Targeted surface conditioning of 100Cr6 during cryogenic hard turning by means of model-based open- and closed-loop process control

• Applicants: Professor Dr.-Ing. Jan C. Aurich; Professor Dr.-Ing. Tilmann Beck; Professor Dr.-Ing. Jörg Seewig
• Subject Area: Metal-Cutting and Abrasive Manufacturing Engineering
• Term: since 2018
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 401538950

The surface layer condition (SLC), such as roughness or residual stresses, of highly stressed components is important for technical applications, especially regarding the service life of the component. The SLC is mainly determined by the process parameters of the finishing process. However, the SLC is influenced by time-variant, process-specific disturbance variables such as tool wear or batch variations of the material. The aim of this project is to improve and in particular to ensure a stable quality of the RSZ of cryogenically hard-turned, high-strength 100Cr6 steel.To achieve this, the disturbance variables during machining are compensated by an adaptive control of the process parameters such as feed rate and cutting speed. In this context, the required monitoring of the present SLC as well as of the disturbance variables during cryogenic hard turning is challenging, since these cannot be measured directly. However, by using measurable in-process variables such as tool temperature, process forces or the surface roughness, they can be indirectly detected. Combined with process knowledge based on specific process models, the SLC can be estimated. As a major object of the project, this is performed by means of soft sensors developed from this knowledge. Furthermore, a controlling framework for cryogenic hard turning will be developed, which allows for targeted surface conditioning using these soft sensors. In the 1st funding period (FP), correlations between process and disturbance variables, in-process measured variables and the SLC were successfully identified. With the help of the latter, static process models were generated, which allow for an estimation of the SLC present after machining on the basis of the known variables.The goal of the 2nd FP is to refine and validate the mentioned models as well as to develop a controlling framework and a soft sensor in order to achieve the overall goal of implementing a control system for a targeted surface conditioning. Aiming at a robust acquisition of the SLC in-situ and ex-situ, the measurement technique used will be further adapted to process-dependent characteristics. In addition to the necessary validation of the process models, the models will also be extended with respect to the influence of consecutive machining steps, as they are common in machining. Here, the possible effects of consecutive cuts on the final SLC are quantified and the models are extended correspondingly. In order to build the soft sensors, these models are combined with the in-process measurement techniques to estimate the SLC. This estimation of the condition is carried out using methods of virtual metrology and approaches associated with Monte Carlo methods, among others. The result of the soft sensors, the estimated SLC, is then used to control the turning process by means of a model predictive control and experimentally validated after integration into the lathe.

• DFG Programme: Priority Programmes
• Subproject of: SPP 2086: Surface Conditioning in Machining Processes
• Co-Investigators: Privatdozent Dr.-Ing. Benjamin Kirsch; Dr.-Ing. Marek Smaga; Dr.-Ing. Gerhard Stelzer