Optimization of machining techniques — A retrospective and literature review

Aggarwal, Aman; Singh, Hari

doi:10.1007/bf02716704

Cited by 112 publications

(54 citation statements)

References 26 publications

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“…In order to minimize the number of runs or trials, optimize values of parameters, assess experimental error, make qualitative estimation of parameters, and to make inference regarding the effect of parameters on the characteristics of the process (Aggarwal and Singh, 2005), it is essential to adopt any of the techniques of design of experiment for the turning experiment. In this work the response surface methodology (RSM), based on central composite (CC) design of experiment (DOE), was selected for the modeling, prediction and optimization of a R and i V as functions of A, B and C in the turning experiment.…”

Section: Design and Analysis Of Experimentmentioning

confidence: 99%

“…As an obligation for processes to work properly in time and at all times, it is of necessity that machined component manufacturers adopt better approaches to ensure that high quality products and services are produced. This drive for quality, and sometimes performance, has motivated efforts leading to the search for optimization techniques, and a shift from the use of traditional to non-traditional techniques, such as reviewed in Aggarwal and Singh [1]), and in Kumar and Uppal [2]. Amongst the non-traditional techniques, the use of response surface methodology (RSM) has attracted the attention of many researchers.…”

Section: Introductionmentioning

confidence: 99%

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An Investigation on Effect of Depth of Cut, Feed Rate and Tool Nose Radius on Induced Vibration and Surface Roughness during Hard Turning of 41Cr4 Alloy Steel Using Response Surface Methodology

Izelu

Eze

2016

IJET

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This paper describes an aspect of a set of turning experiments performed in attempt to model, predict and optimize the machining induced vibration and surface roughness as functions of the machining, tool and work-piece variables during hard turning of 41Cr4 alloy special steel, with standard cutting tool, on a conventional lathe. Amongst others, the input variables of interest include the depth of cut, feed rate and tool nose radius. The response surface methodology, based on central composite design of experiment, was adopted, with analysis performed in Design Expert 9 software environment. Quadratic regression models were suggested, and proved significant by an analysis of variance, for the machining induced vibration of the cutting tool and surface roughness of the work-piece. They also have capability of being used for prediction within limits. Analysis of variance also showed the depth of cut, feed rate and tool nose radius have significant effect on the machining induced vibration and surface roughness. Whereas the depth of cut has dominant effect on the machining induced vibration, the tool nose radius has dominant effect on the surface roughness. The optimum setting of the depth of cut of 1.33095 mm, feed rate of 0.168695 mm/rev, and the tool nose radius of 1.71718 mm is required to minimize the machining induced vibration at 0.08 mm/s 2 and surface roughness at 6.056 µmm with a desirability of 0.830.

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Section: Design and Analysis Of Experimentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Investigation on Effect of Depth of Cut, Feed Rate and Tool Nose Radius on Induced Vibration and Surface Roughness during Hard Turning of 41Cr4 Alloy Steel Using Response Surface Methodology

Izelu

Eze

2016

IJET

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“…Techniques for optimizing cutting conditions can be divided into classical and modern techniques [11]. Classical techniques for optimizing machining conditions are derived from Taylor's tool life equation and include geometric programming, linear programming, goal programming, sequential unconstrained minimization technique and dynamic programming [11].…”

Section: Reverse Algorithm Proceduresmentioning

confidence: 99%

“…Classical techniques for optimizing machining conditions are derived from Taylor's tool life equation and include geometric programming, linear programming, goal programming, sequential unconstrained minimization technique and dynamic programming [11]. Modern techniques include fuzzy logic, scatter search technique, genetic algorithm, Taguchi technique and response surface methodology [11].…”

Section: Reverse Algorithm Proceduresmentioning

confidence: 99%

Reverse algorithm for determining the number of cutting passes and cutting conditions in axial turning

Tošić

Ćosić

2016

Teh. vjesn.

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REVERSE ALGORITHM FOR DETERMINING THE NUMBER OF CUTTING PASSES AND CUTTING CONDITIONS IN AXIAL TURNING Marina Tošić, Predrag ĆosićPreliminary notes To avoid production of low quality products and reduce unnecessary time wastage, algorithms for decision making are widely used in process planning. Usually decision of selecting cutting conditions (depth of cut, feed and cutting speed) is made by experience or using handbooks. Much effort during years was invested in finding optimizing algorithms for metal cutting processes. Algorithm given by G. Halevi does not include specific tool or machine for machining process and that is why it is called reverse algorithm. Taking in consideration boundary values of cutting conditions and their effects on surface roughness, algorithm finds maximum values of cutting conditions, giving preference to the feed rate and thus minimizing the number of cutting passes. Proposed algorithm was explained by data flow implemented in software package MATLAB and tested on the example of axial turning. The obtained results were compared to recommendations retrieved from Iscar's catalog and show that algorithm is suitable for determining appropriate number of cutting passes and cutting conditions. This paper is a contribution to the automated algorithms for decision making in process planning. Keywords: reverse algorithm, cutting conditions, axial turning Obrnuti postupak za određivanje broja prolaza i parametara obrade kod uzdužnog tokarenjaPrethodno priopćenje Kako bi se smanjila proizvodnja škarta i nepotrebno rasipanje vremena, algoritmi za donošenje odluka koriste se kod projektiranja tehnoloških procesa. Odabir parametara obrade (dubine reza, posmaka i brzine) uobičajeno se donosi na temelju iskustva ili upotrebom priručnika. Tijekom godina mnogo truda je uloženo u cilju pronalaska optimalnih algoritama za obradu metala. Algoritam koji je predložio G. Halevi ne uzima u obzir zadani alat ili stroj te se zbog toga naziva obrnutim postupkom. Uzimajući u obzir granične vrijednosti parametara obrade i njihov utjecaj na površinsku hrapavost, algoritam pronalazi maksimalne vrijednosti parametara, dajući prednost posmaku i time reducirajući broj prolaza kod obrade. Predloženi algoritam je prikazan pomoću dijagrama, implementiran u softverski paket MATLAB i testiran na primjeru uzdužnog tokarenja. Dobiveni rezultati su uspoređeni s preporukama iz Iscarovog kataloga te upućuju na to da je algoritam primjeren za određivanje broja prolaza i parametara obrade. Ovaj članak je doprinos automatiziranim algoritmima za donošenje odluka koji se koriste kod projektiranje tehnoloških procesa. Ključne riječi: obrnuti postupak, parametri obrade, uzdužno tokarenje

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“…In order to select optimal cutting parameters, manufacturing to obtain optimal cutting parameters, manufacturing industries have depended on the use of handbook based information which leads to decrease in productivity due to suboptimal use of machining capability. This causes high manufacturing cost and low product quality (Aggarwal & Singh, 2005). Hence, there is a need of a systematic methodological tool for optimization of parameters.…”

Section: Introductionmentioning

confidence: 99%

Parametric optimization during machining of AISI 304 Austenitic Stainless Steel using CVD coated DURATOMIC cutting insert

Kaladhar¹,

Subbaiah²,

Rao³

2012

ijiec

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In this work, Taguchi method is applied to determine the optimum process parameters for turning of AISI 304 austenitic stainless steel on CNC lathe. A Chemical vapour deposition (CVD) coated cemented carbide cutting insert is used which is produced by Duratomic TM technology of 0.4 and 0.8 mm nose radii. The tests are conducted at four levels of Cutting speed, feed and depth of cut. The influence of these parameters are investigated on the surface roughness and material removal rate (MRR). The Analysis Of Variance (ANOVA) is also used to analyze the influence of cutting parameters during machining. The results revealed that cutting speed significantly (46.05%) affected the machined surface roughness values followed by nose radius (23.7%). The influence of the depth of cut (61.31%) in affecting material removal rate (MRR) is significantly large. The cutting speed (20.40%) is the next significant factor. Optimal range and optimal level of parameters are also predicted for responses.

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Optimization of machining techniques — A retrospective and literature review

Cited by 112 publications

References 26 publications

An Investigation on Effect of Depth of Cut, Feed Rate and Tool Nose Radius on Induced Vibration and Surface Roughness during Hard Turning of 41Cr4 Alloy Steel Using Response Surface Methodology

An Investigation on Effect of Depth of Cut, Feed Rate and Tool Nose Radius on Induced Vibration and Surface Roughness during Hard Turning of 41Cr4 Alloy Steel Using Response Surface Methodology

Reverse algorithm for determining the number of cutting passes and cutting conditions in axial turning

Parametric optimization during machining of AISI 304 Austenitic Stainless Steel using CVD coated DURATOMIC cutting insert

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