Correlation between material properties and free vibration characteristics of TIG and laser welded stainless steel 304 reinforced with Al2O3 microparticles

Kumar, Anand; Selvakumar, A S; Balachandar, K.; Ahmed, Aqeel; Arabath, A. Yashar

doi:10.1016/j.jestch.2021.01.017

Cited by 8 publications

(8 citation statements)

References 49 publications

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“…A total of two samples are fabricated through the TIG and Nd: YAG Laser welding process. In our previous work [47] we have related the material properties of the weldments with their free vibrational characteristics. In this study, the welding power and travel speed are considered key process parameters for developing the model using a machine-learning approach.…”

Section: Methodsmentioning

confidence: 99%

“…We have made a correlation between the predicted mechanical properties of the weldment through the ANFIS model to that of (PSNR) values obtained for the corresponding optical microstructures through the image processing technique. The theoretical data derived from the ANFIS and UCNN models are compared with the experimental values from our previous work [47]. Our previous research is to achieve and predict a sound joint between two similar (SS 304) alloys through two different joining processes Nd: YAG Laser Welding and (TIG) welding.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of machine learning techniques for the Nd: YAG Laser & TIG welded stainless steel 304

Kumar,

Krishna,

Hasanabadi

et al. 2024

FME Transactions

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Nd: YAG Laser and Tungsten Inert Gas (TIG) welding processes are the most promising joining techniques used for stainless steel (SS) alloys due to their significant weld characteristics. In this study, the effect of two process parameters (weld power and travel speed) on the mechanical properties (ultimate tensile strength and microhardness) of the weldment is investigated. Two different machine learning techniques, namely Adaptive Neuro-Fuzzy Inference System (ANFIS) and Unified Convolutional Neural Network (UCNN) are also evaluated for prediction of mechanical properties and defect detection through the image processing technique, respectively. A correlation has been performed between these two machine learning approaches with the experimental values. The training data sets are developed for the machine learning techniques, and the obtained results of (ANFIS) and (UCNN) models are related to the actual experimental values. The output of both developed models (ANFIS & UCNN) showed a good agreement with the actual experimental test results. The predicted tensile and microhardness values from the (ANFIS) model were found to greatly agree with the Peak Signal-to-Noise Ratio (PSNR) values from the (UCNN) model. However, owing to the increase in the applications of welding processes in industries, the utilization of machine learning techniques would be more efficient when compared with the other traditional methods that are being adopted.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Evaluation of machine learning techniques for the Nd: YAG Laser & TIG welded stainless steel 304

Kumar,

Krishna,

Hasanabadi

et al. 2024

FME Transactions

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“…The most common typical characteristic features like surface appearance and flash formation, shape and size of the nugget zone, onion ring-like pattern, banded structure formation, and the various defects (most common voids, cavities, tunnel, and pinholes) are visually examined at different magnifications (low or high) [59,66,[78][79][80][81]. The conditions like the addition of reinforcement particles, workpiece material property (including thickness, type, temperature, and thermal conductivity), process parameters range, and tool geometries are the most important that play a major role in the origin of all the above-mentioned features [53,54,[82][83][84][85].…”

Section: Macrostructure Of Mmr Butt Jointsmentioning

confidence: 99%

Nanoparticles reinforced joints produced using friction stir welding: a review

Singh¹

2023

Eng. Res. Express

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Fusion welding processes do not find attention due to their restriction to use for certain grades of materials and alloys. Friction Stir welding (FSW) or the solid-state joining process has proven to be a more efficient process for the welding of metal-matrix reinforced composite butt joints by employing various combinations of reinforcement particles and base matrices compared to the fusion welding process. Due to the solid-state nature of the FSW process, no melting of base material would occur which leads to no toxic fumes, low energy consumption, environmentally friendly, and ease of operation that makes FSW a more versatile welding process for aerospace, automobile, and transportation industries. However, the challenges in joining metal matrix reinforced composites remain even with a substantial advancement in the process made during current years. This review article outlines a brief overview of the state of art of metal matrix reinforced welds joined using FSW. More emphasis with critical evaluation has been done on the (a) macrostructural and microstructural characteristics of a metal matrix reinforced welds which merely depend on the plasticized material flow and reinforcement particles behavior and its difficulty for uniform distribution of reinforcement particles in the weld zone based on the combination of reinforcement particles and the metal matrix materials, (b) assessment of mechanical properties of the metal matrix reinforced welds those are directly interdependent on the selection of process parameters by which the grain refinement and their reinforcement particles distribution depend that leads to changes in such properties, and (c) fracture and wear properties of the metal matrix reinforced welds which again depends on the reinforcement particles and the metal matrix materials combination. The conclusions of this review are ended up with recommendations for the directions for future research.

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“…Welding techniques include fusion welding, brazing and soldering, and solid -state welding. During fusion welding, the zone being joined melts and solidifies (Varun Kumar et al, 2021). For metals and plastics, melting occurs in both the workpieces and the filler material.…”

Section: Introductionmentioning

confidence: 99%

Experimental studies on fillet weldment joints by artisans in Ghana’s Western Region

Acquah,

Sekyi-Ansah

2024

JGhIE

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Constant progress, demand in the manufacturing of mechanisms parts, and the ability to join parts together is another critical aspect of manufacturing for assemblers. Joining methods plays a significant role and welding must be constantly improved, experimented with, and upgraded due to its increasing use in all aspects of manufacturing. The quality of a weld joint is highly dependent on the process parameter. Welds are examined to ensure that they meet specifications through various experimental processes ranging from computational networks, evolutionary algorithms, non-destructive testing (NDT) and destructive testing (DE) to ascertain the quality of joints. The main objective of the study was to ascertain the quality of welded joints through non-destructive evaluation (NDE). To achieve this goal, an experimental method of four (4) NDE methods were employed to evaluate the quality of weldment joints on a mild steel material joined using manual metal arc welding (MMAW) process in selected fabrication shops in Takoradi-Kokompe, Ghana. Steel specimens were prepared and taken to the artisans to be created into corner joints using the fillet method. The specimens were tested using non-destructive testing techniques. The results on the welded joints were evaluated using the AWS D1.1 code of acceptance for structural steels. The results revealed that all welded joints from fabricators failed to meet the AWS D1.1:2000 acceptance criteria and, thus, had to be rejected. The results implies that through the artisans have worked for many years in the welding trade, their lack of competence and skills in the selection of right input welding parameters contributed to the results. The results implies that, the finished artefacts may look fine but internal structures may contribute to future failure of the fabricated artefacts.

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Correlation between material properties and free vibration characteristics of TIG and laser welded stainless steel 304 reinforced with Al2O3 microparticles

Cited by 8 publications

References 49 publications

Evaluation of machine learning techniques for the Nd: YAG Laser & TIG welded stainless steel 304

Evaluation of machine learning techniques for the Nd: YAG Laser & TIG welded stainless steel 304

Nanoparticles reinforced joints produced using friction stir welding: a review

Experimental studies on fillet weldment joints by artisans in Ghana’s Western Region

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