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One of the main objectives of production engineering is to reproducibly manufacture (complex) defect-free parts. To achieve this, it is necessary to employ an appropriate process or tool design. While this will generally prove successful, it cannot, however, offset stochastic defects with local variations in material properties. Closed-loop process control represents a promising approach for a solution in this context. The state of the art involves using this approach to control geometric parameters such as a length. So far, no research or applications have been conducted with closed-loop control for microstructure and product properties. In the project on which this paper is based, the local martensite content of parts is to be adjusted in a highly precise and reproducible manner. The forming process employed is a special, property-controlled flow-forming process. A model-based controller is thus to generate corresponding correction values for the tool-path geometry and tool-path velocity on the basis of online martensite content measurements. For the controller model, it is planned to use a special process or microstructure (correlation) model. The planned paper not only describes the experimental setup but also presents results of initial experimental investigations for subsequent use in the closed-loop control of α’-martensite content during flow-forming.

The production of complex multi-functional, high-strength parts is becoming increasingly important in the industry. Especially with small batch size, the incremental flow forming processes can be advantageous. The production of parts with complex geometry and locally graded material properties currently depicts a great challenge in the flow forming process. At this point, the usage of closed-loop control for the shape and properties could be a feasible new solution. The overall aim in this project is to establish an intelligent closed-loop control system for the wall thickness as well as the α’-martensite content of AISI 304L-workpieces in a flow forming process. To reach this goal, a novel sensor concept for online measurements of the wall thickness reduction and the martensite content during forming process is proposed. It includes the setup of a modified flow forming machine and the integration of the sensor system in the machine control. Additionally, a simulation model for the flow forming process is presented which describes the forming process with regard to the plastic workpiece deformation, the induced α’-martensite fraction, and the sensor behavior. This model was used for designing a closed-loop process control of the wall thickness reduction that was subsequently realized at the real plant including online measured feedback from the sensor system.

This paper evaluates the suitability of the X-ray diffraction (XRD) technique to characterize the phase transformation during the metal forming of the metastable austenitic steel AISI 304L. Due to plastic deformation, phase transformation from γ-austenite into α’-martensite occurs. The XRD peaks at specific 2θ diffraction angles give information about the phase amount. Analyses of the results with different characterization techniques such as microscopic analysis, including electron backscatter diffraction (EBSD), macro- and microhardness tests and magneto-inductive measurements of α’-martensite, were carried out. A qualitative and quantitative correlation to compute the amount of α’-martensite from the XRD measurements was deduced. XRD was validated as a suitable technique to characterize the phase transformation of metastable austenites. Additional data could provide necessary information to develop a more reliable model to perform a quantitative analysis of the phases from XRD measurements.

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