O. Adams

Self-optimizing Production Technologies

Customer demands have become more individual and complex, requiring a highly flexible production. In high-wage countries, efficient and robust manufacturing processes are vital to ensure global competitiveness. One approach to solve the conflict between individualized products and high automation is Model-based Self-optimization (MBSO). It uses surrogate models to combine process measures and expert knowledge, enabling the technical system to determine its current operating point and thus optimize it accordingly. The objective is an autonomous and reliable process at its productivity limit. The MBSO concept is implemented in eight demonstrators of different production technologies such as metal cutting, plastics processing, textile processing and inspection. They all have a different focus according to their specific production process, but share in common the use of models for optimization. Different approaches to generate suitable models are developed. With respect to implementation of MBSO, the challenge is the broad range of technologies, materials, scales and optimization variables. The results encourage further examination regarding industry applications.

Modellbasierte prädiktive Kraftregelung beim Fräsen

Kurzfassung Die Zerspankraft liefert in spanenden Fertigungsverfahren eine wichtige Information über den Prozesszustand. Trotz optimal ausgelegter Prozesse wird selten eine konstante Zerspankraft erreicht, da Unsicherheiten, wie z. B. Werkzeugverschleiß oder Materialkenngrößen, diese beeinflussen. Aus Schutz vor Überlast, werden die Prozessparameter so gewählt, dass die vom Werkzeug ertragbare Last auch zum Standzeitende nicht überschritten wird. Damit einher geht ein Produktivitätsverlust bei schneidscharfem Werkzeug. Durch Regelung der Prozesskraft kann eine signifikante Produktivitätssteigerung erreicht werden. Die modellbasierte prädiktive Regelung (MPR) erlaubt die explizite Berücksichtigung von Nichtlinearitäten sowie zeitvariantem Übertragungsverhalten und ermöglicht so eine deutlich höhere Regelgüte als klassische PI-Regler.

Model-based predictive force control in milling: determination of reference trajectory

Today, powerful process simulation tools allow an offline process planning and optimization of metal cutting processes. The quality of the optimization strongly depends on the model and its parameters. Real cutting processes are influenced by uncertainties such as tool wear status or material properties, which are both unknown. To overcome this limitation, sensors and process control systems are used. Model-based Predictive Control (MPC) was developed in the 1970s for the chemical process industry. This control method was found to be very suitable to control complex manufacturing processes such as milling processes. Using MPC in metal cutting processes allows considering technological boundary conditions explicitly. Adapting the feed velocity and thus the process force increases the productivity and process stability in milling. A core element of the MPC is the use of a reference trajectory representing the time-dependent set point value in the optimization procedure. The tool path information, however, is given position-based. Thus, calculating the reference trajectory is not trivial and strongly influences the control quality. This paper presents two methods for determining the reference trajectory. The first method is based on an adaptive signal filter. For the second method the MPC is extended to a two-layer MPC: the first layer calculates an optimal reference trajectory; the second layer controls the machine tool.

Experimental Identification of Cutting Force Coefficients for Finish-Milling Operations Considering the Sensor’s Transmissibility

Due to lightweight construction numerous parts in turbomachinery industry with aerodynamic properties exhibit thin-walled features. Typical examples are compressor blades or turbine blades. Finish-milling depicts a stage of the manufacturing process of these parts with significant value creation. A major limitation of productivity is process stability in terms of self-excited or forced vibration. Different simulation approaches attempt determining a priori the process stability to avoid a bad surface quality, accelerated tool wear, tool breakage or scrapped parts. One distinctive part of these simulations is a cutting force model which incorporates material and tool dependent coefficients. The simulation accuracy directly depends on the exactness of these coefficients. Usually, these coefficients are identified experimentally from cutting force measurements with piezoelectric sensors, whose transmissibility is nonlinear. In this paper a multidimensional stationary inverse filter for compensating the influence of the nonlinear transmissibility of force sensors is presented. In a subsequent step, a Levenberg–Marquardt algorithm is used to identify cutting force coefficients from filtered force measurements. The functionality of the filter is validated by comparing highly nonlinear and almost linear piezoelectric force measurement sensors connected in series during finish-milling experiments. The accuracy of the identified cutting force coefficients is assessed by comparing cutting force simulations to measurements.Copyright

Evaluation of Multi-axis Machining Processes Based on Macroscopic Engagement Simulation

Process planning and process design to identify stable process areas is nowadays characterized by time-consuming correction loops, where the number of iterations and the effort involved are mostly from the experience and knowledge of process designer. This requires on the one hand additional planning steps as deriving process parameters and secondly an evaluation of the achieved product quality. By using the macro simulation model introduced in this paper, the computational complexity to obtain significant process knowledge is decreased and thus made accessible more easily. Detailed tool-workpiece engagement is calculated through the presented model, which co-relates to mechanical and thermal stresses on the tool. Based on the calculations the process can be designed by reducing the tool load in the course of the process. This way, the tool life of the used milling cutters can be significantly increased resulting in an increase of process robustness and efficiency, thereby reducing used resources.