2015
DOI: 10.1007/s11223-015-9621-7
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Effect of Grinding Conditions of a TC4 Titanium Alloy on its Residual Surface Stresses

Abstract: The grindability of titanium alloys that are classified as hard-to-machine materials is studied in high-speed cylindrical grinding using a cubic boron nitride (CBN) wheel. The investigation is concerned with residual surface stresses, including the construction of its empirical model, orthogonal experiments with a CBN grinding wheel at a speed of 45-150 m/s, and prediction with the back propagation (BP) network. The results of residual surface stress measurements obtained in grinding experiments and simulatio… Show more

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Cited by 9 publications
(5 citation statements)
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“…For the present study, it is possible to conclude that the relationship between the bonding strength and process variables may be complex, and thus it may not be captured by MLR. As stated before, ANNs are capable of accurate mapping of complex or nonlinear relationship between the variables [28]. Therefore, findings of the present study are reasonable and compatible with those of earlier investigations.…”
Section: Predictive Ability Of the Ann And Mlr Methodssupporting
confidence: 93%
See 1 more Smart Citation
“…For the present study, it is possible to conclude that the relationship between the bonding strength and process variables may be complex, and thus it may not be captured by MLR. As stated before, ANNs are capable of accurate mapping of complex or nonlinear relationship between the variables [28]. Therefore, findings of the present study are reasonable and compatible with those of earlier investigations.…”
Section: Predictive Ability Of the Ann And Mlr Methodssupporting
confidence: 93%
“…ANNs are known as a massively parallel distributed processor that is able to simulate the behavior of the biological neural network [27]. They have a good capability of nonlinear mapping, which allows the network to capture the complex relationship in data structure accurately [28]. The function of ANNs resembles the human brain in two respects: (1) the network acquires the knowledge by a learning procedure, and (2) connection strengths are used to store the acquired knowledge [27].…”
Section: Artificial Neural Networkmentioning
confidence: 99%
“…Using this method, a typical example was provided by Chen and Rowe [42] for En9 (medium carbon steel, AISI 1055), where the simulated maximum grinding temperature was 247°C and the predicted maximum tensile residual stress was 178 MPa. Additionally, Li and Jia [234] adopted a BP network model to establish the relationship between the grinding-induced residual stresses and the key grinding parameters; at the same time, this method was also utilized to predict the grinding forces and grinding burn of workpiece [234][235][236][237][238]. The accuracy of the BP network analysis results has been verified in grinding of titanium alloy Ti6Al-4V.…”
Section: Prediction Of Residual Stresses Based On Fuzzy Logic Methods mentioning
confidence: 97%
“…Wen et al [31] analyzed calculation formula of ultrasonic grinding force and the temperature field of ultrasonic grinding transient moving heat source, and established a thermal-mechanical coupling residual stress prediction model of ultrasonic grinding using finite element method. Li et al [32] suggested an empirical model of workpiece surface residual stress based on the experimental results obtained from high speed cylindrical grinding of titanium alloy. Alauddin et al [33] proposed a grinding force model by conducting grinding tests on K1045 steel with an alumina wheel.…”
Section: Introductionmentioning
confidence: 99%