The use of factorial design and response surface methodology for fast determination of optimal heat treatment conditions of different Ni–Co–Mo surfaced layers

Grum, Janez; Slabe, Janez Marko

doi:10.1016/j.jmatprotec.2004.04.220

Cited by 104 publications

(39 citation statements)

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“…4,6, and 8. These response contours can help in the prediction of the response (UTS) for any region of the experimental domain [15].…”

Section: Optimization Of Process Parameters Of Friction Stir Welded Amentioning

confidence: 99%

“…Microstructural studies revealed that the material placed on the advancing side dominates the nugget region. Hardness studies showed that the lowest hardness in the weldment occurred in the heat-affected zone on alloy of 5083 side, where tensile failures were observed to take place [6]. Though various aluminum joints produced by FSW were studied by several researchers, optimization of the FSW process parameter for aluminum alloy AA5083-O which is used typically in the marine, automotive, structural and construction industries, has not yet been studied with help of response surface methodology (RSM).…”

Section: Introductionmentioning

confidence: 99%

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Optimization of process parameters of friction stir welded AA 5083-O aluminum alloy using Response Surface Methodology

Jannet¹,

Mathews²,

Raja³

2015

Bulletin of the Polish Academy of Sciences Technical Sciences

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Abstract.A methodology was exhibited to create the experimental model for assessing the Ultimate Tensile Strength of AA 5083-O aluminum compound which is broadly utilized as a part of boat building industry by Friction Stir Welding (FSW). FSW process parameters, such as: tool rotational speed, welding speed, and axial force were optimized for better results. FSW was completed considering three-component 3-level Box Behnekn Design. Response surface Methodology (RSM) was implemented to obtain the relationship between the FSW process parameters and ultimate Tensile Strength. Analysis of Variance (ANOVA) procedure was utilized to check the aptness of the created model. The FSW process parameters were additionally streamlined utilizing Response Surface Methodology (RSM) to augment tensile strength. The joint welded at a rotational speed of 1100 rpm, a welding speed of 75 mm/min and a pivotal energy of 2.5 t displays higher tensile strength compared with different joints in comparison with other joints.

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“…4,6, and 8. These response contours can help in the prediction of the response (UTS) for any region of the experimental domain [15].…”

Section: Optimization Of Process Parameters Of Friction Stir Welded Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimization of process parameters of friction stir welded AA 5083-O aluminum alloy using Response Surface Methodology

Jannet¹,

Mathews²,

Raja³

2015

Bulletin of the Polish Academy of Sciences Technical Sciences

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“…Fusion welding techniques for joining materials are becoming difficult, because it is necessary to control the melting on both sides of two materials together due to the required differences of the physical properties such as; thermal expansion, melting temperature and thermal conductivity. [2,3]. A fusion welding process will be faced problems by defects like; porosity, weld cracks and segregation, and to overcome on these problems, joining dissimilar metals can be used by a welding process in the solid state [4].…”

Section: Introductionmentioning

confidence: 99%

Corrosion Behavior of Diffusion Bonding Joints of (OFHC) Copper with Stainless Steel 304L in 3.5% NaCl

Ajeel

Akbar

Hassoni

2018

NJES

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“…Recent studies have used the response surface method (RSM) to optimise parameters in the casting process [3]. The computational efficiency of the RSM approach significantly reduces as the number of process parameters increase [4].…”

Section: Introductionmentioning

confidence: 99%

A New Back-Propagation Algorithm for Diagnosing Defective Casting

Nawi¹

2013

IJET

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Abstract-The diagnosis of defective castings has always been a centre of attention in the manufacturing industry. An intelligent diagnosis system should be able to diagnose effectively the causal representation and also justify its diagnosis. Recently, the artificial-neural networks (ANN), or simply neural-networks (NN), technique has gained more popularity in learning cause and effect analysis in casting processes However, the algorithm comes with problems like slow convergence and convergence to local minima and unable to model exponential increase/decrease in belief values in cause and effect relationships. This paper proposed a new algorithm for improving the conventional back-propagation algorithm by introducing gain parameters during the learning process. The abilities of the proposed method to capture the exponential change in the belief variation of the cause when the belief in the effect is at its minimum is compared with the outputs from both the conventional back propagation on a real casting data set.

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The use of factorial design and response surface methodology for fast determination of optimal heat treatment conditions of different Ni–Co–Mo surfaced layers

Cited by 104 publications

References 1 publication

Optimization of process parameters of friction stir welded AA 5083-O aluminum alloy using Response Surface Methodology

Optimization of process parameters of friction stir welded AA 5083-O aluminum alloy using Response Surface Methodology

Corrosion Behavior of Diffusion Bonding Joints of (OFHC) Copper with Stainless Steel 304L in 3.5% NaCl

A New Back-Propagation Algorithm for Diagnosing Defective Casting

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