Multi-Quality Prediction Model of CNC Turning Using Back-Propagation Network

Lan, Tian-Syung; Lo, Chih-Yao; Wang, Ming-Yung; Yu, Yen-An

doi:10.3923/itj.2008.911.917

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Ozel et al (2007) reported significant enhancement in tool life for the case of low feed rate along with low cutting speed. Lan et al (2008) considered four cutting parameters viz. feed rate, cutting speed, depth of cut and nose runoff varied in three levels for predicting the surface roughness of CNC turned components.…”

Section: Introductionmentioning

confidence: 99%

Influence of machining parameters on surface roughness and dry friction

Kumar¹,

Kumar²

2016

10.5267/j.esm

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Dry friction depends on the surface topography which, in turn, is governed by machining parameters in addition to several other factors,. Therefore, in order to establish a qualitative relationship among these factors, the surface roughness and coefficient of static friction are measured for specimens machined on lathe and shaper machines with different values of machining parameters. For the case of lathe, the measured Ra value is found to increase with increase in feed rate and depth of cut, whereas, a marginal decrease is observed with increasing spindle speed. Similar results with respect to cutting speed have been obtained for the case of shaper machine. On the other hand, the coefficient of static friction, measured on steel substrate using inclined plane method, is found to decrease with increasing Ra values for both the specimen types.

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Section: Introductionmentioning

confidence: 99%

Influence of machining parameters on surface roughness and dry friction

Kumar¹,

Kumar²

2016

10.5267/j.esm

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“…Many works for process modeling between the machining performance and process parameters have been conducted. Lan et al [2] developed the multi-quality prediction model from four process parameters (cutting depth, feed rate, cutting speed and tool nose runoff of CNC turning) using back-propagation neural network. Latha et al [3] developed the prediction model of surface roughness in drilling of composite materials from different cutting conditions (spindle speeds, feed rates, and drill diameters) using fuzzy logic rule-based modeling method for the CNC drilling machine.…”

Section: Introductionmentioning

confidence: 99%

A methodology for multiple performances optimization of computer numerical controlled (CNC) machining process based on design of experiment, multi-criteria decision-making and multiple regression model

Ryang,

Yang,

Kuon

et al. 2024

Int J Interact Des Manuf

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This paper proposes multiple performances optimization methodology for computer numerical controlled (CNC) machining based on Taguchi method, multi-criteria decision making (MCDM) and multiple regression (MR) model. It consists of the following steps: (1) setting levels of process parameters and selecting appropriate orthogonal array (OA), ( 2) arranging the parameters on OA and measuring machining performance values at every trials, (3) calculating comprehensive performance (CP) by integrating the multiple performances using a reasonable MCDM, (4) developing MR model between CP and process parameters, (5) analyzing influence of process parameters using correlation analysis and Taguchi method, and (6) determining optimal process parameters using Taguchi and grid search methods. The methodology is applied to determine optimal turning process parameters for optimizing multiple machining performances in the high speed CNC turning of AISI P20 tool steel. It may be widely applicable to solve many practical multiple performances optimization problems in manufacturing.

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“…Reddy et al [17] adopted multiple regression model and artificial neural network to deal with surface roughness prediction model for machining of aluminium alloys by CNC turning. Lan et al [18] considered four cutting parameters: speed, feed, depth of cut, and nose runoff varied in three levels for predicting the surface roughness of CNC turned product. Thamma [19] constructed the regression model to find out the optimal combination of process parameters in turning operation for Aluminium 6061 work pieces.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Optimization of Correlated Multiple Surface Quality Characteristics of Mild Steel Turned Product

Datta¹,

Mahapatra²

2010

IIM

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Present work highlights application of utility theory combined with Principal Component Analysis (PCA) and Taguchi’s robust design for simultaneous optimization of correlated multiple surface quality characteristics of mild steel machined product prepared by straight turning operation. The study aims at evaluating the most favorable process environment followed by an optimal parametric combination for achieving high surface quality. Traditional Taguchi based hybrid optimization approaches rely on the assumption that quality indices are uncorrelated or independent. But it is felt that, in practice, there may be some correlation among various quality indices (responses) under consideration. To overcome this limitation of Taguchi approach, the present study proposes application of PCA to convert correlated responses into uncorrelated quality indices called principal components. Finally based on utility theory, Taguchi method has been applied to solve this optimization problem. The study demonstrates detailed methodology and concludes robustness and flexibility of the proposed optimization technique and validates its effectiveness through a case study in which correlated multiple response characteristics of turning operation have been optimized

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Multi-Quality Prediction Model of CNC Turning Using Back-Propagation Network

Cited by 7 publications

References 11 publications

Influence of machining parameters on surface roughness and dry friction

Influence of machining parameters on surface roughness and dry friction

A methodology for multiple performances optimization of computer numerical controlled (CNC) machining process based on design of experiment, multi-criteria decision-making and multiple regression model

Simultaneous Optimization of Correlated Multiple Surface Quality Characteristics of Mild Steel Turned Product

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