Optimization of cutting conditions using artificial neural networks and the Edgeworth-Pareto method for CNC face-milling operations on high-strength grade-H steel

Abstract Computer Numerical Control (CNC) face milling is commonly used to manufacture products from high-strength grade-H steel in both the automotive and the construction industry. The various milling operations for these components have key performance indicators: accuracy, surface roughness (Ra), and machining time for removal of a unit volume min/cm3 (Tm). The specified surface roughness values for machining each component is achieved based on the prototype specifications. However, poor adherence to specifications can result in the rejection of the machined parts, implying extra production costs and raw material wastage. An algorithm using an artificial neural network (ANN) with the Edgeworth-Pareto method is presented in this paper to optimize the cutting parameter in CNC face-milling operations. The set of parameters are adjusted to improve surface roughness and minimal unit-volume material removal rates, thereby reducing production costs and improving accuracy. An ANN algorithm is designed in Matlab, based on a 3–10-1 Multi-Layer Perceptron (MLP), which predicts the Ra of the workpiece surface to an accuracy of ± 5.78% within the range of the experimental angular spindle speed, feed rate, and cutting depth. An unprecedented Pareto frontier for Ra and Tm was obtained for the finished grade-H steel workpiece using an ANN algorithm that was then used to determine optimized cutting conditions. Depending on the production objective, one or the other of two sets of optimum machining conditions can be used: the first one sets a minimum cutting power, while the other sets a maximum Tm with a slight increase (under 5%) in milling costs.

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Prediction of Surface Roughness Profiles for Milling Process with Fractal Parameters Based on LS-SVM

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.97-101.1186 ◽

2010 ◽

Vol 97-101 ◽

pp. 1186-1193 ◽

Cited By ~ 2

Author(s):

Ben Gan ◽

Yi Jian Huang ◽

Gui Xia Zheng

Keyword(s):

Surface Roughness ◽

Cutting Parameters ◽

Milling Process ◽

Face Milling ◽

Cutting Conditions ◽

Vertical Scaling ◽

Support Vector ◽

Fractal Function ◽

Milling Operations ◽

Fractal Parameters

Least squares support vector machines (LS-SVM) were developed for the analysis and prediction of the relationship between the cutting conditions and the corresponding fractal parameters of machined surfaces in face milling operation. These models can help manufacturers to determine the appropriate cutting conditions, in order to achieve specific surface roughness profile geometry, and hence achieve the desired tribological performance (e.g. friction and wear) between the contacting surfaces. The input parameters of the LS-SVM are the cutting parameters: rotational speed, feed, depth of milling. The output parameters of the LS-SVM are the corresponding calculated fractal parameters: fractal dimension D and vertical scaling parameter G. The LS-SVM were utilized successfully for training and predicting the fractal parameters D and G in face milling operations. Moreover, Weierstrass-Mandelbrot（W–M ）fractal function was integrated with the LS-SVM in order to generate an artificially fractal predicted profiles at different milling conditions. The predicted profiles were found statistically similar to the actual measured profiles of test specimens and there is a relationship between the scale-independent fractal coefficients(D and G).

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Analysis of the Behaviour Effect of Face Cutting Edge Inserts on Surface Roughness when Milling Steels with MQL Lubrication

Materials Science Forum ◽

10.4028/www.scientific.net/msf.526.25 ◽

2006 ◽

Vol 526 ◽

pp. 25-30

Author(s):

Xavier Salueña Berna ◽

Jose Antonio Ortiz Marzo ◽

Jasmina Casals Terré

Keyword(s):

Experimental Study ◽

Surface Roughness ◽

Face Milling ◽

Cutting Edge ◽

Cutting Conditions ◽

Surface Appearance ◽

Milling Operations ◽

Comparison Of The Results ◽

Milling Tools

The main objectives of this work are the study of the obtained surface roughness on steels, using face cutting edge inserts milling tools in finishing face milling operations with microlubrication (MQL), and comparison of the results obtained with the widely-used radius inserts. This experimental study analyzes the roughness and surface appearance obtained with both sort of inserts. The interest about this study is to determine the steel types and the optimal cutting conditions for milling with this face cutting edge inserts. Another result analysed is the utility of the MQL implementation compared to the dry system.

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Improvement of surface roughness models for face milling operations through dimensionality reduction

Integrated Computer-Aided Engineering ◽

10.3233/ica-2012-0398 ◽

2012 ◽

Vol 19 (2) ◽

pp. 179-197 ◽

Cited By ~ 14

Author(s):

Maciej Grzenda ◽

Andres Bustillo ◽

Guillem Quintana ◽

Joaquim Ciurana

Keyword(s):

Surface Roughness ◽

Dimensionality Reduction ◽

Face Milling ◽

Milling Operations

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Experimental Study on the Relationship between Surface Roughness and Cutting Parameters when Face Milling High Strength Steel

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.139-141.782 ◽

2010 ◽

Vol 139-141 ◽

pp. 782-787

Author(s):

Yue Ding ◽

Wei Liu ◽

Xi Bin Wang ◽

Li Jing Xie ◽

Jun Han

Keyword(s):

Surface Roughness ◽

Feed Rate ◽

High Strength Steel ◽

Cutting Speed ◽

High Strength ◽

Face Milling ◽

Depth Of Cut ◽

Radial Depth ◽

The Relationship ◽

Axial Depth

In this study, surface roughness generated by face milling of 38CrSi high-strength steel is discussed. Experiments based on 24 factorial design and Box-Behnken design method are conducted to investigate the effects of milling parameters (cutting speed, axial depth of cut and radial depth of cut and feed rate) on surface roughness, and a second-order model of surface roughness is established by using surface response methodology (RSM); Significance tests of the model are carried out by the analysis of variance (ANOVA). The results show that the most important cutting parameter is feed rate, followed by radial depth of cut, cutting speed and axial depth of cut. Moreover, it is verified that the predictive model possesses highly significance by the variance examination at a level of confidence of 99%. And the relationship between surface roughness and the important interaction terms is nonlinear.

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Study on Surface Roughness in Micro End Milling of Mold Material

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.325.594 ◽

2011 ◽

Vol 325 ◽

pp. 594-599 ◽

Cited By ~ 6

Author(s):

Hiroo Shizuka ◽

Koichi Okuda ◽

Masayuki Nunobiki ◽

Yasuhito Inada

Keyword(s):

Surface Roughness ◽

End Milling ◽

Milling Process ◽

Cutting Conditions ◽

Mold Material ◽

Depth Of Cut ◽

Milling Operations ◽

Radial Depth ◽

Micro End Milling ◽

End Milling Process

The effects of cutting conditions on the surface roughness in a micro-end-milling process of a mold material are described in this paper. Micro-end-milling operations were performed under different cutting conditions such as feed rate and depth of cut, in order to investigate the factors that had the greatest influence on the finished surface during micro-end-milling. It was revealed that the surface roughness begins to deteriorate when the radial depth of the cut exceeds the tool radius. In addition, it was found that this phenomenon is peculiar to micro-end-milling processes.

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Artificial Neural Networks for Surface Roughness Prediction when Face Milling Al 7075-T7351

Journal of Materials Engineering and Performance ◽

10.1007/s11665-009-9452-4 ◽

2009 ◽

Vol 19 (2) ◽

pp. 185-193 ◽

Cited By ~ 21

Author(s):

Patricia Muñoz-Escalona ◽

Paul G. Maropoulos

Keyword(s):

Neural Networks ◽

Surface Roughness ◽

Artificial Neural Networks ◽

Face Milling ◽

Surface Roughness Prediction ◽

Al 7075 ◽

Roughness Prediction ◽

Artificial Neural

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Monitoring tool wear and surface roughness in the face milling process of high-strength compacted graphite cast irons

Journal of the Brazilian Society of Mechanical Sciences and Engineering ◽

10.1007/s40430-021-02897-7 ◽

2021 ◽

Vol 43 (4) ◽

Author(s):

L. R. R. da Silva ◽

P. H. P. França ◽

C. L. F. Andrade ◽

R. B. Da Silva ◽

W. L. Guesser ◽

...

Keyword(s):

Surface Roughness ◽

Tool Wear ◽

High Strength ◽

Milling Process ◽

Face Milling ◽

Monitoring Tool ◽

Cast Irons ◽

Compacted Graphite ◽

The Face

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Experimental study on the chip morphology, tool–chip contact length, workpiece vibration, and surface roughness during high-speed face milling of A6061 aluminum alloy

Proceedings of the Institution of Mechanical Engineers Part B Journal of Engineering Manufacture ◽

10.1177/0954405419863221 ◽

2019 ◽

Vol 234 (3) ◽

pp. 610-620 ◽

Cited By ~ 4

Author(s):

Thi-Hoa Pham ◽

Duc-Toan Nguyen ◽

Tien-Long Banh ◽

Van-Canh Tong

Keyword(s):

Surface Roughness ◽

Aluminum Alloy ◽

Feed Rate ◽

High Speed ◽

Cutting Speed ◽

Chip Morphology ◽

Contact Length ◽

Face Milling ◽

Cutting Conditions ◽

Workpiece Vibration

In this study, experiments of high-speed face milling of A6061 aluminum alloy with a carbide insert milling cutter under dry cutting conditions were conducted. The contact length between tool and chip, the workpiece vibration amplitude, and the arithmetic average surface roughness were measured under varying cutting conditions (cutting speed, feed rate, and depth of cut). The characteristics of chip morphology were observed using scanning electron microscope. Experimental results showed that the increasing cutting speed reduced the tool–chip contact length, the workpiece vibration, and the surface roughness. The tool–chip contact length, the workpiece vibration, and the surface roughness were all increased with increasing cutting depth and feed rate. The results of chip morphology showed that the chips with serrated form were generated under high-speed cutting conditions. Moreover, scratch lines, plastic deformation cavities, and local molten chip material were observed on the slide chip surface.

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Optimizing Surface Roughness in Face Milling Using a New Meta-Heuristic Method of Harmony Search

ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis, Volume 4 ◽

10.1115/esda2010-24217 ◽

2010 ◽

Author(s):

M. R. Razfar ◽

R. Farshbaf Zinati ◽

M. Haghshenas

Keyword(s):

Neural Network ◽

Surface Roughness ◽

Artificial Neural Network ◽

Stainless Steel ◽

Search Algorithm ◽

Harmony Search ◽

Harmony Search Algorithm ◽

Face Milling ◽

Statistical Experimental Design ◽

Artificial Neural

The focus of this study is on a new approach for determination of the optimal cutting parameters leading to minimum surface roughness in face milling of X20Cr13 stainless steel by coupling Neural Network (NN) and Harmony Search (HS) algorithm. In this regard, advantages of statistical experimental design technique, experimental measurements, artificial neural network and Harmony Search algorithm were exploited in an integrated manner. For this purpose, numerous experiments on X20Cr13 stainless steel were conducted to obtain surface roughness values. A predictive model for surface roughness was created using a feed forward artificial neural network exploiting experimental data. The optimization problem was solved by Harmony Search algorithm. Additional experiments were performed to validate optimum surface roughness value predicted by HS algorithm. From the obtained results, it is clearly seen that the Harmony Search algorithm is an efficient and accurate method in approaching the global minimum of surface roughness in face milling.

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