Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018)

Šibalija, Tatjana V.

doi:10.1016/j.asoc.2019.105743

Cited by 94 publications

(37 citation statements)

References 87 publications

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“…A detailed review and critical analysis of the PSO application in optimizing manufacturing processes, including laser-based processes, is presented in [44].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Previous studies showed that metaheuristic algorithm-specific parameters should be accurately set to ensure a fast convergence to the actual global optimum [6,44], but this issue has rarely been studied. Moreover, most of the methods mentioned are problem-dependent, so their applicability for optimizing other processes cannot be guaranteed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…As reported by several authors [44,46], the PSO parameters must be properly tuned to retain an equilibrium between global and local exploration and to find the actual optimum.…”

Section: Evaluate the Objective Function At All Particlesmentioning

confidence: 99%

“…The proposed swarm size is 2n to 5n [47]; n is the number of process parameters in this case. It has been noted that a large swarm size significantly improves the success rate of the algorithm [44]. Since there are four cutting parameters analyzed in this study, the following swarm sizes are tested: 8, 20, and 50.…”

mentioning

confidence: 99%

“…The following inertia weight ranges are tested: For moderately sized problems, it has been suggested to use c 1 = c 2 = 2.0 [47]. Since premature convergence is a major weakness of PSO, velocity reduction is recommended to improve the probability of obtaining a global optimum [44]. Therefore, the following values are tested: c 1 = c 2 = 0.1; c 1 = c 2 = 0.5; c 1 = c 2 = 2.0; c 1 = c 2 = 5, and c 1 = 0.7, c 2 = 1.5.…”

mentioning

confidence: 99%

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Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Šibalija

Petronić²,

Milovanović

2019

Metals

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This paper presents an experimental study carried out on Nimonic 263 alloy sheets to determine the optimal combination of laser cutting control factors (assisted gas pressure, beam focus position, laser power, and cutting speed), with respect to multiple characteristics of the cut area. With the aim of designing laser cutting parameters that satisfy the specifications of multiple responses, an advanced multiresponse optimization methodology was used. After the processing of experimental data to develop the process measure using statistical methods, the functional relationship between cutting parameters and the process measure was determined by artificial neural networks (ANNs).Using the trained ANN model, particle swarm optimization (PSO) was employed to find the optimal values of laser cutting parameters. Since the effectiveness of PSO could be affected by its parameter tuning, the settings of PSO algorithm-specific parameters were analyzed in detail. The optimal laser cutting parameters proposed by PSO were implemented in the validation run, showing the superior cut characteristics produced by the optimized parameters and proving the efficacy of the suggested approach in practice. In particular, it is demonstrated that the quality of the Nimonic 263 cut area and the microstructure were significantly improved, as well as the mechanical characteristics.Parameters of the laser cutting process must be properly tuned in order to obtain the desired outputs. The laser power must be sufficient to enable the cutting; the cutting speed should be high enough to prevent the diffusion of heat in a material and to form a broad heat-affected zone (HAZ) [4]. The assisting gas is very important for producing a high-quality cut without a grate, since it does not permit molten material droplets' solidification onto the cut surface. The cutting speed must be balanced; if excessive speed is used the grate would remain, while, with a very low speed, the edge quality of the cut would be affected and a wide HAZ would form [5].The cutting control factors considered in this study are the assisting gas pressure, the position of the beam focus, the laser power, and the cutting speed. At the output, seven responses of the cut area are observed. Since the process is characterized by multiple parameters and responses, an advanced optimization methodology is needed to obtain the optimal cutting setting that satisfies the specifications for multiple, correlated responses. Simulated annealing (SA) and a genetic algorithm (GA) have been employed previously, and it has been demonstrated that SA outperformed GA in the proposed method [6]. Particle swarm optimization (PSO) is used in this study and benchmarked with SA in terms of the quality, i.e., the accuracy of the obtained optimum, the effects of the algorithm's parameters on the obtained optimum, and the convergence speed. Since metaheuristic algorithms must be adequately tuned to obtain the right solution, the setting of the major PSO algorithm's parameters is studied in detail to evaluate th...

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“…A detailed review and critical analysis of the PSO application in optimizing manufacturing processes, including laser-based processes, is presented in [44].…”

Section: Literature Reviewmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

“…As reported by several authors [44,46], the PSO parameters must be properly tuned to retain an equilibrium between global and local exploration and to find the actual optimum.…”

Section: Evaluate the Objective Function At All Particlesmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Šibalija

Petronić²,

Milovanović

2019

Metals

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Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co(NH₃)₆]Cl₃ complex

Landolsi,

Echouchene,

Chouaieb

et al. 2024

Can J Chem Eng

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The study focuses on the efficiency of hexaamminecobalt (III) chloride (HACo, [Co(NH3)6]Cl3) immobilized on activated carbon for removing methylene blue (MB) from water solutions. The primary objective of this study was to assess the sorption performance of HACo immobilized on activated carbon in removing MB from water solutions. Additionally, predictive models were developed to optimize the MB removal percentage. Lastly, the study aimed to determine the optimal conditions for achieving maximum MB removal. Samples were characterized using scanning electron microscopy. Batch sorption experiments were conducted to analyze the impact of MB concentration, adsorbent mass, pH, temperature, and contact time. Predictive models were built using multiple linear regression and neural network techniques, specifically artificial neural networks (ANN) and hybrid ANN–particle swarm optimization (ANN‐PSO). The PSO‐ANN model with a single hidden layer of eight neurons trained using the Levenberg–Marquardt algorithm demonstrated high accuracy in predicting MB removal percentage, with mean absolute percentage error (MAPE) = 0.083788, root mean square error (RMSE) = 0.11441, and R2 = 0.99693. The MB adsorption process followed a mono‐layer with one energy model and a pseudo‐first‐order kinetic model. Optimization using the genetic algorithm revealed that the maximum MB removal percentage of 99.56% is achievable at an MB concentration of 9.36 mg/L, adsorbent mass of 15.72 mg, and temperature of 311.2 K. The study confirms the effectiveness of HACo immobilized on activated carbon for MB removal. The PSO‐ANN predictive model proved superior in accuracy compared to empirical models. Optimization results provide the optimal conditions for maximizing MB removal, offering valuable insights for practical applications.

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Control of the Molten Steel Level in the Top Side‐Pouring Twin‐Roll Casting Process Based on Fuzzy Rules Optimized by Particle Swarm Optimization Algorithm

Zhou

Mao

Xuan

et al. 2023

steel research int.

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Twin‐roll strip casting is a near‐net‐shape casting technology that can produce thin steel strips directly from molten steel. Stably controlling the molten steel level is regarded as an important issue to ensure strip quality and casting process stability. As the control of the molten steel level is a time‐varying, nonlinear, and multidisturbance complex system, it is difficult to establish an accurate process model for designing a model‐based controller. Top side‐pouring twin‐roll casting is a new kind of twin‐roll strip casting technology. This study introduces the control system of the top side‐pouring twin‐roll casting process. A fuzzy logic controller (FLC) with its fuzzy rules optimized by particle swarm optimization (PSO) is developed to regulate the molten steel level. Simulation results show that the performance of the FLC can be improved while its fuzzy rules are optimized by PSO. The objective function of PSO has a great influence on the optimization of the fuzzy rules. The top side‐pouring twin‐roll casting experiments are carried out using the FLC with its fuzzy rules optimized by PSO; the results show that strip quality and casting process stability are guaranteed.

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Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018)

Cited by 94 publications

References 87 publications

Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co(NH₃)₆]Cl₃ complex

Control of the Molten Steel Level in the Top Side‐Pouring Twin‐Roll Casting Process Based on Fuzzy Rules Optimized by Particle Swarm Optimization Algorithm

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Particle swarm optimisation in designing parameters of manufacturing processes: A review (2008–2018)

Cited by 94 publications

References 87 publications

Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Experimental Optimization of Nimonic 263 Laser Cutting Using a Particle Swarm Approach

Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co(NH3)6]Cl3 complex

Control of the Molten Steel Level in the Top Side‐Pouring Twin‐Roll Casting Process Based on Fuzzy Rules Optimized by Particle Swarm Optimization Algorithm

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Computational intelligence for empirical modelling and optimization of methylene blue adsorption phenomena utilizing an activated carbon‐supported [Co(NH₃)₆]Cl₃ complex