Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Boukezzi, Farid; Noureddine, Rachid; Benamar, Ali; Noureddine, Farid

doi:10.1504/ijise.2017.085227

Cited by 7 publications

(10 citation statements)

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“…ey have concluded that the ANN framework was known to be the best to foresee the roughness with great accuracy than the regression model. ese works by Boukezzi et al [5,6] said that ANN techniques emerge as the main tool to model the nonlinear problems in machining processes.…”

Section: Introductionmentioning

confidence: 99%

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Rajesh

Prabhuswamy

Raghavan

2022

Advances in Materials Science and Engineering

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In this paper, the influence of process components on surface roughness in turning of Inconel 625 using cubic boron nitride (CBN) is studied. A predictive model is developed to forecast the surface roughness using the cascade forward neural network (CFNN). The experiments are designed based on Taguchi. L27 orthogonal array (OA) is used to perform the experimental trails by considering speed, feed, and depth of cut as input factors. Out of 27 experimental trails, 18 experiments are used for training and 9 experimental trails are used for testing. The developed predictive model by the CFNN is compared with regression model values. The average prediction error for surface roughness is 2.94% with R2 = 99.99% by the CFNN. The CFNN is known to be superior to predict the response with minimum of percentage error. The minimum and maximum roughness observed at trail 8 and trail 20 is noted, respectively, and the increases in roughness at experimental trail 8 is equal to 3.384 times higher than the roughness observed at experimental trail.20. The feed rate dominates effectively on the roughness rather than other factors. The consequences of process factors on surface roughness are studied with the help of ANOVA. This experimental study and developed model would be used for aero parts manufacturing to forecast the roughness accurately before to the actual experiment so that actual machining and material cost could be avoided.

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Section: Introductionmentioning

confidence: 99%

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Rajesh

Prabhuswamy

Raghavan

2022

Advances in Materials Science and Engineering

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“…In manufacturing process, the satisfactory empirical and analytic physical models are limited, thus the neural network offers an excellent solution approach [23]. In this network model, a neuron is a basic unit that connected each other by links that known as synapses, each of synapses coupled with weight factor [24].…”

Section: Artificial Neural Network Modellingmentioning

confidence: 99%

Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling

Zakaria

Izamshah

Kasim

et al. 2019

emitter

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Sustainability plays an important role in manufacturing industries through economically-sound processes that able to minimize negative environmental impacts while having the social benefits. In this present study, the modeling of wire electrical discharge machining (WEDM) cutting process using an artificial neural network (ANN) for prediction has been carried out with a focus on sustainable production. The objective was to develop an ANN model for prediction of two sustainable measures which were material removal rate (as an economic aspect) and surface roughness (as a social aspect) of titanium alloy with ten input parameters. By concerning environmental pollution due to its intrinsic characteristics such as liquid wastes, the water-based dielectric fluid has been used in this study which represents an environmental aspect in sustainability. For this purpose, a feed-forward backpropagation ANN was developed and trained using the minimal experimental data. The other empirical modelling techniques (statistics based) are less in flexibility and prediction accuracy. The minimal, vague data and nonlinear complex input-output relationship make this ANN model simple and perfects method in the manufacturing environment as well as in this study. The results showed good agreement with the experimental data confirming the effectiveness of the ANN approach in the modeling of material removal rate and surface roughness of this cutting process.

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“…They used soft sensors for quality prediction, optimization for operating conditions improvement, and multivariate statistical process control (MSPC) for fault detection in a steel industry application. During the last two decades, neural networks have been widely used for process modelling and quality prediction as well (Boukezzi, 2017;Bhadesia H., 1999;Tamminen et al, 2013). Also Bayesian decision theory can provide solutions for simple systems, such as monitoring the condition of the manufacturing equipment (Rashidi and Jenab, 2013).…”

Section: Literature Reviewmentioning

confidence: 99%

“…Neural networks have been a popular method for modelling data with complex relationships between variables (Boukezzi, 2017;Bhadesia H., 1999;Tamminen et al, 2013). Lately, ensemble algorithms have risen to challenge them with equal accuracy, faster learning, tendency to reduce bias and variance, but also higher tendency to overfit.…”

Section: Models For Quality and Rejection Probability Predictionmentioning

confidence: 99%

An online quality monitoring tool for information acquisition and sharing in manufacturing: requirements and solutions for the steel industry

Tamminen

Ferreira

Tiensuu

et al. 2019

IJISE

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The purpose of this study was to develop an innovative online supervisor system to assist the operators of an industrial manufacturing process in discovering new solutions for improving both the products and the manufacturing process itself. In this paper, we discuss the requirements and practical aspects of building such a system and demonstrate its use and functioning with different types of statistical modelling methods applied for quality monitoring in industrial applications. The two case studies presenting the development work were selected from the steel industry. One case study predicting the profile of a stainless steel strip tested the usability of the tool offline, while the other study predicting the risk of roughness of a steel strip had an online test period. User experiences from a test use period were collected with a system usability scale questionnaire.

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Modelling, prediction and analysis of surface roughness in turning process with carbide tool when cutting steel C38 using artificial neural network

Cited by 7 publications

References 0 publications

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Modelling and Analysis of Surface Roughness Using the Cascade Forward Neural Network (CFNN) in Turning of Inconel 625

Enhancing the Productivity of Wire Electrical Discharge Machining Toward Sustainable Production by using Artificial Neural Network Modelling

An online quality monitoring tool for information acquisition and sharing in manufacturing: requirements and solutions for the steel industry

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