Applying extended Oxley’s machining theory and particle swarm optimization to model machining forces

Filho, Joel Martins Crichigno

doi:10.1007/s00170-016-9155-6

Cited by 11 publications

(9 citation statements)

References 18 publications

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“…Performance index of error absolute value time integration is adopted as the minimum target function of parameter selection and the quadratic term of controlling input is added to the target function to limit the excess controlling energy. The following formula is used as the optimal index of parameter selection [11].…”

Section: Evaluation Function Selectionmentioning

confidence: 99%

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Adsorption control of a pipeline robot based on improved PSO algorithm

Wang

et al. 2020

Complex Intell. Syst.

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Particle swarm optimization (PSO) is a widely used method that can provide good parameters for the motion controller of mobile robots. In this paper, an improved PSO algorithm that optimize the control PID parameters of a specific robot have been proposed. This paper first presents a brief review of recently proposed PSO methods, and then presents a detailed analysis of the PID optimization algorithm, which uses H∞ theory to reduce the search space and fuses the information entropy to ensure the diversity of particles. Simulations in Matlab show that the algorithm can improve the convergence speed and get a better global optimization ability than the standard PSO algorithm. Experimental results present a sound effects for the control of the negative pressure adsorption motor in the power grid pipeline robot during its adsorption along the circular movements, which verifies the effectiveness of the proposed method.

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Section: Evaluation Function Selectionmentioning

confidence: 99%

“…In recent years, there are many PID parameter tuning methods based on artificial intelligence technology [10], such as expert system method. The PSO PID control algorithm proposed in reference [11] has a good control effect on the process control with large time delay.…”

Section: Introductionmentioning

confidence: 99%

Adsorption control of a pipeline robot based on improved PSO algorithm

Wang

et al. 2020

Complex Intell. Syst.

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“…As mentioned in the previous sections, in contrast to the original DPZD model, the enhanced version establishes a two-way relation between stress and temperature distributions while updating the involved material properties (see Fig. 3) so that they are equilibrated in terms of energy (12). The addition of temperature dependence makes a significant difference in the stress distribution due to the thermal softening effect.…”

Section: Comparison Between Original and Enhanced Dpzd Modelsmentioning

confidence: 99%

“…To calibrate the parameters, an objective function is defined as follows: (15) where x = {A, B, C, n, m} is the vector that contains JC material parameters to be calibrated, F = √ 3τ e from Eq. 8 is the effective stress calculated from DPZD model and f = σ e is the consistent JC flow stress computed from the assumed shear strain rate (2), shear strain (5), and temperature distributions (12). For the effective strain rate in Eq.…”

Section: Identification Processmentioning

confidence: 99%

“…A number of studies focused on the implementation of more robust optimization algorithms for inverse identification of JC parameters have been made. For instance, Filho [12] used the particle swarm optimization algorithm, and Shrot and Bäker [13] utilized a re-identification process of the JC parameters based on Levenberg-Marquardt optimization algorithm. Malakizadi et al [14] combined response surface methodology with Oxley's machining theory for inverse identification of JC parameters.…”

Section: Introductionmentioning

confidence: 99%

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A thermomechanically motivated approach for identification of flow stress properties in metal cutting

Erturk

Malakizadi

Larsson

2020

Int J Adv Manuf Technol

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The paper presents a novel thermomechanically coupled distributed primary deformation zone model to assist the inverse identification of Johnson-Cook material parameters to be used for machining simulations. A special feature of the enhanced model is that the assumed stress field is temperature-dependent, where the thermomechanical coupling governs the stress and temperature distributions across the primary shear zone to describe the thermal softening effect. By using stress, strain, strain rate, and temperature distributions from the thermomechanically enhanced model, Johnson-Cook material parameters are calibrated for orthogonal cutting tests of C38, 42CrMo4, and AA6082 materials where continuous chip formation prevails. The performance of the parameters is compared with that of a wider set of cutting tests using finite element simulations. The results show that the thermomechanically motivated model yields closer results to experiments in terms of cutting force and chip thickness (9% and 34% difference, respectively) compared with the original thermally uncoupled model (47% and 92% difference, respectively). Identification of the material parameters by this method focuses directly on the orthogonal cutting test and it does not require many experiments or simulations. In fact, the proposed methodology is computationally robust and cost-efficient which makes it preferable compared with other methods which are more accurate but highly time-consuming.

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An analytical transient cutting force model of high-speed ultrasonic vibration cutting

Zhang

et al. 2018

Int J Adv Manuf Technol

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Applying extended Oxley’s machining theory and particle swarm optimization to model machining forces

Cited by 11 publications

References 18 publications

Adsorption control of a pipeline robot based on improved PSO algorithm

Adsorption control of a pipeline robot based on improved PSO algorithm

A thermomechanically motivated approach for identification of flow stress properties in metal cutting

An analytical transient cutting force model of high-speed ultrasonic vibration cutting

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