Virtual Minimization of Residual Stress and Deflection Error in the Five-Axis Milling of Turbine Blades

To simulate and analyse the real machined parts in virtual environments, virtual machining systems are applied to the production processes. Due to friction, chip forming, and the heat produced in the cutting zone, parts produced using machining operation have residual stress effects. The machining force and machining temperature can cause the deflection error in the machined turbine blades, which should be minimized to increase the accuracy of machined blades. To minimize the residual stress and deflection error of machined parts, optimized machining parameters can be obtained. In the present research work, the application of a virtual machining system is presented to predict and minimize the residual stress and deflection error in a five-axis milling operations of turbine blades. In order to predict the residual stress and deflection error in machined turbine blades, finite element analysis is implemented. Moreover, to minimize the residual stress and deflection error in machined turbine blades, optimized parameters of machining operations are obtained by using a genetic algorithm. To validate the research work, experimentally determining residual stress by using a X-ray diffraction method from the machined turbine blades is compared with the finite element results obtained from the virtual machining system. Also, in order to obtain the deflection error, the machined blades are measured by using the CMM machines. Thus, the accuracy and reliability of machined turbine blades can be increased by analysing and minimizing the residual stress and deflection error in virtual environments.

Surface Roughness Prediction and Minimization in 5-Axis Milling Operations of Gas Turbine Blades

To enhance the quality of machined parts, virtual machining systems are presented in this study. In the turbine blades, the minimization of the surface roughness of the blades can decrease the Reynolds number to decrease the loss of energy in power generation. Due to difficulties of polishing process in minimizing the surface roughness of machined blades, the optimized machining parameters for minimizing the surface roughness is an effective solution for the problem. In this study, a virtual machining system is developed to predict and minimize the surface roughness in 5-Axis machining operations of gas turbine blades. To minimize the surface roughness, the machining parameters were optimized by the Genetic algorithm. To validate the developed system, the turbine blades were machined using a 5-Axis CNC machine tool and the machined blades were measured using the CMM machine to obtain the surface roughness of machined parts. So, a 41.29% reduction in the measured surface roughness and a 42.09% reduction in the predicted surface roughness are obtained using the optimized machining parameters. The developed virtual machining system can be applied in the machining process of turbine blades to enhance the surface quality of machined blades and thus improve the efficiency of gas turbines.

Virtual Machining überzeugt in der Lohnfertigung

Der Auftragsfertiger CNC Berger setzt mit Erfolg auf eine durchdachte Fertigungs-IT-Infrastruktur. Dabei kommen CAM- und Simulationsanwendungen verschiedener Hersteller auf neutralem Werkzeug- und Technologie-Datenbank-Fundament zum Einsatz.

Automatische Ausrichtung in der Maschine

Auf der EMO in Mailand zeigt Open Mind Technologies die aktuelle Version 2021.2 der CAD/CAM-Suite Hyper-Mill. Zudem bieten die CAD/CAM-Experten einen ersten Ausblick auf Version 2022.1 und zeigen Zukunftsweisendes: Das Hyper-Mill Virtual Machining Center erstellt Simulationen auf Basis echter NC-Daten, schafft ein virtuelles Abbild der Maschinenrealität und soll damit Ausgangspunkt für fortschrittliche Prozessoptimierung und Automation sein.

Deflection Error Prediction and Minimization in 5-Axis Milling Operations of Thin-Walled Impeller Blades

To enhance accuracy as well as efficiency in process of machining operations, the virtual machining systems are developed. Free from surfaces of sophisticated parts such as turbine blades, airfoils, impellers, and aircraft components are produced by using the 5-axis CNC machine tools which can be analyzed and developed by using virtual machining systems. The machining operations of thin walled structures such as impeller blades are with deflection errors due to cutting forces and cutting temperatures. The flexibility of thin walled impeller blades can cause machining defects such as overcut or undercut. So, the desired accuracy in the machined impeller blades can be achieved by decreasing the deflection error in the machining operations. To minimize the deflection of machined impeller blades, optimized machining parameters can be obtained. An application of virtual machining system in predicting and minimizing the deflection errors of 5-Axis CNC machining operations of impeller blades is presented in the study to increase accuracy and efficiency in process of part production. The finite element analysis is applied to obtain the deflection error in machined impeller blades. In order to minimize the deflection error of impeller blades in the machining operations, the optimization methodology based on the Genetic algorithm is applied. The impeller is machined by using the 5-axis CNC machine tool in order to validate the developed virtual machining system in the study. Then, the machined impeller is measured by using the CMM machines to obtain the deflection error. As a result, the deflection error of in machining operations of impeller by using 5-Axis CNC machine tools can be decreased in order to enhance accuracy and efficiency of part manufacturing.

A Digital Twin of Multi-Axis Machine Tool for Micro Process Planning

Achieving high performance of machining production systems requires the use of multi-axis machine tools. In order to maximize the performance of multi-axis machine tools, micro process planning for creating machining data is important. Many researches on micro process planning mainly focused on 3-axis machining. As promising approaches among them, a micro process planning system was proposed that reuses actual machining cases and analyzes case data to derive the necessary rules. However, it is not always effective for multi-axis machining, because enough case data are not collected for micro process planning of a specific multi-axis machine tool. In this study, a digital twin of multi-axis machine tool in cyberspace is proposed to collect real and virtual machining case data for micro process planning.

Fehlerfreie Programmierung komplexer Bauteile

Der Schweizer Lohnfertiger Werkhalle Schmid AG will bei der Umsetzung seiner Digitalisierungsstrategie das Optimum erreichen. Daher setzt er auf die moderne Virtual-Machining-Prozesslösung von Coscom. Dadurch konnten die Rüstzeiten in der Fertigung auf ein Minimum reduziert werden.