Determination of the stability lobes in milling operations based on homotopy and simulated annealing techniques

Olvera, D.; Elı́as-Zúñiga, Alex; Martínez-Alfaro, H.; Lacalle, Luís Norberto López de; Rodrı́guez, Ciro A.; Campa, Francisco J.

doi:10.1016/j.mechatronics.2014.01.009

Cited by 40 publications

(14 citation statements)

References 32 publications

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“…The initial temperature T 0 is supposed to be high enough to allow acceptance of any new state in the first step. In each step, the procedure generates a fixed number of neighborhood solutions and evaluates them using the current temperature value T s = αT 0 , where α(0 < α < 1) is an attenuation factor [33,34]. The proposed simulated annealing algorithm used in this paper can be summarized as Figure 4.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table by Using Simulated Annealing Algorithm

Yao

Wan

2018

Applied Sciences

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In order to study the anti-shock performance of equipment under different working conditions, a hydraulic servo shaking table is used to replicate the desired motion with high fidelity. However, the sinusoidal acceleration response waveform is not a pure sinusoid due to the inherent nonlinearities within the system. The generating higher harmonic deteriorates the control performance and leads to system instability. To suppress the harmonic distortion and accurately estimate harmonic information, the harmonic estimation scheme based on simulated annealing algorithm is proposed. The sum of error square between actual value and estimated value is defined as the objective function. The amplitude and phase of each harmonic can be directly extracted when the objective function is minimized. Simulation and experimental results indicate that the proposed algorithm has good convergence performance and high estimation precision.

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Section: Simulated Annealing Algorithmmentioning

confidence: 99%

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table by Using Simulated Annealing Algorithm

Yao

Wan

2018

Applied Sciences

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“…Even though, it requires a high number of iterations to find the optimum solution, it can find the global optimum with high probability. Readers interested in the principles of SA are referred to [50][51][52][53] …”

Section: Optimization Algorithmsmentioning

confidence: 99%

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

Coppel

Abellán-Nebot

Siller

et al. 2015

Int J Adv Manuf Technol

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Nowadays, the miniaturization of many consumer products is extending the use of micro-milling operations with high quality requirements. However, the impacts of cutting-tool wear on part dimensions, form and surface integrity are not negligible and part quality assurance for a minimum production cost is a challenging task. In fact, industrial practices usually set conservative cutting parameters and early cutting replacement policies in order to minimize the impact of cutting-tool wear on part quality. Although these practices may ensure part integrity, the production cost is far away to be minimized, especially in highly tool-consuming operations like mold and die micromanufacturing. In this paper, an Adaptive Control Optimization (ACO) system is proposed to estimate cutting-tool wear in terms of part quality and adapt the cutting conditions accordingly in order to minimize the production cost, ensuring quality specifications in hardened steel micro-parts. The ACO system is based on: i) a monitoring sensor system composed of a dynamometer; ii) an estimation module with Artificial Neural Networks models; iii) an optimization module with evolutionary optimization algorithms; and iv) a CNC interface module. In order to operate in a nearly real time basis and facilitate the implementation of the ACO system, different evolutionary optimization algorithms are evaluated such as Particle Swarm Optimization (PSO), Genetic Algorithms (GA) and Simulated Annealing (SA) in terms of accuracy, precision and robustness. The results for a given micro-milling operation showed that PSO algorithm performs better than GA and SA algorithms under computing time constraints. Furthermore, the implementation of the final ACO system reported a decrease in the production cost of 12.3% and 29% in comparison with conservative and high production strategies, respectively.

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“…whose exact solutions are similar to those given by (6). We shall next explore the applicability of the nonlinear transformation method in obtaining the equivalent representation form of (1) and, then, we will compare the numerical integration solutions of five dynamics systems, having nonlinear restoring forces with rational or irrational terms, with respect to their equivalent representation forms [13][14][15][16][17][18][19][20]. First, let us consider the case for which the restoring forces are of the cubic type.…”

Section: A Nonlinear Transformation Approachmentioning

confidence: 99%

“…The parameter values used to obtain these plots were = 1, 1 = 2 = 3 = 4 = 1, and = −2.9, with 0 (0) = 0.5,̇0(0) = 0, Mathematica 9.0 or the MATLAB V.2012a computer packages. In an attempt to capture the dynamics system response with this nonlinear value, we have numerically solved (25) by using the Enhanced Multistage homotopy perturbation method (EMHPM) introduced in [18]. This technique also fails at values of ≥ 4, as illustrated in Figure 6 by the black and the purple dots.…”

Section: Examplementioning

confidence: 99%

A Transformation Method for Solving Conservative Nonlinear Two-Degree-of-Freedom Systems

Elı́as-Zúñiga

Trejo

Real

et al. 2014

Mathematical Problems in Engineering

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Determination of the stability lobes in milling operations based on homotopy and simulated annealing techniques

Cited by 40 publications

References 32 publications

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table by Using Simulated Annealing Algorithm

Acceleration Harmonic Estimation for Hydraulic Servo Shaking Table by Using Simulated Annealing Algorithm

Adaptive control optimization in micro-milling of hardened steels—evaluation of optimization approaches

A Transformation Method for Solving Conservative Nonlinear Two-Degree-of-Freedom Systems

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