Machined surface quality prediction models based on moving least squares and moving least absolute deviations methods

Svalina, Ilija; Sabo, Kristian; Šimunović, Goran

doi:10.1007/s00170-011-3353-z

Cited by 20 publications

(13 citation statements)

References 21 publications

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“…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%

“…Natarajan et al [6] reported the turning of brass C26000 material and the deduced prediction model of surface roughness using artificial neural network (ANN) based on Matlab. Svalina et al [7] analyzed the effect of the depth of cut, feed rate, and speed on the surface roughness, which is predicted by using neural networks. Abdullah et al [8] reported a model for predicting surface roughness obtained by turning AISI 4140 steel using ANN and the Taguchi method.…”

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%

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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

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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.

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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

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“…On the contrary, the on-machine measurement approach measures every machined workpiece with probes fixed on the machine tool through contactless methods, such as laser or image processing techniques [2] [3]. Hence, the on-machine measurement approach can measure every machined workpiece in a timely manner.…”

mentioning

confidence: 99%

A novel virtual metrology scheme for predicting machining precision of machine tools

Yang

Hung

Cheng

2013

2013 IEEE International Conference on Robotics and Automation

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Because virtual metrology (VM) can achieve real-time and on-line total inspection, it is a promising way for measuring machining precision of machine tools. However, the machining processes possess the characteristics of severe vibrations. Thus, how to effectively handle signals with low signal/noise ratios and extract key features from them is a challenging issue for successfully applying VM to the machine tools. In this paper, a novel VM scheme for predicting machining precision of machine tools is proposed based on several previously developed methods for data quality evaluation, model reliance evaluation, and machining precision prediction. Besides, for data preprocess, we propose a Wavelet-based de-noising method to improve the S/N ratio of sensor data. In addition, we base on the stepwise technique to develop an automatic feature selection method that can extract key features related to machining operations in time, frequency, and time-frequency domains, and can reduce the dimension of essential features. Testing results of a 3-axis CNC machine center machining standard workpieces show that the VMS can achieve the performance that the maximum average error of machining-precision conjecture is less than 2 um and the conjecture of 20 machining-precision items can be completed within 3.8 sec.

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“…The paper [15] analyses the influence of the depth of cut, feed rate, and number of revolutions on the surface roughness. Dweiri et al [16] and Palani and Natarajan [17] applied neural networks to model the surface roughness of face CNC milled aluminium.…”

Section: Introductionmentioning

confidence: 99%

Predicting the Surface Quality of Face Milled Aluminium Alloy Using a Multiple Regression Model and Numerical Optimization

Šimunović¹,

Šimunović²,

Šarić³

2013

Measurement Science Review

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The surface roughness is a very significant indicator of surface quality. It represents an essential exploitation requirement and influences technological time and costs, i.e. productivity. For that reason, the main objective of this paper is to analyse the influence of face milling cutting parameters (number of revolution, feed rate and depth of cut) on the surface roughness of aluminium alloy. Hence, a statistical (regression) model has been developed to predict the surface roughness by using the methodology of experimental design. Central composite design is chosen for fitting response surface. Also, numerical optimization considering two goals simultaneously (minimum propagation of error and minimum roughness) was performed throughout the experimental region. In this way, the settings of cutting parameters causing the minimum variability in response were determined for the estimated variations of the significant regression factors.

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Machined surface quality prediction models based on moving least squares and moving least absolute deviations methods

Cited by 20 publications

References 21 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

A novel virtual metrology scheme for predicting machining precision of machine tools

Predicting the Surface Quality of Face Milled Aluminium Alloy Using a Multiple Regression Model and Numerical Optimization

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