VIRTUAL MACHINING SYSTEM ENGINE FOR SIMULATION OF THE PROCESS MACHINE INTERACTION

2012 ◽  
Vol 2012 (01) ◽  
pp. 310-314
Author(s):  
Andreas Archenti ◽  
Mihai Nicolescu ◽  
Thomas Lundholm
2021 ◽  
Vol 111 (05) ◽  
pp. 336-342
Author(s):  
Christian Brecher ◽  
Thomas Bergs ◽  
Caroline Kiesewetter-Marko ◽  
Maximilian Schrank ◽  
Stephan Neus ◽  
...  

Für die Endbearbeitung von Zahnrädern wird unter anderem das Verzahnungshonen eingesetzt. Die theoretisch erreichbare Maschinenleistung kann aufgrund auftretender Schwingungen eingeschränkt sein. In einem aktuellen Forschungsprojekt zur Abbildung der Prozess-Maschine-Interaktion beim Verzahnungshonen werden die Eigenschaften der Maschine und die Prozesskräfte berücksichtigt. In diesem Beitrag wird ein gekoppeltes Prozess-Maschine-Modell für das Verzahnungshonen vorgestellt.   One of the processes used for finishing gears is gear honing. The theoretically achievable machine performance can be limited due to occurring vibrations. A current research project for modelling the process-machine interaction in gear honing considers the characteristics of the machine and the process forces. This article presents a coupled process-machine model for gear honing.


2018 ◽  
Vol 25 ◽  
pp. 338-343 ◽  
Author(s):  
Nikolas Theissen ◽  
Theodoros Laspas ◽  
Károly Szipka ◽  
Andreas Archenti

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Wei Feng ◽  
Bin Yao ◽  
BinQiang Chen ◽  
DongSheng Zhang ◽  
XiangLei Zhang ◽  
...  

Interaction of process and machine in grinding of hard and brittle materials such as cemented carbide may cause dynamic instability of the machining process resulting in machining errors and a decrease in productivity. Commonly, the process and machine tools were dealt with separately, which does not take into consideration the mutual interaction between the two subsystems and thus cannot represent the real cutting operations. This paper proposes a method of modeling and simulation to understand well the process-machine interaction in grinding process of cemented carbide indexable inserts. First, a virtual grinding wheel model is built by considering the random nature of abrasive grains and a kinematic-geometrical simulation is adopted to describe the grinding process. Then, a wheel-spindle model is simulated by means of the finite element method to represent the machine structure. The characteristic equation of the closed-loop dynamic grinding system is derived to provide a mathematic description of the process-machine interaction. Furthermore, a coupling simulation of grinding wheel-spindle deformations and grinding process force by combining both the process and machine model is developed to investigate the interaction between process and machine. This paper provides an integrated grinding model combining the machine and process models, which can be used to predict process-machine interactions in grinding process.


2017 ◽  
Vol 5 (3) ◽  
pp. 299-304 ◽  
Author(s):  
Hong-seok Park ◽  
Bowen Qi ◽  
Duck-Viet Dang ◽  
Dae Yu Park

Abstract Feedrate optimization is an important aspect of getting shorter machining time and increase the potential of efficient machining. This paper presents an autonomous machining system and optimization strategies to predict and improve the performance of milling operations. The machining process was simulated and analyzed in virtual machining framework to extract cutter-workpiece engagement conditions. Cutting force along the cutting segmentation is evaluated based on the laws of mechanics of milling. In simulation, constraint-based optimization scheme was used to maximize the cutting force by calculating acceptable feedrate levels as the optimizing strategy. The intelligent algorithm was integrated into autonomous machining system to modify NC program to accommodate these new feedrates values. The experiment using optimized NC file which generates by our smart machining system were conducted. The result showed autonomous machining system, was effectively reduced 26%. Highlights The smart machining system was implemented in the CNC machine. Optimal feed rates enhance machine tool efficiency. The smart machining system is reliable to reduce machine time.


2021 ◽  
Vol 67 (5) ◽  
pp. 235-244
Author(s):  
Mohsen Soori ◽  
Mohammed Asmael

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.


2021 ◽  
Author(s):  
Arameh Eyvazian ◽  
Farayi Musharavati ◽  
Afrasyab Khan ◽  
Mohsen Soori ◽  
Tamer A. Sebaey ◽  
...  

Abstract 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.


Author(s):  
Mohsen Soori ◽  
Behrooz Arezoo ◽  
Mohsen Habibi

Virtual manufacturing systems carry out the simulation of manufacturing processes in digital environment in order to increase accuracy as well as productivity in part production. There are different error sources in machine tools, such as tool deflection, geometrical deviations of moving axis, and thermal distortions of machine tool structures. The errors due to tool deflection are caused by cutting forces and have direct effects on dimensional accuracy, surface roughness of the parts, and efficient life of the cutting tool, holder, and spindle. This paper presents an application of virtual machining systems in order to improve the accuracy and productivity of part manufacturing by monitoring and minimizing the tool deflection error. The tool deflection error along machining paths is monitored to present a useful methodology in controlling the produced parts with regard to desired tolerances. Suitable tool and spindle can also be selected due to the ability of error monitoring. In order to minimize the error, optimization technique based on genetic algorithms is used to determine optimized machining parameters. Free-form profile of virtual and real machined parts with tool deflection error is compared in order to validate reliability as well as accuracy of the software.


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