A comparison of machine learning methods for cutting parameters prediction in high speed turning process

Jurković, Zoran; Cukor, Goran; Brezočnik, Miran; Brajkovic, Tomislav

doi:10.1007/s10845-016-1206-1

Cited by 109 publications

(52 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In anticipation of the next sixth technology revolution, it is becoming an increasingly important technique for processing large data sets using artificial intelligence and the integration of artificial intelligence algorithms in automated production. Many previous investigations have been devoted towards developing prediction models for rough turning [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19]. Risbood et al [1] researched and produced models for forecasting roughness and dimensional deviation for dry and wet turning of mild steel rods.…”

Section: Introductionmentioning

confidence: 99%

“…Al Bahkali et al [15] studied the effect of feed, cutting depth, radius of curvature of the tool tip and the cutting speed on surface roughness in turning cast iron. Mia and Dhar [16] developed an artificial neural network (ANN) model to predict the average surface roughness in turning hardened steel EN 24 T. Jurkovic et al [17] compared three machine learning methods for predicting the high-speed turning observed parameters (surface roughness (Ra), cutting force (Fc), and the tool life (T)). Tootooni et al [18] reported surface roughness using a noncontact measurement method during the turning process.…”

Section: Introductionmentioning

confidence: 99%

“…Abbas [19] analyzed the effect of the feed rate, depth of cut and cutting speed on the surface roughness in turning high-strength steel. Though all the previously mentioned research [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19] provided prediction models of surface roughness, they failed to solve the problem of determining the optimal cutting parameters for the minimum surface roughness and maximum production rate.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

View full text Add to dashboard Cite

High-strength steels are used in various civilian and military products. The initial cost of the raw materials for these products is very high. The surface roughness of these products is extremely important during the finishing pass to be accepted during the final inspection. The surface roughness should conform to the required values stated on the design drawing. The paper presents the results of experiments in turning of highstrength steel featuring three factors-cutting speed V, feed rate f, and depth of cut t-on five levels (125 specimens). These were divided into 25 groups. Each of the five groups was subjected to one common machining speed. Each group was machined using five levels of cutting depth. Each depth was processed using five levels of feed rate. Tessa was used for examination of surface roughness. There is little modern research on machining high-strength steel. The high cost of this material compels us to look for the optimum turning conditions to provide for the specified roughness of surface Ra and the minimum machining time of unit volume T m . As a result of our study, an artificial neural network was designed in Matlab on the basis of the MLP 3-10-1 multilayer perceptron that allows us to predict Ra of the workpiece with ±2.14% accuracy within the range of the experimental cutting speed, depth of cut, and feed rate values. For the first time, a Pareto frontier was obtained for Ra and T m of the finished workpiece from high-strength steel using the artificial neural network model that was later used to determine the optimum cutting conditions. It is possible to integrate the suggested optimization algorithms into computer-aided manufacturing using Matlab.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Abbas

Pimenov

Ердаков

et al. 2017

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…SVM as a classification technique has its roots in SLT (Khemchandani & Chandra, 2009;Salahshoor, Kordestani, & Khoshro, 2010) and has shown promising empirical results in a number of practical manufacturing applications (Chinnam, 2002;Widodo & Yang, 2007) and works very well with high-dimensional data (Azadeh et al, 2013;Ben-hur & Weston, 2010;Salahshoor et al, 2010;Sun, Rahman, Wong, & Hong, 2004;Wu, 2010;Wuest, Irgens, & Thoben, 2014). Current literature suggests that the performance of SVM compared to other ML methods is still very competitive (Jurkovic, Cukor, Brezocnik, & Brajkovic, 2016).Another aspect of this approach is that it represents the decision boundary using a subset of the training examples, known as the support vectors.…”

Section: Supervised Machine Learning Algorithms In Manufacturing Applmentioning

confidence: 99%

Machine learning in manufacturing: advantages, challenges, and applications

Wuest

Weimer²,

Irgens

et al. 2016

Production & Manufacturing Research

770

502

View full text Add to dashboard Cite

The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

show abstract

“…The commonly used Multiple Regression Analyses are based on many different regression models [23][24][25][26][27][28] . Many efforts have been made in order to achieve a highly accurate multiple regression model 25,[27][28][29] . However, the widely used regression models are quite trivial and their accuracy is in many cases quite low 11 .…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Curing Cycle Optimization for Glass Fabric/Epoxy Composites Using Poisson Regression and Genetic Algorithm

et al. 2018

View full text Add to dashboard Cite

In this study, a multi-parameter design of experiments, using Taguchi method, has been conducted in order to investigate the optimum curing conditions for glass fabric/epoxy laminated composites, followed by a statistical analysis and genetic algorithm optimization. Heating rate a, temperature T 1 and duration h 1 were treated as independent variables in a L 25 Taguchi orthogonal array addressing five levels each. Tensile load and flexural strength were examined as pre-selected quality objectives. The results of the analysis of variance performed showed that the significant parameters for both tensile and flexural strength were temperature and duration, at a 95% confidence level. The estimation of the curing parameters for optimum tensile and flexural performance was achieved with an error considerably lower than 1%. The Poisson regression analysis was introduced to achieve a highly accurate regression model, with R 2 greater than 97% for both optimization criteria. Finally, these two regression models were converted into a two-fold function for maximizing both criteria, and used as fitness function for a multi-objective optimization genetic algorithm.

show abstract

A comparison of machine learning methods for cutting parameters prediction in high speed turning process

Cited by 109 publications

References 19 publications

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Minimization of turning time for high-strength steel with a given surface roughness using the Edgeworth–Pareto optimization method

Machine learning in manufacturing: advantages, challenges, and applications

Multi-Objective Curing Cycle Optimization for Glass Fabric/Epoxy Composites Using Poisson Regression and Genetic Algorithm

Contact Info

Product

Resources

About