Prediction of surface roughness in hard machining of EN31 steel with TiAlN coated cutting tool using fuzzy logic

Sivarajan, S.; Elango, M.; Sasikumar, M.; Doss, Arockia Selvakumar Arockia

doi:10.1016/j.matpr.2022.04.161

Cited by 8 publications

(4 citation statements)

References 19 publications

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“…They proposed a genetic algorithm concept to optimize the weighting factors of the network and found that the error when the network is optimized by genetic algorithm reduced to less than 2% [24]. S. Their suggested model was very close to the experimental results with error less than 5% [9]. Generally, machinability measures of PMS cannot be improved in the same cutting conditions, but it depends on the customer requirements.…”

Section: Introductionmentioning

confidence: 93%

“…Mainly, the researchers use one or two soft computing method, where some studies applied one of genetic algorithms [5], neural networks [6], or fuzzy techniques El Hossainy et al Journal of Engineering and Applied Science (2023) 70:7 [7][8][9], while other studies used combined techniques [10][11][12][13]. Without an exception, single or two soft computing techniques could be used.…”

Section: Introductionmentioning

confidence: 99%

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Machining process parameters optimization using soft computing technique

Hossainy

Zeyada²,

Abdelkawy

2023

J. Eng. Appl. Sci.

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This work introduces an approach for optimization machinability measures of power consumption, machining time, and the surface roughness (PMS). This approach is starting with market customer’s demands, passing by optimizing the machinability measures (PMS), and ending by the optimized cutting conditions. The fuzzy logic was used to define the weights of each of required machinability measurement using method through expert rules depending on factory requirements. Genetic algorithm was formulated for giving optimum output values based on the customer’s demands. A neural network was designed for controlling the input cutting conditions with the PMS output parameters. The proposed soft computing technique creates reasonable results compared to experimental results and gives rich investigations for optimizing the output parameters not only for increasing productivity and quality demands but also for saving power consumed. The variation of consumed power, machining time, and surface roughness was calculated based on different customer demand levels. When the machining time and power consumed importance increased, the proposed technique reduced them by about 20% and 10% for the testes case.

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Section: Introductionmentioning

confidence: 93%

Section: Introductionmentioning

confidence: 99%

Machining process parameters optimization using soft computing technique

Hossainy

Zeyada²,

Abdelkawy

2023

J. Eng. Appl. Sci.

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“…Random forest modelling fared better than other modelling techniques, such as multiple regression and quantile regression approach, during the hard turning operation for predicting surface roughness. Sivarajan et al [39] employed fuzzy logic modelling technique to foresee the surface finish in hard part turning of EN31 steel. MATLAB R2020b software is used to develop the model.…”

Section: Adopted Modelling Techniques In Hard Machiningmentioning

confidence: 99%

A contemporary advancement of intelligent machining and sustainability aspects in hard machining area: A Critical Review

Khatai,

Sahoo,

Kumar

et al. 2023

E3S Web Conf.

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The intelligent manufacturing devotes considerable effort to towards machining process. This phenomenon is engendered by the growing demand for advanced machining process for manufacturing of precise parts by adopting optimization techniques. This article illustrates the most significant developments in the sustainability aspects as well as optimization and modelling techniques adopted to solve the problems and complexity in hard machining process. Machining realisation necessitates recent and future breakthroughs in technological innovations for Industry 4.0. A significant amount of focus is also paid to the different sustainability aspects, modelling strategies and performance analysis during hard machining process. Many avenues for future study on the needs of intelligent manufacturing are discussed in this article. The future directions for intelligent machine systems and sustainability factors are also discussed for the green, sustainable, and high dimensional accuracy manufacturing in hard machining area.

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“…The results show that the ANFIS model outperformed the RSM model by 42% higher in predictive accuracy. Sivarajan et al [48] investigated the predictive accuracy of fuzzy logic during the hard turning of EN31 steel [49]. It was concluded that the fuzzy logic model predicted surface roughness with acceptable accuracy based on experimental conditions.…”

Section: Introductionmentioning

confidence: 99%

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

Adizue,

Tura,

Isaya

et al. 2023

Preprint

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High-quality machining is a crucial aspect of contemporary manufacturing technology due to the vast demand for precision machining for parts made from hardened tool steels and super alloys globally in the aerospace, automobile, and medical sectors. The necessity to upheave production efficiency and quality enhancement at minimum cost requires deep knowledge of this cutting process and development of machine learning-based modeling technique, adept in providing essential tools for design, planning, and incorporation in the machining processes. This research aims to develop a predictive surface roughness model and optimize its process parameters for Ultra-precision hard-turning finishing operation. Ultra-precision hard-turning experiments were carried out on AISI D2 of HRC 62. The response surface method (RSM) was applied to understand the effect of process parameters on surface roughness and carry out optimization. Based on the data gained from experiments, Machine learning models and algorithms were developed with Support vector machine (SVM), Gaussian process relation (GPR), Adaptive-neuro fuzzy inference system (ANFIS), and artificial neural network (ANN) for the prediction of surface roughness. The results show that ANFIS gave the best predictive accuracy of average R, RMSE, and MAPE values of 0.98, 0.06, and 9.98%, respectively, and that of additional validation tests were 0.81, 0.17 and 32.34%, respectively, which are found reasonably accurate. The RSM analysis shows that the feed is the most significant factor for minimizing surface roughness Ra among the process parameters, with 92% influence, and optimal cutting conditions was found to be cutting speed = 100 m/min, feed = 0.025 mm/rev and depth of cut = 0.09 mm, respectively. This finding can be helpful in the decision-making on process parameters in the precision machining industry.

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Prediction of surface roughness in hard machining of EN31 steel with TiAlN coated cutting tool using fuzzy logic

Cited by 8 publications

References 19 publications

Machining process parameters optimization using soft computing technique

Machining process parameters optimization using soft computing technique

A contemporary advancement of intelligent machining and sustainability aspects in hard machining area: A Critical Review

Surface Quality Prediction by Machine Learning Methods and Process Parameter Optimization in Ultra-Precision Machining of AISI D2 Using CBN tool

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