Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RMS and desirability approach

Selaimia, Abdel-Ali; Yallese, Mohamed Athmane; Bensouilah, Hamza; Meddour, Ikhlas; Khattabi, Riad; Mabrouki, Tarek

doi:10.1016/j.measurement.2017.05.012

Cited by 59 publications

(27 citation statements)

References 32 publications

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“…Many researchers have developed surface roughness prediction models in face milling using AI (Srinivasa Pai et al 2002;Vosniakos 2002, 2003;Saglam and Unuvar 2003;Bruni et al 2008;El-Sonbaty et al 2008;Lela et al 2009;Muñoz-Escalona and Maropoulos 2010;Razfar et al 2011;Bharathi Raja and Baskar 2012;Grzenda et al 2012;Bajić et al 2012;Kovac et al 2013;Simunovic et al 2013;Grzenda and Bustillo 2013;Elhami et al 2013;Saric et al 2013;Rodríguez et al 2017;Simunovic et al 2016;Selaimia et al 2017;Svalina et al 2017). Srinivasa Pai et al (2002 presented an estimation of flank wear in face milling based on the radial basis function (RBF) of neural networks using acoustic emission signals, surface roughness, and cutting conditions (cutting speed and feed).…”

Section: Introductionmentioning

confidence: 99%

“…Simunovic et al (2016) presented a machined surface roughness investigation based on the features of a digital image such as spindle speed, feed per tooth, and cutting depth, but without considering tool wear; the digital image was produced following a milling operation of an aluminum alloy Al6060. Selaimia et al (2017) modeled the output responses, namely: surface roughness (Ra), cutting force (FC), cutting power (PC), specific cutting force (KS) and metal removal rate (MRR) during the face milling of the austenitic stainless steel X2CrNi18-9 with coated carbide inserts (GC4040). ANOVA was used for evaluating the influence of the cutting parameters: cutting speed (VC), feed per tooth and depth of cut (aP) on the output responses.…”

Section: Introductionmentioning

confidence: 99%

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Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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Nowadays, face milling is one of the most widely used machining processes for the generation of flat surfaces. Following international standards, the quality of a machined surface is measured in terms of surface roughness, Ra, a parameter that will decrease with increased tool wear. So, cutting inserts of the milling tool have to be changed before a given surface quality threshold is exceeded. The use of artificial intelligence methods is suggested in this paper for real-time prediction of surface roughness deviations, depending on the main drive power, and taking tool wear, V B into account. This method ensures comprehensive use of the potential of modern CNC machines that are able to monitor the main drive power, N , in real-time. It can likewise estimate the three parameters -maximum tool wear, machining time, and cutting power-that are required to generate a given surface roughness, thereby making the most efficient use of the cutting tool. A series of artificial intelligence methods are tested: random forest (RF), standard Multilayer perceptrons (MLP), Regression Trees, and radial-based functions. Random forest was shown to have the highest model accuracy, followed by regression trees, displaying higher accuracy than the standard MLP and the radial-basis function. Moreover, RF techniques are easily tuned and generate visual information for direct use by the process engineer, such as the linear relationships between process parameters and roughness, and thresholds for avoiding rapid tool wear. All of this information can be directly extracted from the tree structure or by drawing 3D charts plotting two process inputs and the predicted roughness depending on workshop requirements. Keywords

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

2017

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“…The process-parameter ranges are shown in Table 1, and were selected based on preliminary research work. 19 Fifteen sets of experiments were conducted for the microhardness and surface roughness with central composite face centred (CCF) design of the response surface methodology. The face milled diesel engine head is depicted in Figure 2.…”

Section: Experimental Setup and Designmentioning

confidence: 99%

“…In this research work, the selection of the method is considered on the desirability function (DF) approach, which allows the optimization with a multi-objective criterion. [18][19][20] This approach widely used by several researchers for reasons of its weighting flexibility, simplicity and insertion in statistical software. 21,22 The present research concentrates on a machinability characteristic study of a newly cast, A413, dieselengine-head aluminium alloy produced under optimal casting conditions by pressure die casting.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation of the microhardness and surface roughness of a pressure-die-casted A413 alloy on milling using the desirability approach

Raghunayagan¹,

Murugarajan²

2019

Mater. Tehnol.

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This research, focused on a newly casted A413 diesel-engine-head aluminium alloy produced under optimal casting conditions by pressure die casting, is also used for a machinability characteristics study. The experiments were methodically conducted based on a central composite face-centred design of the response surface methodology to understand the influence of milling process parameters such as cutting speed (m/min), feed rate (mm/tooth) and depth of cut (mm) on the microhardness (HV) and surface roughness (μm). The significance of the responses was validated using the analysis of variance. Multi-objective optimization using the desirability function was adopted to optimize the process parameters that simultaneously maximize the microhardness and minimize the surface roughness. The scanning electron microscopy (SEM) of the machined surface results shows that there is certain surface damage that reduces the quality of final surface components, such as scratches, feed line damage and inclusion of the tool material. Corresponding to the highest desirability, the optimal values of the process parameters were found to be 215.644 m/min for the cutting speed, 0.230 mm/tooth for the feed rate and 1.043 mm for the depth of cut.

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“…A so-called desirability approach was applied in Reference [15] for the modelling of the following output responses by Response Surface Methodology (RMS): surface roughness (Ra), cutting force (Fc), cutting power (Pc), specific cutting force (Ks) and metal removal rate (MRR), during the face milling of the austenitic stainless steel X2CrNi18-9 with coated carbide inserts (GC4040). A full factorial design (L27) is selected for the experiments and ANOVA is used in order to evaluate the influence of the cutting parameters of cutting speed (v c ), feed per tooth and depth of cut (a p ) on the out-put responses.…”

Section: Introductionmentioning

confidence: 99%

Effects of Setting Errors (Insert Run-Outs) on Surface Roughness in Face Milling When Using Circular Inserts

Felhő

Kundrák

2018

Machines

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In face milling, the roughness of the machined surface varies due to the movement of the cutting edge. Changes in roughness parameter values in the axis of rotation (symmetry plane) have been examined at a constant depth of cut for symmetrical milling. In this paper, the effect of increasing feed per tooth on the topography of the surface is studied in fly-cutting and in multi-point face milling. The study takes into account the axial run-out of the inserts. Theoretical roughness values were modelled, the real values were tested in experiments and in both cases the impact of the run-out of the cutting edges and the change of the chip cross-section were also taken into account. Based on the performed experiments it can be stated that the accuracy of the introduced roughness prediction method increases with the increase in feed and therefore the application of the method in the case of high-feed milling is particularly effective. The results have also shown that the run-out of the insert significantly effects the roughness of the milled surfaces and therefore the measurement and minimization of these setting errors is essential.

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Modeling and optimization in dry face milling of X2CrNi18-9 austenitic stainless steel using RMS and desirability approach

Cited by 59 publications

References 32 publications

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Artificial intelligence for automatic prediction of required surface roughness by monitoring wear on face mill teeth

Experimental investigation of the microhardness and surface roughness of a pressure-die-casted A413 alloy on milling using the desirability approach

Effects of Setting Errors (Insert Run-Outs) on Surface Roughness in Face Milling When Using Circular Inserts

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