Systematic Process Improvement with Noise

Systematic Process Improvement with Noise Systematic Process Improvement with Noise Systematic Process Improvement with Noise Systematic Process Improvement with Noise Systematic Process Improvement with Noise Systematic Process Improvement with Noise Systematic Process Improvement with Noise Systematic Process Improvement with Noise

Sheet metal forming process design is becoming more and more complex. This greater complexity is the result of the continuously increasing geometrical complexity of stamped parts as well as the numerous applications of high-strength steels. Furthermore, the highly competitive market situation creates a great demand for the delivery of high quality stamped parts with an ever decreasing lead time. With a focus on these latest challenges in the automotive industry, AutoForm Engineering has developed an innovative approach to the systematic improvement of the forming process. This systematic approach brings transparency to the forming process, allows for better understanding of the forming process, addresses stamping robustness and leads to shorter development and tryout times. Increased efficiency is achieved as engineers can now address and solve key manufacturing problems before going into production.

Systematic Process Improvement

Stamping simulation allows engineers to detect errors and problems, such as wrinkles or splits in parts, at an early stage of the forming process. During tool and process design, many design parameters must be defined, such as part radii, binder geometry, addendum geometry, use and position of drawbeads, blankholder forces, lubrication, etc. The decisions made by engineers are mainly based on company standards and experience and they have a direct influence on the quality of the forming process. It is important to identify which design parameters influence part quality and to what extent. Multiple stamping simulations are carried out automatically. During these simulations, design parameters are varied while engineers maintain their focus on the quality targets set for the stamped part. The quality target can be specified with regard to one or more output variables from the simulation, e.g. no wrinkles, no splits or sufficient stretching. In this way, design parameters which have the most influence on the stamped part can be identified already during the tool and process design stage, enabling engineers to make necessary adjustments, which thereby contribute to the systematic improvement of the forming process. The best selected set of design variable values results in a feasible process.


The process design does not only have to be feasible, it must also be robust. In everyday production, parts produced smoothly one day may experience problems the next day even though production conditions have not changed at all. This is due to naturally present “noise” and variations in the forming process. To analyze the influence of noise variables on the forming process, robustness analysis is carried out. For this analysis, the user defines a variation for every noise variable in the form of a mean value and the associated standard deviation. Based on this variation, multiple simulations are carried out automatically. All simulations are analyzed focusing on a quality function, which is dependent on the noise variables. In the robustness analysis, it can be verified whether a forming process provides stable results under the influence of common noise of various parameters. The robustness analyses takes into account the noise and variability that are inherent in the forming process which thereby better reflects the real state of manufacturing.

Systematic Process Improvement with Noise

Instead of performing systematic process improvement and robustness tasks subsequently, these tasks can now be combined into one analysis called systematic process improvement with noise. This analysis directly takes into account the result variation caused by the noise variables when identifying the best possible design variable values.

The image illustrates the different feasible process windows with or without taking noise into account. In the case of the systematic process improvement analysis, the black line represents the corresponding result while varying the design variable. Those results below the upper limit fulfill the quality criterion. The illustration shows that both small and large design variable values result in a feasible process. During the systematic process improvement with noise analysis, design and noise variables are simultaneously varied. The result is represented by the area in yellow. For small design variable values, the process proves to be very sensitive to the noise and the result scatters above the upper limit. For these design variable values, the process is not robust. Large design variable values show less sensitive behavior and all results remain below the upper limit. Therefore, for high design variable values the process not only fulfills the quality criterion but is robust as well.

Systematic process improvement with noise enables the engineer to develop a much faster and more reliable forming process. This methodology can be characterized as highly standardized as it reduces the trial-and-error approach of the manual optimization and results in a robust process required for tryout and during the manufacturing phase. There is no other software on the market with this new approach, which can be used by both experienced engineers as well as those without long experience. Systematic process improvement with noise ensures the most efficient and stable manufacturing process while simultaneously meeting the desired quality targets. A stable production process is a key requirement for cost efficient manufacturing.