Parametric appraisal of fused deposition modelling process using the grey Taguchi method

Sood, Anil K.; Ohdar, Rajkumar; Mahapatra, Siba Sankar

doi:10.1243/09544054jem1565

Cited by 145 publications

(89 citation statements)

References 14 publications

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“…However, to date, it is still a very challenging task to build accurate physical models because it requires in-depth knowledge of FDM process and many factors can affect the model accuracy, such as anisotropic material properties [49,63], fatigue effects [31], etc. Given the limitations, researchers also employed experiment-based empirical modeling approaches [14,28,38,[45][46][47]. Sensing techniques have also been used to ensure the quality of FDM built parts [11,27,40,41].…”

Section: Machine Condition and Process Monitoringmentioning

confidence: 99%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

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Fused deposition modeling (FDM) is one of the most popular additive manufacturing technologies for fabricating prototypes with complex geometry and different materials. However, current commercial FDM machines have the limitations in process reliability and product quality. In order to overcome these limitations and increase the levels of machine intelligence and automation, machine conditions need to be monitored more closely as in closedloop control systems. In this study, a new method for in situ monitoring of FDM machine conditions is proposed, where acoustic emission (AE) technique is applied. The proposed method allows for the identification of both normal and abnormal states of the machine conditions. The time-domain features of AE hits are used as the indicators. Support vector machines with the radial basis function kernel are applied for state identification. Experimental results show that this new method can potentially serve as a nonintrusive diagnostic and prognostic tool for FDM machine maintenance and process control.

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Section: Machine Condition and Process Monitoringmentioning

confidence: 99%

In situ monitoring of FDM machine condition via acoustic emission

Hai-xi

Wang

2015

Int J Adv Manuf Technol

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“…In order to overcome this challenge, a grey relational grade approach [42][43][44] is proposed. Grey relational analysis is applied in various fields, such as decision-making [44], machining science [45][46][47], manufacturing strategies [48], and product development strategies [49][50]. A grey relational grade approach is one where two alternatives are compared, based on global and local sets of data points.…”

Section: Adopted Multi-criteria Approachmentioning

confidence: 99%

Multi-Criteria Grey Relational Approach to Evaluating Reconfigurable Manufacturing Configurations

Rehman

2017

SAJIE

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In response to highly dynamic markets, manufacturing industries need alternative feasible manufacturing strategies. The strategy currently adopted is to reconfigure the existing manufacturing system. The conditions to reconfigure existing configurations change from time to time. An attempt is made to identify and apply an approach to evaluate these configurations. The paper proposes a grey relational decision-making approach. The approach takes into account multiple performance measures. The ProModel 6.2 simulation platform is adopted to examine the performance of each feasible alternative manufacturing configuration. OPSOMMINGVervaardigingsindustrieë het alternatiewe, uitvoerbare vervaardiging-strategieë nodig om by die hoogs dinamiese markte aan te pas. Die huidige strategie is om die bestaande vervaardigingstelsel te herkonfigureer. Die omstandighede om bestaande konfigurasies te herkonfigureer verander met tyd. ŉ Poging word gemaak om ŉ benadering te identifiseer en toe te pas om hierdie konfigurasies te evalueer. ŉ Grys-verwante besluitmakingsbenadering word voorgestel. Die benadering neem veelvuldige vertoningsmaatstawwe in ag. Die ProModel 6.2 simulasie platform is gebruik om die vertoning van elke uitvoerbare alternatiewe vervaardiging konfigurasie te ondersoek.

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“…For instance, (Lee, et al, 2005) and (Laeng, et al, 2006) investigated the elasticity performance of ABS material. Similarly, (Anitha, et al, 2001) optimized the FDM process parameters improving the surface roughness of build parts, while (Gregorian, et al, 2001), (Sood, et al, 2010) …”

Section: Imentioning

confidence: 99%

Improving the Dimensional Accuracy And Surface Roughness of Fdm Parts Using Optimization Techniques

Venkatasubbareddy¹,

Siddikali²,

Saleem³

2016

IOSR

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Fused deposition modeling (FDM) is unique of the rapid prototyping methods that use the plastics materials for example ABS (Acrylonitrile -butadiene -styrene) Keywords: Rapid prototyping, Fused deposition modeling, Grey-Taguchi. I.Introduction Rapid Prototyping (RP) is an additive manufacturing technology that automatically builds functional assemblies using CAD model of the part. In general, RP process includes five basic steps to build a part model automatically: (a) Create a CAD model of the design (b) Convert the CAD model to STL format (c) Slice the STL file into thin cross-sectional layers (d) Construct the model one layer atop another (e) Clean and finish the model. Surface roughness is the key property of RP build parts. Surface finish is considered as a vital feature and parts must be prepared in line with the product finishing specificationsThe surface finish of parts obtained through these manufacturing processes is important, especially in cases where the components are in contact with other elements or materials in their service life. For example building moulds to produce components by means of Solid Free Form Manufacturing Processes, or cases of other functional components where their surface characteristics will have a considerable effect on their mechanical properties such as fatigue, wear, and corrosion. Therefore, it is important to have prior knowledge, by means of conceptual models, of the manufacturing process parameters that allow the user to predict the surface finish of manufactured prototypes.Fused Deposition Modeling (FDM) is a leading RP technology that is used for fabricating solid prototypes in various materials directly from a computer-aided design (CAD) data. The quality and the strength of the FDM build parts are dependent essentially on the process parameters. In order to understand the performance and the behavior of FDM build parts, the influence of the process parameters on outcome quality of the build parts must be studied. Earlier studies (Mahapatra, et al, 2009), (Ahn, et al, 2002) have reported that FDM parameters such as layer thickness, air gap, raster width, and raster orientation were significantly impacting the quality characteristics of build parts. The FDM systems available in the market are different in their build speed, build volume, range of parameter settings and build materials (Masood, et al, 2010). In relevant empirical studies, parametric optimization was used to develop the quality characteristics of FDM parts or the process performance where the number of FDM process parameters were studied and optimized. For instance, (Lee, et al, 2005) and (Laeng, et al, 2006) investigated the elasticity performance of ABS material. Similarly, (Anitha, et al, 2001) optimized the FDM process parameters improving the surface roughness of build parts, while (Gregorian, et al., 2001), (Sood, et al., 2010) have looked into the dimensional accuracy of FDM parts

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Parametric appraisal of fused deposition modelling process using the grey Taguchi method

Cited by 145 publications

References 14 publications

In situ monitoring of FDM machine condition via acoustic emission

In situ monitoring of FDM machine condition via acoustic emission

Multi-Criteria Grey Relational Approach to Evaluating Reconfigurable Manufacturing Configurations

Improving the Dimensional Accuracy And Surface Roughness of Fdm Parts Using Optimization Techniques

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