Mechanical shearing introduces a material limit lower than the onset of local necking. This new limit is critical for understanding and predicting material performance in industrial forming applications. The sheared edge condition, influenced by strain hardening and microstructural damage, reduces ductility in the shear-affected zone, posing challenges for reliable characterization and numerical prediction. This study presents an approach that integrates finite element modelling (FEM) with geometric shape matching to establish a practical, simulation-compatible material limit for sheared-edge formability. Instead of relying on localized strain measurements, the methodology determines the punch displacement in FEM simulations that best replicates the experimentally observed deformed geometry of Hole Expansion Capacity (HEC) test specimens, along with the corresponding maximum deformation of elements at the stretched edges. The method proves effective for industrial forming processes, enhancing FEM-based edge failure prediction and providing a valuable tool for process optimization and defect troubleshooting in sheet metal forming. 

Accurate monitoring of draw-in behaviour during sheet metal forming is crucial for understanding material flow, optimizing process parameters, and validating finite element (FE) simulations. This study presents an integrated approach combining high-resolution optical measurement, laser displacement sensors, and numerical simulations to analyse draw-in variations during the first forming operation of an automotive front door inner panel. A dedicated optical system was employed to capture sequential images of the blank edge, which were calibrated and processed using computer vision techniques to extract precise draw-in values at predefined locations. The results demonstrate that optical monitoring provides reliable insights related to the sheet metal forming process, highlighting the influence of real-world process disturbances. Furthermore, the study explores the feasibility of integrating measured draw-in data into an adaptive control framework, applying artificial intelligence techniques to refine process stability. By utilizing experimental data alongside numerical predictions, this methodology enhances process understanding and enables data-driven decision-making in industrial sheet metal forming. The findings contribute to the development of intelligent forming control strategies, bridging the gap between modelling and real-world manufacturing conditions to improve product quality and production efficiency.

Reducing the CO2 footprint has become a key objective in the manufacturing sector, with the automotive industry being no exception. A significant portion of a car body consists of stamped components, making the reduction of CO2 emissions in stamping lines a critical focus. One major contributor to emissions is the high material usage, partially derived from the scrap generation. Additionally, production ramp-ups in critical components, such as side door-inners and wheel housings, often lead to increased defect rates, further exacerbating waste. This work investigates the influence of stamping tool temperature increases during production and its consideration in the early stages of process design. Using a Volvo side door inner as an industrial benchmark, various numerical solutions were explored using AutoForm software. The technical note presents the advantages, limitations, and challenges of these approaches, while highlighting the potential of numerical tools to drive CO2 footprint reduction in stamping processes from an early design stage.

With the increased focus on smart manufacturing and Industry 4.0, the use of simulations for the creation of cyber-physical manufacturing systems is increasing. The sheet metal forming manufacturing process, commonly used for production of automotive body components, is one of the processes that currently benefits from the use of simulations without exploiting them in a cyber-physical system setup. This study set out to initially identify the key controllable and uncontrollable parameters of the sheet metal forming manufacturing process for the design of an intelligent quality controller. Subsequently, the study investigates the possibility of using data points from a stochastic numerical analysis as training data for an Artificial Neural Network. The stochastic numerical model used is based on the existing Finite Element simulation standard at Volvo Cars to allow for a seamless integration of the methodology into the standard workflow of CAE departments. Lastly, the study will present a validation of the trained Artificial Neural Network using the Volvo XC90 inner front door component as an industrial demonstrator.

This study explores the estimation of process work in the sheet metal forming process by numerically integrating the punch force curve as a function of the press crank angle. Two numerical integration methods, the trapezoidal rule and Simpson’s 3/8 rule, are evaluated for their ability to estimate process work. While both methods yielded similar results, the Simpson 3/8 rule was found to produce significantly lower estimation errors. The method was then tested in an industrial case study involving the production of Volvo XC90 front door inner components. By analyzing the process work for each blank, it was found that the method effectively captured changes in applied cushion force and material coil. A further analysis, incorporating average lubrication data, showed that the process work also accurately reflected variations in lubrication conditions. The results suggest that process work could serve as a cost-effective and efficient tool for in-line monitoring of process health and has the potential to improve process monitoring and quality control in industrial sheet metal forming operations.

The automotive industry is currently facing increasing sustainability demands in order to reduce the environmental impact of their businesses and products. As a part of these demands, reduced amount of scrapped parts in current production is favourable since it contributes to both an increased productivity as well as improved environmental sustainability. Furthermore, in the near future, more sustainable sheet metals will be introduced in the production which could have a larger variation in properties which could increase the number of scrapped parts. These new demands and sheet materials have been the starting point for the study presented in this paper. It is based on results from a Volvo Cars stamping plant for a part in production that has experienced production disturbances. The information from the press shop stated which combinations of sheet metal coatings and lubricants that gave a robust production and which combinations that generated an unacceptable number of scrapped parts. These different tribological systems have then been simulated using the AutoForm R12 Sigma software with TriboForm models of the used tribological systems in the press shop. The simulations are also using the Cold Forming with Temperature Effects functionality in AutoForm R12 which makes it possible to also include the effects of temperature increase in the stamping die during the production of the part.

Global manufacturing is entering an era of unprecedented variability in material properties, driven by sustainability goals and market volatility. The adoption of recycled steels introduces supplier-specific differences, while cost-reduction strategies and geopolitical disruptions (tariffs, trade barriers, and resource shortages) further amplify process scatter. These dynamics challenge conventional quality control in automotive sheet metal forming and demand intelligent, adaptable production systems.

This dissertation addresses how data-driven methods can strengthen process robustness without major infrastructure changes. Three guiding hypotheses are explored: (H1) machine learning can provide insights into the impact of input variations; (H2) synthetic data can supplement or replace operational data for model development; and (H3) existing sensor signals can be reinterpreted to reduce reliance on additional instrumentation.

A hybrid methodology combining finite element simulations, stochastic modeling, and industrial press shop data was developed. Key contributions include: (1) generation of synthetic datasets for predictive modeling of draw-in and cushion force; (2) application of unsupervised learning for early detection of anomalous material batches; and (3) a novel process monitoring metric, process work, derived from existing sensors to monitor process health.

The findings provide a framework for integrating intelligent data-driven tools into legacy systems, supporting the transition toward resilient and sustainable manufacturing practices.

The hole expansion ratio (HER) observed in a standardized hole expansion test (HET) is commonly used to determine the edge fracture of steel sheets. A large variation of the measured HER restricts the practical application of the method. This study presents a systematic investigation on uncertainties in the HER of DP800 sheet material, including the hole-edge quality, pre-strain due to the hole-punching process, the friction coefficient, and the determination of fracture. An artificial neural network was trained to develop a surrogate model using a database gained from a thousand finite element simulations of the HET. Monte-Carlo simulations were performed using the trained surrogate model to characterize the distribution of the HER. Sensitivity analysis via Sobol's indices is calculated to determine the influence of the input variables on the output. It is found that the pre-strain and pre-damage generated during the hole punching process in the shear-affected zone dominate the variation of the HER. Discussions on reducing the output's variation are detailed. In general, these findings provide valuable insights for the determination of HER as well as the edge crack behavior of the investigated DP800 steel sheet.

Within the automotive industry, there is an increasing demand for a paradigmshift in terms of which materials are used for the manufacturing of the automotive body. Globalclimate goals are forcing a rapid adaption of new, advanced, sustainable material grades suchas the fossil free steels and materials containing higher scrap content. With the introduction ofthese new and untested materials, methods for accounting for variation in material propertiesare needed directly in the press lines.The following study will focus on creating an initial virtual shadow of the manufacturing of aVolvo XC90 inner door panel through the application of Artificial Neural Networks (ANN). Thevirtual shadow differs from the concept of the digital twin by only being a virtual representationof the production line, with training data generated exclusively by numerical simulations, andhaving no automated communication with the physical press line control system. The virtualshadow can be used as an assistance to the press line operators to see how different press linesettings and material parameter variations will impact the quality of the stamped component.The study aims to validate the virtual shadow through accurate predictions of the materialdraw-in measured in the physical press line.

During the running production of automotive body components drifts in theprocess window is seen causing problems with non-conforming parts. Up until now, these driftshave been counter-acted based on the knowledge and experience of the press line operators.This experience-based process control will however become more troublesome in the future asrecycled material grades will undoubtedly present larger in-coil variations in material parametersand effect also the friction conditions from component to component.The following study will present two cases from production of the Volvo XC60. For thetwo cases, the initial simulations made for the components showed a safe part, but duringrunning production failure occurred suspected to be due to temperature effects in the tribologysystem. The study will furthermore present updated simulations considering developing thermaleffects to replicate the failures, as well as present both standard and thermal simulations of theadjustments made in production.