An ANFIS based approach for predicting the weld strength of resistance spot welding in artificial intelligence development

Zaharuddin, Mohd Faridh Ahmad; Kim, Dong-Hyun; Rhee, Sehun

doi:10.1007/s12206-017-1041-0

Cited by 31 publications

(8 citation statements)

References 19 publications

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“…An artificial neural network was used to develop the prediction model. The neural network has been frequently used to monitor welding quality using dynamic resistance in the resistance welding process [20,21]. A schematic of the DNN configuration is shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

Misalignment monitoring between electrodes and a rivet in a three-sheet resistance element welding-based dynamic resistance

Kim¹,

Jo²,

Lee³

et al. 2023

Preprint

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Recently, resistance element welding (REW), a method for welding dissimilar materials is gaining popularity in the automobile industry. However, in a three-sheet REW process, in which a rivet is inserted into the middle sheet, stable welding quality cannot be guaranteed because the alignment of the rivet and electrodes cannot be visually inspected. In this study, the three-sheet REW of SPFC590 (structural steel), SABC1470 (high-strength steel), and Al6061 (aluminum alloy) was investigated. The welding quality was evaluated at an increasing misalignment, and it was compared to that of a 0-mm misalignment distance. The welding quality was significantly reduced from a 6-mm misalignment distance. The pre-contact process, which is designed for predicting misalignment, was applied prior to the actual REW process. The dynamic resistance waveform in the pre-contact process was significantly different at each misalignment distance condition because of the difference in each alignment with electrodes and workpieces. The features extracted from the dynamic resistance waveforms were applied to a deep neural network (DNN) model to monitor the misalignment between electrodes and a rivet. Because the weld quality (weld nugget diameter) was not satisfactory at distances greater than 6 mm, the threshold value of the misalignment distance was set as 6 mm. A neural network that can predict the state of the misalignment between electrodes and a rivet was designed and trained, and the misalignment was predicted with 100% accuracy from DNN model.

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Section: Resultsmentioning

confidence: 99%

Misalignment monitoring between electrodes and a rivet in a three-sheet resistance element welding-based dynamic resistance

Kim¹,

Jo²,

Lee³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…In addition, the welding occurs internally between the joining surfaces and the nugget formation can be controlled by various process parameters of the RSW process such as welding current, electrode force, electrode tip diameter, welding time, material thickness, etc. The weld quality is affected if the value of RSW parameters changes slightly [12]. Therefore, various studies have focused on the influence of process parameters in resistance spot welding.…”

Section: Introductionmentioning

confidence: 99%

Examination on joining of 2 mm thick dissimilar stainless steel plates using resistance spot welding

Vignesh

2023

Eng. Res. Express

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In the present study, dissimilar metals such as austenitic stainless steel (ASS) and duplex stainless steel (DSS) with a thickness of 2 mm are joined by resistance spot welding process to investigate the welding metallurgy and failure behavior under different heat inputs. Non-uniform electrode impressions are observed on ASS and DSS sides due to their different thermal conductivity and electrical resistivity. The microstructure of the Fusion Zone (FZ) shows that higher heat input accelerates the growth of Intra Granular Austenite (IGA) due to faster cooling. Scanning electron microscopy (SEM) - Energy Dispersive X-ray (EDX) investigation at FZ showed that Chromium & Molybdenum decrease with increasing heat input due to a decrease in ferrite content. X-ray diffraction analysis confirmed that ferrite formation is limited at higher heat input. Microhardness study revealed that the higher hardness is in the middle of the weld nugget, which is due to the presence of equiaxed grains and IGA. The lowest hardness on the ASS side of Heat Affected Zone (HAZ) is due to the phenomenon of grain growth, and the HAZ DSS side has a higher hardness than DSS Base Metal (BM) due to the mechanism of solid solution strengthening. The tensile shear test showed that the tensile shear strength increases with the addition of heat. SEM Examination of the fracture surface revealed the presence of an equiaxed dimple structure on the ASS side, confirming ductile fracture, and torn bonds on the DSS side, confirming quasi-gap fracture due to the work-hardening ability of both plates. This study is carried out to understand the relationship between mechanical, metallurgical, and failure behaviours, because, researches on joining of dissimilar (AISI 316L and DSS 2205) stainless steel sheets using Resistance Spot Welding process is very limited and need to be studied in detail.

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“…Researcher found that it should be tried to have lower heat input to manage hot cracking tendency in weldments. Zaharuddin et al [18] to predict the weld strength of resistance spot welding for steel sheets CR780 having high strength an adaptive neuro fuzzy inference system (ANFIS) based proposal was used in artificial intelligence development. They found that on comparison of both artificial neural network (ANN) and ANFIS, the prediction of ANFIS was more accurate than that of ANN for weld strength.…”

Section: Introductionmentioning

confidence: 99%

Experimental investigation on cladding with metal cored wire using GMAW process and parametric optimization

et al. 2021

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Cladding is widely used in manufacturing industries for the production of pressure vessel by depositing thick layer of filler material for providing corrosion resistant-surface. The use of metal cored wire in gas metal arc welding (GMAW) process is popular due to its higher deposition rate and productivity. This work investigates the effect of process parameters on the deposition of cladding layer with ER 309L metal core wire (as filler material) on a corrosion resistant material (IS 2062). The welding parameters viz., wire feed rate (WFR), voltage (V), welding speed (S) and nozzle to plate distance (NTD) are employed as process parameters while penetration (P), bead width (W), reinforcement (R), weld penetration shape factor (WPSF) and weld reinforcement form factor (WRFF) as welding responses. The predictive model developed for P, W, R, WPSF, and WRFF using the response surface methodology (RSM) approach is found adequate at 95% confidence interval. The validation results for the developed model results in a model accuracy (MA) of 92.82%, 96.34%, 91.47% 88.98% and 87.75% for model P, W, R, WPSF, and WRFF respectively and it shows higher predictability and accuracy. The process parameters are optimized simultaneously with integrated optimization approach using RSM with Jaya algorithm and obtain optimal solution in less than 20 number of iterations. The minimum fitness value obtained as 1.3008 at an optimal parameter setting of WFR = 12 m min−1, V = 26 V, S = 280 mm min−1, NTD = 10 mm. The validation result at the optimal parameter setting results in an improvement of 6.45%, 11.29%, 13.58%, 16.07%, 15.38% is noted for P, W, R, WPSF, and WRFF respectively.

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An ANFIS based approach for predicting the weld strength of resistance spot welding in artificial intelligence development

Cited by 31 publications

References 19 publications

Misalignment monitoring between electrodes and a rivet in a three-sheet resistance element welding-based dynamic resistance

Misalignment monitoring between electrodes and a rivet in a three-sheet resistance element welding-based dynamic resistance

Examination on joining of 2 mm thick dissimilar stainless steel plates using resistance spot welding

Experimental investigation on cladding with metal cored wire using GMAW process and parametric optimization

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