Multi-Objective Optimization of the Resistance Spot-Welding Process Parameters for the Welding of Dual-Phase Steel Dp500

Đurić, Aleksija; Milčić, Dragan; Klobčar, Damjan; Marković, Biljana

doi:10.17222/mit.2020.095

Cited by 3 publications

(2 citation statements)

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“…Taguchi method has been used by several researchers to optimize the welding parameters [18,19], and 3D printing [20]. Patel and Kadia [21] have adopted the Taguchi experiment and ANOVA to determine the influence of process parameters like raster width, contour width, layer thickness, air gap, and raster orientation on tensile, flexural & impact strengths and hardness of the prototypes built using FDM process.…”

Section: Introductionmentioning

confidence: 99%

Effect of process parameters on the strength of ABS based FDM prototypes: novel machine learning based hybrid optimization technique

Ramiah¹,

Pandian²

2023

Mater. Res. Express

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Even though the prototypes built using Fused Deposition Modelling (FDM) process are found to exhibit good mechanical properties, there are ample scopes to improve them by means of selecting suitable process parameters. Since the FDM process involves more number of process parameters, the selection of optimized values becomes more complex and time consuming. Further, the complex correlation among the process parameters makes the selection process more tedious and involves more numerical steps. Hence it has been intended to perform a physical experiment with the known parameters to determine the performance measures of the built prototypes. With this moto, in this work the effect of the 3D printing parameters is studied and the optimal combination of these parameters are determined. The Taguchi L18 orthogonal array based values are assigned for process parameters and the physical prototypes are fabricated. These specimens are tested in the laboratory and the observations are analyzed. It has been found that the process parameters under consideration have a good effect on the strength of the built models. Out of the 18 experiments, better experiments are selected by using a Machine Learning (ML) approach namely decision tree (DT). Finally, the best combination of parameters has been determined by using a novel hybrid multi objective technique which is formulated by integrating Fuzzy Analytical Hierarchy Process (FAHP) and Complex Proportional Assessment of alternatives (COPRAS) techniques. Then a confirmation experiment has also been done to confirm the optimal combination of parameters. The influence of the parameters is also found by using ANOVA (Analysis of Variance) method. The final results show that the raster angle influences the outputs more while the raster to raster gap has the least influence.

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Section: Introductionmentioning

confidence: 99%

Effect of process parameters on the strength of ABS based FDM prototypes: novel machine learning based hybrid optimization technique

Ramiah¹,

Pandian²

2023

Mater. Res. Express

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“…The DP500 has good forming and welding ability, and is very suitable for the manufacture of safety parts in cars such as reinforcement elements [7]. The DP500 steel is very suitable for RSW, and with optimized welding parameters, very good mechanical characteristics of the joint can be obtained [8]. Recently, several groups of aluminum alloys have been used in the automotive industry, the most common of which are alloys AA5754 and AA6082.…”

Section: Introductionmentioning

confidence: 99%

Microstructure and Fatigue Properties of Resistance Element Welded Joints of DP500 Steel and AW 5754 H22 Aluminum Alloy

et al. 2022

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The modern concept of lightweight design (LW) requires the application of different materials in one structure (multi-material structures). The structure of different materials has a good perspective for application in the automotive and aerospace industries but only if it is possible to achieve a quality joint between different materials. The most used technology for joining different materials in the automotive industry is Resistance spot welding (RSW). Due to different mechanical, physical, and chemical properties, the joining of different materials by RSW technology does not provide a quality joint, and accordingly, alternative technologies for joining different materials have emerged. Resistance element welding (REW) was developed to enable joint of different materials. This paper presents the welding of AW 5754 H22 Al alloy (1.0 mm-thick) and DP500 steel (1.5 mm-thick) using novel REW. The peak load, absorption energy, microstructure, microhardness and fatigue strength of the REW joint has been investigated. The joint of the same materials has been done also using conventional RSW to compare some results. The results that will be presented in this paper show that that REW can achieve reliable joining of the two materials at relatively low welding currents compared to RSW. Using REW process with a significantly lower welding current, satisfactory mechanical characteristics of the weld joint can be achieved, so peak load is between 2300–2500 N, displacement is between 2.5–3 mm and the absorption energy is between 3.3–5.7 J. REW joints showed fatigue strength with the fatigue limit of 882 N.

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A multi-response optimization approach to mechanical properties improvement of dissimilar resistance spot welding joints

Hussein,

Akbari,

Noorossana

et al. 2023

JEDT

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Purpose This study aims to investigate the effects of process input parameters (welding current, welding time, electrode pressure and holding time) on the output responses (nugget diameter, peak load and indentation) that control the mechanical properties and quality of the joints in dissimilar resistance spot welding (RSW) for the third generation of advanced high-strength steel (AHSS) quenching and partitioning (Q&P980) and (SPFC780Y) high-strength steel spot welds. Design/methodology/approach Design of experiment approach with two level factors and center points was adopted. Destructive peel and shear tensile strengths were used to measure the responses. The significant factors were determined using analysis of variance implemented by Minitab 18 software. Finally, multiresponse optimization was carried out using the desirability function analysis method. Findings Holding time was the most significant factor influencing nugget diameter, whereas welding current had the greatest impact on peak load and indentation. Multiresponse optimization revealed that the optimal settings were a welding current of 12.5 KA, welding time of 18 cycles, electrode pressure of 420 Kgf and holding time of 10 cycles. These settings produced a nugget diameter of 8.0 mm, a peak load of 35.15 KN and an indentation of 22.5%, with a composite desirability function of 0.764. Originality/value This study provides an effective approach for multiple response optimization to the mechanical behavior of RSW joints, even though there have been few studies on the third generation of AHSS joints and none on the dissimilar joints of the materials used in this study.

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Multi-Objective Optimization of the Resistance Spot-Welding Process Parameters for the Welding of Dual-Phase Steel Dp500

Cited by 3 publications

References 0 publications

Effect of process parameters on the strength of ABS based FDM prototypes: novel machine learning based hybrid optimization technique

Effect of process parameters on the strength of ABS based FDM prototypes: novel machine learning based hybrid optimization technique

Microstructure and Fatigue Properties of Resistance Element Welded Joints of DP500 Steel and AW 5754 H22 Aluminum Alloy

A multi-response optimization approach to mechanical properties improvement of dissimilar resistance spot welding joints

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