A particle swarm approach for grinding process optimization analysis

Lee, T. S.; Ting, T. O.; Lin, Yu-Chiao; Htay, Than

doi:10.1007/s00170-006-0538-y

Cited by 39 publications

(11 citation statements)

References 15 publications

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“…The explicit expressions of grinding roughness and grinding temperature can be obtained using Eqs. (12) and (13), which lay the foundation for the next multiobjective optimization.…”

Section: Response Surface Modelingmentioning

confidence: 99%

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Optimization of grinding efficiency considering surface integrity of bearing raceway

Chang

Jia

2019

SN Appl. Sci.

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In order to improve the grinding efficiency of bearing raceways, a multi-objective optimization method that considers the surface integrity constraints of the bearing raceway is proposed. Appropriate design points are selected through an orthogonal test, and a response surface model of the grinding parameters along with the response output is established on the basis of the test results. Explicit expressions for the grinding force and roughness are formulated, and the constraints necessary to meet the quality requirements are obtained. The genetic algorithm NSGA-II is applied to the multi-objective optimization of grinding time and material removal rate, and the Pareto set is solved. The results of the study show that using optimized grinding parameters can reduce the grinding time and material removal rate, and can also help broaden the available knowledge base on high-speed and high-efficiency grinding technology.

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“…The explicit expressions of grinding roughness and grinding temperature can be obtained using Eqs. (12) and (13), which lay the foundation for the next multiobjective optimization.…”

Section: Response Surface Modelingmentioning

confidence: 99%

“…Baskar [11] uses the ant colony algorithm to optimize plane-grinding parameters, reduce production costs, and improve productivity. Lee [12] uses particle swarm optimization to optimize grinding parameters, while ensuring grinding quality, improving productivity, and reducing production costs.…”

Section: Introductionmentioning

confidence: 99%

Optimization of grinding efficiency considering surface integrity of bearing raceway

Chang

Jia

2019

SN Appl. Sci.

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“…Therefore, several researchers used traditional methods for optimizing the cutting conditions such as Taguchi method [ 29 ] and Response surface methodology (RSM) [ 30 , 31 ]. However, Taguchi and RSM methods obtain optimal solutions dependent on the randomly chosen initial solutions, and the optimization falls into the local solution [ 32 , 33 ]. On the other hand, metaheuristic algorithms are being proposed by researchers to guarantee a globally optimal solution for machining characteristics.…”

Section: Introductionmentioning

confidence: 99%

Milling of Graphene Reinforced Ti6Al4V Nanocomposites: An Artificial Intelligence Based Industry 4.0 Approach

et al. 2020

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The studies about the effect of the graphene reinforcement ratio and machining parameters to improve the machining performance of Ti6Al4V alloy are still rare and incomplete to meet the Industry 4.0 manufacturing criteria. In this study, a hybrid adaptive neuro-fuzzy inference system (ANFIS) with a multi-objective particle swarm optimization method is developed to obtain the optimal combination of milling parameters and reinforcement ratio that lead to minimize the feed force, depth force, and surface roughness. For achieving this, Ti6Al4V matrix nanocomposites reinforced with 0 wt.%, 0.6 wt.%, and 1.2 wt.% graphene nanoplatelets (GNPs) are produced. Afterward, a full factorial approach was used to design experiments to investigate the effect of cutting speed, feed rate, and graphene nanoplatelets ratio on machining behaviour. After that, artificial intelligence based on ANFIS is used to develop prediction models as the fitness function of the multi-objective particle swarm optimization method. The experimental results showed that the developed models can obtain an accurate estimation of depth force, feed force, and surface roughness with a mean absolute percentage error of 3.87%, 8.56%, and 2.21%, respectively, as compared with experimentally measured outputs. In addition, the developed artificial intelligence models showed 361.24%, 35.05%, and 276.47% less errors for depth force, feed force, and surface roughness, respectively, as compared with the traditional mathematical models. The multi-objective optimization results from the new approach indicated that a cutting speed of 62 m/min, feed rate of 139 mm/min, and GNPs reinforcement ratio of 1.145 wt.% lead to the improved machining characteristics of GNPs reinforced Ti6Al4V matrix nanocomposites. Henceforth, the hybrid method as a novel artificial intelligent method can be used for optimizing the machining processes with complex relationships between the output responses.

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“…When designing an operation for a CNC machine, the greatest difficulties occur at the stage when the technologist assigns cutting parameters and projects a feed switching cycle, depending on the remaining part of the allowance. In the process of designing the grinding operation, the technologist should strive to design optimal cycle parameters that will ensure the maximum productivity of machining the batch of parts, taking into account the following technological factors and requirements noted in the works of Petrakov and Chamata (2015), Pereverzev and Akintseva (2017), Maris et al (1975), Lin et al (2009) and Nathan et al (2001):…”

Section: Introductionmentioning

confidence: 99%

Designing optimal automatic cycles of round grinding based on the synthesis of digital twin technologies and dynamic programming method

et al. 2019

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Abstract. The article presents methodology for designing optimal feed control cycles resistant to unstable machining conditions for a batch of parts in round grinding operations performed on CNC machines. To improve the quality and reliability of control programs for CNC machines, a digital twin (DT) is proposed to be used. It performs virtual testing of a given grinding cycle for the possibility of defect occurrence at some combination of variable technological factors. To design the optimal grinding cycle by calculating the optimum trajectory of the radial feed change cycle, we propose the dynamic programming method (DPM) used in solving the classical transport problem, in which a network of roads with intermediate stations is specified. In the task it is necessary to find the optimal trajectory of transport. The conditions for the appearance of defects detected by the DT are introduced into the system of restrictions, and the feed cycle is again optimized in the DPM optimization system. A new optimized cycle is again tested by the DT. These iterations are repeated until the DT fixes the present of a defect for the operation. The methodology proposed in the article for the synthesis of digital twin and DPM technologies at the stage of preparation of control programs for CNC machines makes it possible to guarantee the maximum productivity of the grinding operation while ensuring the specified quality of the machined surface under varying processing conditions that vary within the specified limits.

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A particle swarm approach for grinding process optimization analysis

Cited by 39 publications

References 15 publications

Optimization of grinding efficiency considering surface integrity of bearing raceway

Optimization of grinding efficiency considering surface integrity of bearing raceway

Milling of Graphene Reinforced Ti6Al4V Nanocomposites: An Artificial Intelligence Based Industry 4.0 Approach

Designing optimal automatic cycles of round grinding based on the synthesis of digital twin technologies and dynamic programming method

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