Background and Problem Definition
In industrial manufacturing, components are produced through a sequence of production processes. Each of these processes influences the subsequent steps, which can lead to undesirable effects such as geometric distortion due to residual stresses. Such deviations complicate efficient process chain design and require consideration of stochastic uncertainties. 

Although there are advanced methods for modelling individual production steps, there is a lack of integrated approaches that enable global optimization of the entire process chain. The conventional sequential approach uses result data from one process as input for the next without coupling the processes across parameters. This prevents effective optimization with regard to the overall objectives of production. 

Objectives of the SPP 2476
This is precisely where the priority program SPP 2476 comes in. It aims to identify and exploit synergies between the individual production steps. By reversing the traditional process chain design, an integrated model approach is to be developed that enables multi-criteria optimization. The focus is on the consideration of stochastic uncertainties. 

Inverse process chain modeling, a central component of the program, transfers the analysis of individual processes to the entire chain. A joint overall model should make it possible to optimize technical and cost-specific objectives simultaneously. This approach is intended to break through the previous limits of purely sequential design and improve the quality, costs and resource efficiency of production. 

Requirements and Structure of the Program
The following requirements apply to research within SPP 2476: 

• Process chains: The chains to be investigated must comprise at least two independent manufacturing processes and deliver a finished component at the end in accordance with a defined specification. 
• Inverse design: A coupled overall model must be developed for an inverse design that enables the optimization of process and process input variables. 
• Multi-criteria optimization: This optimization should relate to at least three target variables, including technical requirements and optionally cost and resource targets. 
• Uncertainty quantification: A methodical consideration of uncertainties is required across the entire process chain. 
• Materials: The components under consideration must be made of metallic materials and must already be manufactured in the first phase. 

Methodology and Cooperation
The implementation of the program requires interdisciplinary cooperation. Specialists from materials science integrate microstructural changes into the models, while production engineering develops and implements optimal process sequences. Measurement engineers ensure precise data collection using innovative sensor systems. The aim is to achieve a comprehensive understanding of the entire process chain that goes beyond the capabilities of the individual models. 

Course of the Funding Phases
The six-year term of the SPP is divided into two phases of three years each. In the first phase, the focus is on the detailed development and modeling of the process chain as well as the identification and implementation of the necessary sensor technology. At the same time, the individual processes will be developed and combined into a comprehensive process chain. 

On this basis, the modeling is further developed in the second phase with regard to an inverse analysis including the previously quantified uncertainties. The multi-criteria optimization will be carried out using an overall model and validated in practice. 

Proposals must be submitted via the DFG’s elan portal by 15 September 2024. With the new priority program, the DFG is providing significant impetus for the further development of production technology and promoting innovative approaches to process optimization in industrial manufacturing.