Prediction of Surface roughness &amp; Material Removal Rate for machining of P20 Steel in CNC milling using Artificial Neural Networks

Vardhan, M. Vishnu; Sankaraiah, G.; Yohan, M.

doi:10.1016/j.matpr.2018.06.177

Cited by 14 publications

(7 citation statements)

References 4 publications

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“…The focus of this study is on the modeling of artificial neural networks for the prediction of the arithmetic mean roughness (Ra) in milling. Previous studies showed that neural networks can be applied for surface roughness predictions in different machining operations such as turning [5,[9][10][11]14,16,19], milling [3,4,[6][7][8]13,15,17,18,[20][21][22] and drilling [12].…”

Section: Introductionmentioning

confidence: 99%

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

et al. 2022

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Surface quality is one of the most important indicators of the quality of machined parts. The analytical method of defining the arithmetic mean roughness is not applied in practice due to its complexity and empirical models are applied only for certain values of machining parameters. This paper presents the design and development of artificial neural networks (ANNs) for the prediction of the arithmetic mean roughness, which is one of the most common surface roughness parameters. The dataset used for ANN development were obtained experimentally by machining AA7075 aluminum alloy under various machining conditions. With four factors, each having three levels, the full factorial design considers a total of 81 experiments that have to be carried out. Using input factor-level settings and adopting the Taguchi method, the experiments were reduced from 81 runs to 27 runs through an orthogonal design. In this study we aimed to check how reliable the results of artificial neural networks were when obtained based on a small input-output dataset, as in the case of applying the Taguchi methodology of planning a four-factor and three-level experiment, in which 27 trials were conducted. Furthermore, this paper considers the optimization of machining parameters for minimizing surface roughness in machining AA7075 aluminum alloy. The results show that ANNs can be successfully trained with small data and used to predict the arithmetic mean roughness. The best results were achieved by backpropagation multilayer feedforward neural networks using the BR algorithm for training.

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Section: Introductionmentioning

confidence: 99%

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

et al. 2022

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“…However, they have the limitation of only being able to accurately describe linear relationships [5]. On the other hand, GAs are a series of organized steps that describe the process to be followed for an evolving population from which the best one will be chosen, according to some criteria [13]. However, the evaluation of GAs compared to ANNs on highly complex problems can become too expensive in terms of time and resources.…”

Section: Introductionmentioning

confidence: 99%

“…For this reason ANNs have recently become the preferred model by most researchers looking to develop a model that establishes optimal machining conditions [32]- [35]. On the other hand, ANNs are extensively used for modeling the machining processes because of their efficiency to establish optimal conditions [8]- [10], [13]- [15], [26]- [31]. Among the different studies shown in Table 1 that used this technique to model the machining process, it was found that the accuracy of ANN models usually falls between 95% and 99%.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Machining Parameters for Product Quality and Productivity in Turning Process of Aluminum

Abolghasem

Mancilla-Cubides

2021

IyU

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Modern production process is accompanied with new challenges in reducing the environmental impacts related to machining processes. The turning process is a manufacturing process widely used with numerous applications for creating engineering components. Accordingly, many studies have been conducted in order to optimize the machining parameters and facilitate the decision-making process. This work aims to optimize the quality of the machined products (surface finish) and the productivity rate of the turning manufacturing process. To do so, we use Aluminum as the material test to perform the turning process with cutting speed, feed rate, depth of cut, and nose radius of the cutting tool as our design factors. Product quality is quantified using surface roughness (R_a) and the productivity rate based on material removal rate (MRR). We develop a predictive and optimization model by coupling Artificial Neural Networks (ANN) and the Particle Swarm Optimization (PSO) multi-function optimization technique, as an alternative to predict the model response (R_a) first and then search for the optimal value of turning parameters to minimize the surface roughness (R_a) and maximize the material removal rate (MRR). The results obtained by the proposed models indicate good match between the predicted and experimental values proving that the proposed ANN model is capable to predict the surface roughness accurately. The optimization model PSO has provided a Pareto Front for the optimal solution determining the best machining parameters for minimum R_a and maximum MRR. The results from this study offer application in the real industry where the selection of optimal machining parameters helps to manage two conflicting objectives, which eventually facilitate the decision-making process of machined products.

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“…Artificial neural networks (ANNs) model the complex nonlinear relationships between input and output parameters by observing datasets and identifying patterns, without the need to write explicit programs [ 4 ]. An ANN is inspired by the way biological nerves, such as the brain, work to solve problems, and the first artificial neuron was produced in 1943 by McCulloch and the logician Walter Pits [ 26 , 27 ]. The ANN and the genetic algorithm (GA) are important alternatives to be used in machining processes, due to their high complexity in optimizing cutting parameters.…”

Section: Introductionmentioning

confidence: 99%

“…They have become very popular in recent years due to their capability of learning nonlinear behavior [ 28 ], with many studies conducted. The authors in [ 27 ] studied ANNs to predict the material removal rate and surface roughness in the CNC milling of P20 steel and the results indicating a successful application. The authors in [ 9 ] used a backpropagation neural network (BPNN), and optimized the overall cutting performance during the high-speed turning of the Ti-6Al-4V alloy, and the results achieved a balance among all studied responses.…”

Section: Introductionmentioning

confidence: 99%

MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

et al. 2020

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This paper presents a study of the Ti-6Al-4V alloy milling under different lubrication conditions, using the minimum quantity lubrication approach. The chosen material is widely used in the industry due to its properties, although they present difficulties in terms of their machinability. A minimum quantity lubrication (MQL) prototype valve was built for this purpose, and machining followed a previously defined experimental design with three lubrication strategies. Speed, feed rate, and the depth of cut were considered as independent variables. As design-dependent variables, cutting forces, torque, and roughness were considered. The desirability optimization function was used in order to obtain the best input data indications, in order to minimize cutting and roughness efforts. Supervised artificial neural networks of the multilayer perceptron type were created and tested, and their responses were compared statistically to the results of the factorial design. It was noted that the variables that most influenced the machining-dependent variables were the feed rate and the depth of cut. A lower roughness value was achieved with MQL only with the use of cutting fluid with graphite. Statistical analysis demonstrated that artificial neural network and the experimental design predict similar results.

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Prediction of Surface roughness & Material Removal Rate for machining of P20 Steel in CNC milling using Artificial Neural Networks

Cited by 14 publications

References 4 publications

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

Neural-Network-Based Approaches for Optimization of Machining Parameters Using Small Dataset

Optimization of Machining Parameters for Product Quality and Productivity in Turning Process of Aluminum

MQL Strategies Applied in Ti-6Al-4V Alloy Milling—Comparative Analysis between Experimental Design and Artificial Neural Networks

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