The Prediction of Surface Error Characteristics in the Peripheral Milling of Thin-Walled Structures

Wimmer, Sepp; Zaeh, Michael F.

doi:10.3390/jmmp2010013

Cited by 9 publications

(3 citation statements)

References 19 publications

(21 reference statements)

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“…To improve the precision of machined parts, the evaluation of surface error characteristics in thin-walled constructs during peripheral milling has been studied by Wimmer and Zaeh [1]. The process parameter optimization of thin-wall machining for wire arc additive manufactured parts is investigated by Grossi et al [2] to decrease the deformation error in the thin-walled manufactured components.…”

Section: Introductionmentioning

confidence: 99%

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

Soori

Asmael

2021

SV-JME

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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.

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Section: Introductionmentioning

confidence: 99%

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

Soori

Asmael

2021

SV-JME

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“…In such cases, the thin wall deflections from predictions considering mechanical/thermal loads [9,10] or real-time online measurement [8,11,12], are mostly used as position-dependent feedback to reselect and design processing parameters (e.g. smart tool paths [13][14][15] or constant contact forces [16]).…”

Section: Introductionmentioning

confidence: 99%

Controlling of compliant grinding for low-rigidity components

Yang

Liao

et al. 2020

International Journal of Machine Tools and Manufacture

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The machining of low-rigidity components (e.g. thin-walled) with compliant tools presents accuracy challenges as both sides in contact are being deformed. The controlling method presented in this paper enables, for the first time, to obtain the desired and uniform material removal rate by controlling the nominal tool offset when two bodies (workpiece and tool) are compliant in grinding. A contact deformation model is proposed to predict the relation between the nominal and actual tool offsets. The function of nominal tool offsets and material removal rates is obtained based on the calibration tests. Spot grinding tests have been performed for the validation of the calculated material removal rates, normal grinding forces and spot sizes, presenting position-dependent characteristics. The controlling method has been tested for the case of continuous grinding the whole area of a circular aluminium thin wall. The surfaces ground under the time-variant tool offsets (proposed approach) reach the desired removal depth with an average error of ≤10% and achieve 11.2μm~24.2μm (P-V) accuracy in the elastic domain, compared with the error of 76.8%~113.7% and accuracy of 42.6μm~50.1μm (P-V) in the circumstance of constant tool offsets (conventional approach).

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“…They concluded that VSR improved the shape and size of material stability to a significant level by relieving induced residual stresses in thin-walled parts. In addition to static and dynamic models, there are also analytical models related to the development of new methods for predicting the behavior of systems based on frequency response and deformations resulting from cutting forces [31].…”

mentioning

confidence: 99%

Multi-Criteria Selection of the Optimal Parameters for High-Speed Machining of Aluminum Alloy Al7075 Thin-Walled Parts

Lukić

Čep

Vukman

et al. 2020

Metals

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Thin-walled parts made of aluminum alloy are mostly used as structural elements in the aerospace, automobile, and military industries due to good homogeneity, corrosion resistance, and the excellent ratio between mechanical properties and mass. Manufacturing of these parts is mainly performed by removing a large volume of material, so it is necessary to choose quality machining parameters that will achieve high productivity and satisfactory quality and accuracy of machining. Using the Taguchi methodology, an experimental plan is created and realized. Based on its results and comparative analysis of multi-criteria decision making (MCDM) methods, optimal levels of machining parameters in high-speed milling of thin-walled parts made of aluminum alloy Al7075 are selected. The varying input parameters are wall thickness, cutting parameters, and tool path strategies. The output parameters are productivity, surface quality, dimensional accuracy, the accuracy of forms and surface position, representing the optimization criteria. Selection of the optimal machining parameter levels and their ranking is realized using 14 MCDM methods. Afterward, the obtained results are compared using correlation analysis. At the output, integrative decisions were made on selecting the optimal level and rank of alternative levels of machining parameters.

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The Prediction of Surface Error Characteristics in the Peripheral Milling of Thin-Walled Structures

Cited by 9 publications

References 19 publications

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

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

Controlling of compliant grinding for low-rigidity components

Multi-Criteria Selection of the Optimal Parameters for High-Speed Machining of Aluminum Alloy Al7075 Thin-Walled Parts

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