Optimization and characterization of dissimilar friction stir welded DP600 dual phase steel and AA6082-T6 aluminium alloy sheets using TOPSIS and grey relational analysis

Sameer, Mohammad; Birru, Anil Kumar

doi:10.1088/2053-1591/aafba4

Cited by 12 publications

(8 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Literature overviews of the optimization of FSW parameters for aluminum and its alloys , magnesium alloys [47][48][49][50][51][52] , steel and its alloys [53,54] , copper and its alloys (i.e., brass) [55,56] , dissimilar joints of aluminum to copper and their alloys [57][58][59][60][61][62][63][64][65] , aluminum alloys to steel alloys [66][67][68][69][70][71][72] , aluminum alloys to magnesium alloys [73] , and steel alloys to copper [74] are provided in Tables 1-8. It demonstrates the selected parameters in their optimization studies, percentage 3/20 https://www.doi.org/10.53964/jmim.2022005 of contributions % obtained, arrays used, alloys, thicknesses of materials, statistical DOE methods performed, and the efficiencies of responses (properties) acquired in these investigations.…”

Section: Data Collectionmentioning

confidence: 99%

“…Solid-state techniques such as FSW are available to overcome various flaws related to fusion processes, such as hot cracking, distortion, and porosity. FSW performs under the melting temperature, about 10-20%, of the materials [1] and has been vastly utilized for welding similar or dissimilar metals for aluminum and its alloys , titanium alloys [46] , magnesium alloys [47][48][49][50][51][52] , steel and its alloys [53,54] , copper and its alloys (i.e., brass) [55,56] , dissimilar joints of aluminum to copper or brass [57][58][59][60][61][62][63][64][65] , aluminum to steel [66][67][68][69][70][71][72] , aluminum to magnesium [73] , and steel to copper [74] . These metals are regarded as hard to weld by traditional fusion processes.…”

Section: Introductionmentioning

confidence: 99%

“…This study firstly ranked the parameters separately of each metal joining group based on the types of metals. Eight sets were selected, namely aluminum and its alloys , magnesium alloys [47][48][49][50][51][52] , steel and its alloys [53,54] , copper and its alloys (i.e., brass) [55,56] , dissimilar joints of aluminum to copper and their alloys [57][58][59][60][61][62][63][64][65] , aluminum alloys to steel alloys [66][67][68][69][70][71][72] , aluminum alloys to magnesium alloys [73] , and steel alloys to copper [74] . Each metal group had different combinations of optimized parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Analytic Hierarchy Process for a General Ranking of Friction Stir Welding Parameters Optimized Through the Design of Experimental Arrays

2022

J Mod Ind Manuf

View full text Add to dashboard Cite

Objective: This study aims to rank friction stir welding parameters using the analytic hierarchy process (AHP). Methods:The proposed approach includes two phases to achieve a general ranking of the parameters through the separate estimation of their weights on each metal type and aggregation. The criteria used in the AHP for ranking are the contribution percentages and the degrees of the parameters obtained from the analysis of variance of experimental studies. The data were obtained from literature that used the design of experimental arrays to optimize these parameters on multiple properties and many similar and dissimilar metals. Fourteen parameters were assigned in the present analysis, namely rotational speed (RS), traverse speed (TS), axial force (AF), pin profile (PP), tilt angle (TA), the ratio between shoulder diameter and pin diameter (D/d ratio), shoulder diameter (SD), pin diameter (Pd), plunge depth (PD), pin length (PL), tool offset (TO), shoulder concavity (SC), number of passes (NOP) and base metal side (BMS). Results:The parameters were classified into highly significant, moderately significant, and insignificant based on their rankings. The RS, TS, TA, and PP were the highest-ranking parameters with a total contribution of 83.4% to outputs. The insignificant parameters included the D/d ratio, BMS, SC, AF, and Pd, with a low total contribution of 14%. The moderately significant parameters were the TO, NOP, SD, PD, and PL. Conclusion:The above-mentioned significant parameters could be controlled to maximize the response. The failure to achieve the target specified in the standard by controlling only highly significant parameters signifies a need for further modification of the FSW process. Therefore, parameters of moderate significance are potential parameters that need to be controlled to achieve the target. To limit the selection between these parameters, reference should be made to the type of metal used and then the parameters with the greatest impact on the metal are selected, i.e. controlling PL for aluminum, NOP, and SD for magnesium, and TO for aluminum-to-steel and aluminum-to-copper.

show abstract

Section: Data Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Analytic Hierarchy Process for a General Ranking of Friction Stir Welding Parameters Optimized Through the Design of Experimental Arrays

2022

J Mod Ind Manuf

View full text Add to dashboard Cite

show abstract

“…It is based on the principle that the best alternative must have the shortest distance from the positive ideal solution (PIS) and the farthest distance from the negative ideal solution (NIS) [1,2]. The TOPSIS has been widely used in multi attribute decision making (MADM) problems such as materials selection [2][3][4][5][6][7]. Won-Chol Yang et al proposed the materials selection method using TOPSIS with some popular normalization method and applied it to select the optimal boron based tribological coating material with high wear resistance and adhesion among a set of available coating materials [2].…”

Section: Introductionmentioning

confidence: 99%

“…K Thirumavalavan et al applied the TOPSIS and Taguchi analysis to analyze the influence of process parameters of severe surface mechanical treatment process on the surface properties of AA7075 T651 [4]. M D Sameer et al applied the TOPSIS and grey relational analysis to optimize and characterize the dissimilar friction stir welded DP600 dual phase steel and AA6082-T6 aluminium alloy sheets [5]. R A Ramnath et al reviewed the MADM techniques such as TOPSIS for optimizing the machining parameters of composites [6].…”

Section: Introductionmentioning

confidence: 99%

Materials selection method using improved TOPSIS without rank reversal based on linear max-min normalization with absolute maximum and minimum values

Yang¹,

Choe²,

Kim³

et al. 2022

Mater. Res. Express

View full text Add to dashboard Cite

Technique for order preference by similarity to ideal solution (TOPSIS) is a well-known multi attribute decision making (MADM) method and it has been widely used in materials selection. However, the main drawback of the traditional TOPSIS is that it has a rank reversal phenomenon. To overcome this drawback, we propose an improved TOPSIS without rank reversal based on linear max-min normalization with absolute maximum and minimum values by modifying normalization formula and ideal solutions. Moreover, to study the impacts of changing attribute weights on relative closeness values of alternatives, we propose a sensitivity analysis method to attribute weights on the relative closeness values of the alternatives. We applied the proposed method to select best absorbent layer material for thin film solar cells (TFSCs). As a result, copper indium gallium diselinide was selected as the best one and the next cadmium telluride from among five materials. When the alternative is added to or removed from the set of original alternatives, the elements of the normalized decision-matrix, PIS, NIS and the relative closeness values don’t change at all, they are always coincide with the corresponding elements of the original ones. The relative closeness values are absolute values irrelevant to the composition of the alternatives in the improved TOPSIS, while the relative closeness values are relative values relevant to the composition of the alternatives in the traditional TOPSIS. Therefore, the proposed TOPSIS overcomes the rank reversal phenomenon, perfectly. It could be actively applied to practical problems for materials selection.

show abstract

Effect of Tool Pin Profiles Using Tungsten Carbide Tool on Microstructure and Tensile Properties of Friction Stir Welded AA 6082-T6 Thin Aluminium Alloy Joints

Sameer

Birru

2019

Lecture Notes on Multidisciplinary Industrial Engineering

View full text Add to dashboard Cite

Optimization and characterization of dissimilar friction stir welded DP600 dual phase steel and AA6082-T6 aluminium alloy sheets using TOPSIS and grey relational analysis

Cited by 12 publications

References 46 publications

An Analytic Hierarchy Process for a General Ranking of Friction Stir Welding Parameters Optimized Through the Design of Experimental Arrays

An Analytic Hierarchy Process for a General Ranking of Friction Stir Welding Parameters Optimized Through the Design of Experimental Arrays

Materials selection method using improved TOPSIS without rank reversal based on linear max-min normalization with absolute maximum and minimum values

Effect of Tool Pin Profiles Using Tungsten Carbide Tool on Microstructure and Tensile Properties of Friction Stir Welded AA 6082-T6 Thin Aluminium Alloy Joints

Contact Info

Product

Resources

About