A simulator based on artificial neural networks and NSGA-II for prediction and optimization of the grinding process of superalloys with high performance grinding wheels

Nametala, Ciniro Aparecido Leite; Souza, Adriel Magalhães; Júnior, Benvindo Rodrigues Pereira; Silva, Eraldo Jannone da

doi:10.1016/j.cirpj.2020.05.004

Cited by 11 publications

(2 citation statements)

References 54 publications

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“…In comparison with traditional methods such as regression, ANNs are more global and more flexible. Moreover, ANNs have good learning and adaptation capability, which makes them widely applicable in system modeling, image processing, decision making, and function optimization (Nametala et al , 2020). Recurrent dynamic neural network (RDNN) is a type of ANN, which has more complexity in structure compared with static neural networks.…”

Section: Hybrid Pso-rdnn Optimization Methodologymentioning

confidence: 99%

A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation

Pourmostaghimi

Zadshakoyan

Khalilpourazary

et al. 2022

AIEDAM

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In the present work, a new hybrid approach combining particle swarm optimization (PSO) algorithm with recurrent dynamic neural network (RDNN), which is described as PSO-RDNN algorithm, is proposed for multi-performance optimization of machining parameters in finish turning of hardened AISI D2. The suggested optimization problem is solved using the weighted sum technique. Process parameters including cutting speed and feed rate are optimized for minimizing operation cost, maximizing tool life, and producing parts with acceptable surface roughness. Based on experimental results, two neural network models were developed for predicting tool flank wear and surface roughness during the machining process. Based on trained neural networks and structured hybrid algorithm, optimum cutting parameters were obtained. The coefficient of determination for trained neural networks was calculated as R2 = 0.9893 and R2 = 0.9879 for predicted flank wear and surface roughness, respectively, which proves the efficiency of trained neural models in real industrial applications. Furthermore, the offered methodology returns a Pareto optimality graph, which represents optimized cutting variables for several various cutting conditions.

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Section: Hybrid Pso-rdnn Optimization Methodologymentioning

confidence: 99%

A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation

Pourmostaghimi

Zadshakoyan

Khalilpourazary

et al. 2022

AIEDAM

View full text Add to dashboard Cite

show abstract

“…Neural Network applications have been applied for tool tip dynamics, stability and optimization problems [36,44,87,88]. Misaka et al [89] considered Neural Networks, under the form of CNN, based on camera images of the metal cutting processing for machining parameters extraction, obtaining a model accuracy of 85.5% and a precision of 92.9%.…”

Section: Modelingmentioning

confidence: 99%

Recent Advances on Machine Learning Applications in Machining Processes

2021

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This study aims to present an overall review of the recent research status regarding Machine Learning (ML) applications in machining processes. In the current industrial systems, processes require the capacity to adapt to manufacturing conditions continuously, guaranteeing high performance- in terms of production quality and equipment availability. Artificial Intelligence (AI) offers new opportunities to develop and integrate innovative solutions in conventional machine tools to reduce undesirable effects during operational activities. In particular, the significant increase of the computational capacity may permit the application of complex algorithms to big data volumes in a short time, expanding the potentialities of ML techniques. ML applications are present in several contexts of machining processes, from roughness quality prediction to tool condition monitoring. This review focuses on recent applications and implications, classifying the main problems that may be solved using ML related to the machining quality, energy consumption and conditional monitoring. Finally, a discussion on the advantages and limits of ML algorithms is summarized for future investigations.

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A machine learning based optimization method towards removing undesired deformation of energy-absorbing structures

Wen

Yao

et al. 2021

Struct Multidisc Optim

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A simulator based on artificial neural networks and NSGA-II for prediction and optimization of the grinding process of superalloys with high performance grinding wheels

Cited by 11 publications

References 54 publications

A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation

A hybrid particle swarm optimization and recurrent dynamic neural network for multi-performance optimization of hard turning operation

Recent Advances on Machine Learning Applications in Machining Processes

A machine learning based optimization method towards removing undesired deformation of energy-absorbing structures

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