ASAAP – Advancing Sustainable Aluminium by Adaptive Processing

Digital Process Chains for a Sustainable Aluminum Circular Economy

Project Overview

The future of electromobility depends significantly on lightweight-optimized, recyclable materials. The ASAAP project (Advancing Sustainable Aluminium by Adaptive Processing) develops adaptive process chains to safely produce aluminum sheet metal components with increased recycling content from end-consumer scrap.

The goal is the adaptation of process chains to achieve highest product quality combined with maximum resource efficiency.

Goals and Innovations

ASAAP focuses on digital integration and model-based optimization along the entire process chain:

  • Reduction of CO₂ footprint through recycling content in aluminum sheets for battery housings and lightweight structures.
  • Adaptive Process Chains: Flexible manufacturing processes that optimally utilize recycling materials – without compromising strength or processability.
  • Extended Alloy Space: Systematic investigation of millions of alloy variants of 5000-series aluminum alloys to precisely control brittle phases, solidus temperature, and solid solution strengthening (SSS).
  • Data-Driven Alloy Development: Use of Design of Experiments (DoE) and exploratory data analysis to narrow the solution space and identify promising alloys.

Matplus Focus: Digitalized Process Chain and Evolution of Material Properties

Matplus contributes its expertise in digital integration of the process chain:

  • Development of the Adaptive Circularity Monitor (ACM) – an integration environment that captures real-time data from the process chain and model-based generates control suggestions for adjusting process parameters.
  • Through simulation-supported material modeling (e.g., with JMatPro) and exploratory data analysis, changes in material properties are predicted and specifically influenced.
  • The ACM enables continuous optimization of final processing properties – from alloy composition to finished component.

Technological Core

ASAAP combines experimental materials research with digital tools:

  • Model-based prediction of material behavior considering accompanying elements.
  • Adaptive control of process parameters to compensate for variations in recycling material.
  • Closed loop through feedback between simulation, production, and quality assurance.

Project Partners

  • Speira - Industrial implementation and scaling of processes
  • IBF RWTH Aachen - Forming technology
  • LMW Uni Siegen - Materials science
  • Matplus - Digitalization and optimization of the overall process chain
  • MUBEA - Component development and application-specific validation

Funding

State of North Rhine-Westphalia and European Union

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