Optimization of cutting Parameters and Prediction of Ra &amp; MRR for machining of P20 Steel on CNC milling using Artificial Neural Networks

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

doi:10.1016/j.matpr.2018.09.010

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…The Ra parameter has also been used in multiple regression analysis to understand the influence of different parameters in the dressing (replenishing) grinding wheels [17]. Similarly, Neural Networks (NNs) have been used to model the relationship in Computer Numerical Control (CNC) steel milling between the resulting Ra and material removal rate when using different input parameters [18].…”

Section: Introductionmentioning

confidence: 99%

Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models

Milne,

Xie

2023

Preprint

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Control of surface texture in strip steel is essential to meet customer requirements during galvanizing and temper rolling processes. Traditional methods rely on post-production stylus measurements, while on-line techniques offer non-contact and real-time measurements of the entire strip. However, ensuring accurate measurement is imperative for their effective utilization in the manufacturing pipeline. Moreover, accurate on-line measurements enable real-time adjustments of manufacturing processing parameters during production, ensuring consistent quality and the possibility of closed-loop control of the temper mill.In this study, we leverage state-of-the-art machine learning models to enhance the transformation of on-line measurements into a significantly more accurate Ra surface roughness metric. By comparing a selection of data-driven approaches, including both deep learning and non-deep learning methods, to the close-form transformation, we evaluate their potential for improving surface texture control in temper strip steel manufacturing.

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Section: Introductionmentioning

confidence: 99%

Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models

Milne,

Xie

2023

Preprint

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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%

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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Artificial Intelligence-Based Neural Network Prediction Model for Predicting Multi-responses of Finishing Honing Process

Gupta¹,

Goyal²,

Kumar³

et al. 2022

Lecture Notes in Mechanical Engineering

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Optimization of cutting Parameters and Prediction of Ra & MRR for machining of P20 Steel on CNC milling using Artificial Neural Networks

Cited by 7 publications

References 4 publications

Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models

Steel Surface Roughness Parameter Calculations Using Lasers and Machine Learning Models

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

Artificial Intelligence-Based Neural Network Prediction Model for Predicting Multi-responses of Finishing Honing Process

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