<jats:p>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.</jats:p>

<jats:title>Abstract</jats:title> <jats:p>This paper presents the characterization of the microstructure evolution during flow forming of austenitic stainless steel AISI 304L. Due to plastic deformation of metastable austenitic steel, phase transformation from γ-austenite into α’-martensite occurs. This is initiated by the formation of shear bands as product of the external stresses. By means of coupled microscopic and micromagnetic investigations, a characterization of the microstructure was carried out. In particular, this study shows the distribution of the strain-induced α’-martensite and its influence on material properties like hardness at different depths. The microstructural analyses by means of electron backscattered diffraction (EBSD) technique, evidence a higher amount of α’-martensite (ca. 23 %) close to the outer specimen surface, where the plastic deformation and the direct contact with the forming tool take place. In the middle area (ca. 1.5 mm depth from the outer surface), the portion of transformed α’-martensite drops to 7 % and in the inner surface to 2 %. These results are well correlated with microhardness and micromagnetic measurements at different depths. EBSD and atomic force microscopy (AFM) were used to make a detailed characterization of the topography and degree of deformation of the shear bands. Likewise, the mechanisms of nucleation of α’-martensite were discussed. This research contributes to the development of micromagnetic sensors to monitor the evolution of properties during flow forming. This makes them more suitable for closed-loop property control, which offers possibilities for an application-oriented and more efficient production.</jats:p>

The implementation of control systems in metal forming processes improves product quality and productivity. By controlling workpiece properties during the process, beneficial effects caused by forming can be exploited and integrated in the product design. The overall goal of this investigation is to produce tailored tubular parts with a defined locally graded microstructure by means of reverse flow forming. For this purpose, the proposed system aims to control both the desired geometry of the workpiece and additionally the formation of strain-induced α′-martensite content in the metastable austenitic stainless steel AISI 304 L. The paper introduces an overall control scheme, a geometry model for describing the process and changes in the dimensions of the workpiece, as well as a material model for the process-induced formation of martensite, providing equations based on empirical data. Moreover, measurement systems providing a closed feedback loop are presented, including a novel softsensor for in-situ measurements of the martensite content.