Integrated optimization methodology for intelligent machining of inconel 825 and its shop-floor application

Tamang, S. K.; Chandrasekaran, M.

doi:10.1007/s40430-016-0570-2

Cited by 31 publications

(14 citation statements)

References 35 publications

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“…These examples indicate that machine learning methods can be used as effective predictive tools in controlling machining processes. Optimization methods can also be successfully used as components of production process control, as evidenced by various publications [ 6 , 15 , 16 , 17 , 18 , 19 ].…”

Section: Introductionmentioning

confidence: 99%

The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

Świć

Wołos

Gola

et al. 2020

Sensors

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The article presents an original machine-learning-based automated approach for controlling the process of machining of low-rigidity shafts using artificial intelligence methods. Three models of hybrid controllers based on different types of neural networks and genetic algorithms were developed. In this study, an objective function optimized by a genetic algorithm was replaced with a neural network trained on real-life data. The task of the genetic algorithm is to select the optimal values of the input parameters of a neural network to ensure minimum deviation. Both input vector values and the neural network’s output values are real numbers, which means the problem under consideration is regressive. The performance of three types of neural networks was analyzed: a classic multilayer perceptron network, a nonlinear autoregressive network with exogenous input (NARX) prediction network, and a deep recurrent long short-term memory (LSTM) network. Algorithmic machine learning methods were used to achieve a high level of automation of the control process. By training the network on data from real measurements, we were able to control the reliability of the turning process, taking into account many factors that are usually overlooked during mathematical modelling. Positive results of the experiments confirm the effectiveness of the proposed method for controlling low-rigidity shaft turning.

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Section: Introductionmentioning

confidence: 99%

The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

Świć

Wołos

Gola

et al. 2020

Sensors

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show abstract

“…The authors reported that WEDM of Ni55.8Ti at parameters optimized by NSGA-II resulted in improved surface quality (Rz) and process productivity (MRR). Tamang and Chandrasekaran [25] developed a model based on ANN. The authors optimized the process parameters in machining Inconel 825 using particle swarm optimization (PSO).…”

Section: Introductionmentioning

confidence: 99%

Multi-objective optimization of roundness, cylindricity and areal surface roughness of Inconel 825 using TLBO method in wire electrical discharge turning (WEDT) process

George

Manu

Mathew

2019

J Braz. Soc. Mech. Sci. Eng.

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Wire electrical discharge turning (WEDT) is a non-conventional machining process, which was developed to machine cylindrical components on difficult-to-machine, but electrically conductive materials with high precision. In the present work, roundness, cylindricity and areal surface roughness of the parts created by WEDT process are investigated. Inconel 825 was the material chosen for the present study. The main objective of this study is to identify the optimum process parameters required to obtain improved geometry of the turned components with respect to roundness, cylindricity and areal surface roughness. The effects of machining parameters in WEDT process on the output responses are studied using experiments based on Box-Behnken design. ANOVA has been performed to study the effect of process parameters on the output responses. A novel optimization approach is used to identify the optimum parameters for each response. Teaching learningbased optimization (TLBO) is used to optimize the process, considering the three output responses in order to improve the quality of the turned products. Validation experiments were conducted, and the predicted results were verified. It is observed that the roundness, cylindricity and areal surface roughness of components machined with WEDT process obtained from TLBO technique and validation experiments are in close agreement. The surface morphology of the samples having maximum and minimum Sa value was studied using SEM micrographs. Finally, a microelectrode of 185 µm diameter and 4 mm length was successfully fabricated with multiple cut strategy using the WEDT setup in order to demonstrate the process capability.

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“…Therefore, researchers have used MLP neural network in almost all machining processes. They found efficient performance of network model [24,28,29]. Risbood et al [24] used neural network to predict R a in turning using data of 26 experiments for training and testing.…”

Section: Introductionmentioning

confidence: 99%

“…Risbood et al [24] used neural network to predict R a in turning using data of 26 experiments for training and testing. Tamang and Chandrasekaran [28] used data of 22 experiments for training and 5 for validation. Kohli and Dixit [29] have proposed an MLP neural network to predict R a in turning process using data of 30 experiments for training and testing, and they predicted acceptable results.…”

Section: Introductionmentioning

confidence: 99%

Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718

Deshpande¹,

Andhare

Padole

2018

SN Appl. Sci.

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In this paper, artificial neural network approach is used to predict surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718. Experiments were performed by using cryogenically treated and untreated inserts, and various responses were measured. Then, these measured responses were used as input to the artificial neural network to predict surface roughness. It is found that the models developed by artificial neural network are predicting surface roughness with more than 98% accuracy. Further, the predictions obtained by artificial neural network are compared with the results of regression-based prediction models earlier proposed by the authors. The modified regression models were estimating surface roughness with more than 90% accuracy. Based on correlation coefficient values, the prediction results of modified regression model are compared with those obtained by artificial neural network. Finally, it is concluded that artificial neural network models are better for estimating surface roughness than the regression models and such predictions are useful for real-time control of the process to acquire the desired surface roughness. Keywords Artificial neural network • Surface roughness • Inconel 718 Abbreviations CNC Computer numerical control RSM Response surface methodology FOE M Modified first-order equation ANN Artificial neural network BP Backpropagation BR Bayesian regularization LM Levenberg-Marquardt AE Absolute error (%) MAE Mean absolute error (%) MSE Mean square error (%) List of symbols v Cutting speed (m/min) f Feed rate (mm/rev) d Depth of cut (mm) F c Cutting force (N) S Sound pressure level (Pa) V v Vibration velocity of workpiece (m/s) n Number of experiments R ai Average of measured surface roughness in μm R ai Estimated surface roughness h Number of neurons in single hidden layer R 2 Correlation coefficient

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Integrated optimization methodology for intelligent machining of inconel 825 and its shop-floor application

Abstract: are developed that help the machine tool operator to obtain optimum process parameters.

Cited by 31 publications

References 35 publications

The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

The Use of Neural Networks and Genetic Algorithms to Control Low Rigidity Shafts Machining

Multi-objective optimization of roundness, cylindricity and areal surface roughness of Inconel 825 using TLBO method in wire electrical discharge turning (WEDT) process

Application of ANN to estimate surface roughness using cutting parameters, force, sound and vibration in turning of Inconel 718

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