Development of a predictive model for determining mechanical properties of AA 6061 using regression analysis

Achebo, Joseph I.

doi:10.1080/21693277.2015.1025447

Cited by 7 publications

(3 citation statements)

References 16 publications

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“…Modelling and prediction methods for estimating fracture toughness and fatigue crack growth of aluminium alloy typically often use a variety of techniques, including empirical modelling [175] and simulations by using finite element analysis (FEA) [176,177]. These methods are needed to characterise the fracture behaviour of aluminium alloys and ensure their safe and effective use in various applications [178,179]. It is difficult to predict how materials would fracture when exposed to various coastal environmental conditions like temperature, humidity, and corrosion.…”

Section: Modelling and Predictive Approachesmentioning

confidence: 99%

Fracture Behaviour of Aluminium Alloys under Coastal Environmental Conditions: A Review

Alqahtani,

Starr,

Khan

2024

Metals

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Aluminium alloys have been integral to numerous engineering applications due to their favourable strength, weight, and corrosion resistance combination. However, the performance of these alloys in coastal environments is a critical concern, as the interplay between fracture toughness and fatigue crack growth rate under such conditions remains relatively unexplored. This comprehensive review addresses this research gap by analysing the intricate relationship between fatigue crack propagation, fracture toughness, and challenging coastal environmental conditions. In view of the increasing utilisation of aluminium alloys in coastal infrastructure and maritime industries, understanding their behaviour under the joint influences of cyclic loading and corrosive coastal atmospheres is imperative. The primary objective of this review is to synthesise the existing knowledge on the subject, identify research gaps, and propose directions for future investigations. The methodology involves an in-depth examination of peer-reviewed literature and experimental studies. The mechanisms driving fatigue crack initiation and propagation in aluminium alloys exposed to saltwater, humidity, and temperature variations are elucidated. Additionally, this review critically evaluates the impact of coastal conditions on fracture toughness, shedding light on the vulnerability of aluminium alloys to sudden fractures in such environments. The variability of fatigue crack growth rates and fracture toughness values across different aluminium alloy compositions and environmental exposures was discussed. Corrosion–fatigue interactions emerge as a key contributor to accelerated crack propagation, underscoring the need for comprehensive mitigation strategies. This review paper highlights the pressing need to understand the behaviour of aluminium alloys under coastal conditions comprehensively. By revealing the existing research gaps and presenting an integrated overview of the intricate mechanisms at play, this study aims to guide further research and engineering efforts towards enhancing the durability and safety of aluminium alloy components in coastal environments.

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Section: Modelling and Predictive Approachesmentioning

confidence: 99%

Fracture Behaviour of Aluminium Alloys under Coastal Environmental Conditions: A Review

Alqahtani,

Starr,

Khan

2024

Metals

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“…Mathematical multiple linear regression analysis of mechanical properties of potential weld joints, such as yield strength, ultimate tensile strength and percentage elongation, on aluminum alloy AA 6061 during manufacturing conditions is conducted in [17]. Models have proven to be very accurate in prediction of mechanical properties which can greatly assist engineers in proper planning and simultaneously improve efficiency of aluminum alloy welding operations.…”

Section: Introductionmentioning

confidence: 99%

Mathematical Modeling of the Influence Parameters During Formation and Propagation of the Lüders Bands

et al. 2020

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In this study, an analysis of the influence parameters measured by the static tensile test, thermography and digital image correlation was performed during formation and propagation of the Lüders bands. A new approach to the prediction of stresses, maximum temperature changes and strains during the Lüders band formation and propagation is proposed in this paper. Application of the obtained mathematical models of influence parameters gives a clear insight into the behavior of niobium microalloyed steel at the beginning of the plastic flow, which can improve product quality and reduce costs during the forming of microalloyed steels with the appearance of the Lüders bands. The obtained models of influential parameters during formation and propagation of the Lüders bands have been developed by the regression analysis method. The proposed mathematical models showed low deviations of calculated results ranging from 1.34% to 12.37%.The local stress amounts, important in the forming of microalloyed steels since indicating surface roughness and plastic flow possibilities during the Lüders band propagation, are obtained by the mathematical model. It was found that stress amounts increase during the Lüders band propagation in the area behind the Lüders band front. The difference in stress amount between the start of the Lüders band propagation and advanced Lüders band propagation is 25.53 MPa.

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“…Sen et al [13] developed a mathematical model using multiple regression analysis in MINITAB 13.1 to predict the weld bead geometry and the model adequacy checked using analysis of variance (ANOVA). Joseph Achebo [14] developed a robust predictive model for determining mechanical properties of AA 6061 using multiple regression analysis. The study involved using MLR to predict the ultimate tensile strength (UTS), the yield strength (YS) and percentage elongation (% Elongation) of AA 6061.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Mild Steel Welding Process Parameters Using Multivariate Linear Regression

Odinikuku

Atadious

Onwuamaeze

2020

JERR

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Local welders in Nigeria are prone to poor quality weldment because of their lack of welding technical skills. When these local welders carry out their welding operation, the welded joints are considered to be good enough because the metal materials welded together are seen to be good and satisfactory. In most case, when these welded joints have not fully served their service life, these materials fail due to the poor quality of the weldment. Material quality can easily be assessed by inspecting the microstructure of the weldment. In this wok, mild steel welding process parameters were optimized using multivariate linear regression (MLR). The study involves the determination of the suitable set of conditions for the welding process parameters that would give the optimum weld of mild steel (low carbon steel) using Gas Metal Arc welding (GMAW) technique and obtain a relationship between the three welding process parameters and the ultimate tensile strength and Brinell hardness number. For this reason, an experimental study was carried out using nine samples of the specimen of mild steel. The experimental and predicted results show that arc voltage and gas flow rate affect the ultimate tensile strength and the Brinell hardness number of mild steel. The maximum ultimate tensile strength and Brinell hardness number were Odinikuku et al.; JERR, 10(2): 43-50, 2020; Article no.JERR.54101 44 obtained at 180A, 15V and 20l/min. It was also observed that the ultimate tensile strength decreases with increases in arc voltage and gas flow rate. But these two parameters tend to have a positive effect on the Brinell hardness number. Original Research Article

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Development of a predictive model for determining mechanical properties of AA 6061 using regression analysis

Cited by 7 publications

References 16 publications

Fracture Behaviour of Aluminium Alloys under Coastal Environmental Conditions: A Review

Fracture Behaviour of Aluminium Alloys under Coastal Environmental Conditions: A Review

Mathematical Modeling of the Influence Parameters During Formation and Propagation of the Lüders Bands

Optimization of Mild Steel Welding Process Parameters Using Multivariate Linear Regression

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