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Concrete, ceramics, rocks, metal-matrix composites, titanium alloys and other hard-to-cut materials are frequently cut using the non-traditional cold processing method known as abrasive water jet machining (AWJM). A virtual machining system is proposed in the study to minimize roughness of surface and residual stress during AWJM of titanium alloy Ti6Al4V. The water and workpiece are simulated in virtual environments using the smoothed particle hydrodynamics (SPH) approach and finite element method, respectively. Then, the Johnson–Cook law model of titanium alloy is used to obtain the cutting temperature during cutting operations. To calculate residual stress during the AWJM operations, the finite element method is then utilized. The Taguchi optimization method is implemented to minimize surface roughness and residual stress in AWJM operations. Thus, the optimized machining parameters such as water jet pressure, traverse speed, abrasive mass flow rate and standoff distance are calculated to minimize the surface roughness and residual stress during the AWJM of titanium alloy Ti6Al4V. Experimental and computational examinations are carried out in order to validate the effectiveness and efficiency of proposed virtual machining system in minimization of surface roughness and residual stress during abrasive waterjet cutting of titanium alloy Ti6Al4V. As a result, by using the optimized machining parameters, 23.7% and 27.1% reduction in the measured and predicted residual stress of sample machined part are obtained. Also, the sample machined part's surface roughness is reduced by 26.7% and 27.9%, for the measured and predicted surface roughness of sample machined part, respectively. So, the quality and reliability of the machined parts can be enhanced using the developed virtual machining system in the study to minimize the surface roughness and residual stress in AWJM operations. [ABSTRACT FROM AUTHOR]

The residual stress and surface roughness enhancement of ground surfaces can decrease the lifetime of workpiece by reducing its fatigue life. It is critical to accurately predict and minimize the residual stress and surface toughness of ground surfaces in order to enhance efficiency of part production using grinding operations. A virtual machining system is developed in the research work to minimize the surface integrity and residual stress during grinding operations of Inconel 718. The cutting temperature during grinding operations is calculated using the Inconel alloy Johnson–Cook law models. Then, using the finite element method, residual stress during the grinding operation is estimated. Utilizing the established virtual machining method, the surface roughness is predicted in the study. Using the Taguchi optimization approach, the grinding parameters of depth of cut and feed velocity are optimized in order to reduce surface roughness as well as residual stress throughout grinding operations on Inconel 718 superalloy. To confirm the effectiveness of the developed technique in the study, simulation and experimentation are conducted. As a result, the quality as well as reliability of the ground surfaces can be enhanced by using the developed virtual machining system in the study to minimize the surface roughness and residual stress of produced parts using grinding operations. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]