanced High Strength Steels

  • Conventional Dual Phase SteelsFeature a microstructure of ferrite and martensite, offering excellent formability in the drawing and stretching deformation modes. However, the characteristics of this phase combination that work very well in these deformation modes lead to challenges in bending and edge-stretch deformation.
  • Complex Phase SteelsOffer superior performance in bending and edge-stretch deformation due to their more homogenous microstructure and reduced hardness gradients, but do not match comparable-strength dual phase steels in drawing and stretching performance.
  • 3rd Generation Advanced High-Strength SteelsA family of grades engineered to combine the best features of dual phase and complex phase steels while minimizing their respective limitations — making them a compelling option for challenging automotive stampings.

Industrial applications of advanced-grade sheet metal for vehicle bodies can require different techniques and approaches than those used to successfully form parts from high-strength low-alloy (HSLA) steels.

 

Manufacturing Challenges for Cold-Stamped B-Pillars

B-Pillars — also called center pillars — are among the most challenging components for cold stamping applications in modern vehicle design. Current IIHS side impact crash requirements, which simulate a collision with an SUV, are particularly stringent and pose a dual engineering challenge:

  • The upper section must be of sufficiently high strength to prevent cabin intrusion during a side impact.
  • The lower section must maintain at least moderate ductility to absorb crash energy.

One approach to increase pillar stiffness is to realize a deeper draw depth and more complex shape, yet the necessary formability to achieve these are typically limited by the high strength requirements for crash performance.

The door opening regions require flanges to facilitate the joining of outer and inner shell components. Manufacturing cycle times and cost sensitivity dictate that blank edges are typically formed by mechanical shearing rather than laser cutting—a process that generally reduces edge quality. Forming the targeted part shape places these shear-cut edges in tension, exposing higher-strength steels to the risk of edge cracking.

The combination of these challenges often results in auto manufacturers forming B-Pillars and even entire door rings by hot stamping PHS. This manufacturing method enables complex-shaped sheet designs that meet stringent crash requirements while minimizing the splitting problems or formability limitations complicating production.

Given the global commercial availability of 3rd Generation AHSS, cold stamping approaches can again be considered for the production of crashworthy B-Pillar components.

 

Case Studies: Cold-Stamped B-Pillars Using AHSS

 
B-Pillar Upper — Solving Edge Cracking with High Hole Expansion Steel

When forming shear-cut blanks into B-pillar upper shapes, metal flow places cut edges into tension along the highly arched front- and rear-door opening regions adjacent to the B-pillar. Edge cracking propagating into the part — as seen in Figure 1A — is a frequent outcome.

Adding to this complexity is that forming simulations have difficulty predicting the failure risk of shear-cut edges. A contour plot of a forming simulation (Figure 1B) may give a false impression of no cracking or splitting risk in this critical area.

Blank design countermeasures alone can only accomplish so much. However, the steel industry now offers AHSS options at the same tensile strength—with up to twice the cut-edge ductility as conventional dual-phase steel. This can be verified using the hole expansion test. The same part and process design with the high hole expansion steel is capable of achieving the targeted part dimensions and characteristics as shown in Figure 1C.

 

Figure 1: B-Pillar Upper stamped from 590 GA (a conventional galvannealed steel with 590 MPa minimum tensile strength, shown in A) and HHE 590 GA (a steel with 590 MPa minimum tensile strength engineered for high hole expansion, shown in C). The simulation of the conventional 590 GA does not indicate forming issues, as shown in image B.  [Citations J-30, S-125]

 

B-Pillar Lower — Overcoming Split Risk with 3rd Generation AHSS

In B-Pillars, controlled structural deformation to targeted loading levels is required to absorb the kinetic energy of an impacting body. OEMs typically design B-Pillar Lowers (Figure 2A) to absorb side impact crash energy, while the upper section counteracts load on the body structure to minimize intrusion into the passenger cell.

The lower horizontal area of the B-Pillar supports the rocker assembly, requiring a relatively deep draw depth to follow the contours of adjacent components and create a solid hinge connection for B-Pillar kinematics. From a formability perspective, these deep draws and complex geometries put stampings at significant risk of splits due to insufficient elongation and n-value. An example of a split in this area is shown in Figure 2B.

After switching to a 3rd Generation AHSS grade of equivalent tensile strength—but with greater elongation and n-value—the same deep-draw part design can be stamped without splits (Figure 2C).

 

 

 

Figure 2: B-Pillar Lower (shown in A) splits when made from a 1st Generation AHSS grade with 980 MPa minimum tensile strength (shown in B), yet remains split-free when made from 3rd Generation AHSS of the same tensile strength (shown in C). [Citations J-30, S-125]

 

3rd Generation AHSS in Automotive Series Production

Despite the dominance of PHS in B-Pillar and door ring construction, 3rd Generation AHSS solutions are a viable alternative and have been deployed in production vehicles. Recent model year examples include:

2023 Suzuki Fronx: B-Pillar [C-49, S-131]

2023 Chevrolet Blazer EV: B-Pillar [E-14]

2022 Nissan Ariya: B-Pillar [N-35, N-36]

2022 Toyota bZ4X/Subaru Solterra: Lower B-Pillar. [I-28, S-132]

Third Generation AHSS are also found in other parts which have rigorous mechanical load requirements and high drawing ratios, such as front longitudinal beams and seat cross members.. These applications compete with PHS but with a much greater spread in series production than in B-Pillars.

 

Explore More AHSS Alloy Selection Guidance

For additional case studies on other automotive parts and detailed guidance on aligning your steel grade selection to the needs of the part, visit: Case Studies in Alloy Selection

Danny Schaeffler is the Metallurgy and Forming Technical Editor of the AHSS Applications Guidelines available from WorldAutoSteel. He is founder and President of Engineering Quality Solutions (EQS). Danny wrote the monthly “Science of Forming” and “Metal Matters” column for Metalforming Magazine, and provides seminars on sheet metal formability for Auto/Steel Partnership and the Precision Metalforming Association. He has written for Stamping Journal and The Fabricator, and has lectured at FabTech. Danny is passionate about training new and experienced employees at manufacturing companies about how sheet metal properties impact their forming success.

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.