Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

Özel, Tuğrul; Karpat, Yiğit

doi:10.1016/j.ijmachtools.2004.09.007

Cited by 558 publications

(249 citation statements)

References 36 publications

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“…The average surface roughness achieved by them was in the range of 0.165-0.475 µm. Ozel et al [25] used four factors and two levels -a total of 16 runs, which were replicated by them 16 times, to end up with 256 tests. They used a high-precision rigid CNC lathe 8 (Romi Centur 35E) for longitudinal hard turning of hardened AISI H13 steel bar and 16 inserts were used for each run having similar parameters and they arrived at a minimum average surface roughness of about 0.25 µm.…”

Section: Brief Review Of the Machining Studies Using Taguchi Methodsmentioning

confidence: 99%

Parametric design optimization of hard turning of AISI 4340 steel (69 HRC)

Rashid

Goel

Davim

et al. 2015

Int J Adv Manuf Technol

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Abstract:Continuous research endeavors on hard turning (HT), both on machine tools and cutting tools, have made the previously reported daunting limits easily attainable in the modern scenario. This presents an opportunity for a systematic investigation on finding the current attainable limits of hard turning using a CNC turret lathe. Accordingly, this study aims to contribute to the existing literature by providing the latest experimental results of hard turning of AISI 4340 steel (69 HRC) using a CBN cutting tool. An orthogonal array was developed using a set of judiciously chosen cutting parameters. Subsequently, the longitudinal turning trials were carried out in accordance with a welldesigned full factorial-based Taguchi matrix. The speculation indeed proved correct as a mirror finished optical quality machined surface (an average surface roughness value of 45 nm) was achieved by the conventional cutting method. Furthermore, Signal-to-noise (S/N) ratio analysis, Analysis of variance (ANOVA), and Multiple regression analysis were carried out on the experimental datasets to assert the dominance of each machining variable in dictating the machined surface roughness and to optimize the machining parameters. One of the key findings was that when feed rate during hard turning approaches very low (about 0.02mm/rev), it could alone be most significant (99.16%) parameter in influencing the machined surface roughness (Ra). This has, however also been shown that low feed rate results in high tool wear, so the selection of machining parameters for carrying out hard turning must be governed by a trade-off between the cost and quality considerations.

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Section: Brief Review Of the Machining Studies Using Taguchi Methodsmentioning

confidence: 99%

Parametric design optimization of hard turning of AISI 4340 steel (69 HRC)

Rashid

Goel

Davim

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…On the other hand, Özel and Karpat presented a systematic approach for choosing number of hidden layers and number of neurons in turning processes, by using the output parameters of Bayesian regularization algorithm [21].…”

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confidence: 99%

Indirect model for roughness in rough honing processes based on artificial neural networks

Sivatte-Adroer

Parra

Buj-Corral

et al. 2016

Precision Engineering

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In the present paper an indirect model based on neural networks is presented for modelling the rough honing process. It allows obtaining values to be set for different process variables (linear speed, tangential speed, pressure of abrasive stones, grain size of abrasive and density of abrasive) as a function of required average roughness Ra. A multilayer perceptron (feedforward) with a backpropagation (BP) training system was used for defining neural networks. Several configurations were tested with different strategies, number of layers, number of neurons and transfer function. Best configuration for the network was searched by means of two different methods, trial and error and Taguchi design of experiments (DOE). In both cases, best configuration corresponds to a single network with two hidden layers. Once best configuration was found, a network was defined for obtaining honing parameters as a function of required roughness parameters related to Abbott-Firestone curve, Rk, Rpk and Rvk.

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“…In the direct method [4], sensors directly measures the tool wear, such as optical scanning technique, electrical resistance, radioactive technique, measurement of tool geometry, change in work piece size, and analysis of tool wear particles in the chips. The difficulties of direct methods lead to the indirect measuring techniques to measure accessible process variables (machine tool vibrations, acoustic emission, temperature, noise etc.…”

Section: Introductionmentioning

confidence: 99%

Researches regarding cutting tool condition monitoring

2017

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Abstract. The paper main purpose is monitoring of tool wear in metal cutting using neural networks due to their ability of learning and adapting their self, based on experiments. Monitoring the cutting process is difficult to perform on-line because of the complexity of tool wear process, which is the most important parameter that defines the tool state at a certain moment. Most of the researches appraise the tool wear by indirect factors such as forces, consumed power, vibrations or the surface quality. In this case, it is important to combine many factors for increasing the accuracy of tool wear prediction and establish the admissible size of wear. For this, paper both the theoretical data obtained from FEM analyze and experimental ones are used and compared in order to appreciate the reliability of the results.

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Predictive modeling of surface roughness and tool wear in hard turning using regression and neural networks

Cited by 558 publications

References 36 publications

Parametric design optimization of hard turning of AISI 4340 steel (69 HRC)

Parametric design optimization of hard turning of AISI 4340 steel (69 HRC)

Indirect model for roughness in rough honing processes based on artificial neural networks

Researches regarding cutting tool condition monitoring

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