Optimization of Activated Tungsten Inert Gas Welding Process Parameters Using Heat Transfer Search Algorithm: With Experimental Validation Using Case Studies

Vora, Jay; Patel, Vivek; Srinivasan, Seshasai; Chaudhari, Rakesh; Pimenov, Danil Yurievich; Giasin, Khaled; Sharma, Shubham

doi:10.3390/met11060981

Cited by 36 publications

(16 citation statements)

References 42 publications

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“…BBD design of RSM was used to obtain an optimum response by using a series of designed experiments. Another purpose of implementing BBD design was to develop mathematical correlations between input and output parameters [ 53 ]. RSM was employed for the reduction in the experimental trials which avoids additional time and cost required for material [ 54 , 55 ].…”

Section: Methodsmentioning

confidence: 99%

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

et al. 2022

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Nitinol-shape memory alloys (SMAs) are widely preferred for applications of automobile, biomedical, aerospace, robotics, and other industrial area. Therefore, precise machining of Nitinol SMA plays a vital role in achieving better surface roughness, higher productivity and geometrical accuracy for the manufacturing of devices. Wire electric discharge machining (WEDM) has proven to be an appropriate technique for machining nitinol shape memory alloy (SMA). The present study investigated the influence of near-dry WEDM technique to reduce the environmental impact from wet WEDM. A parametric optimization was carried out with the consideration of design variables of current, pulse-on-time (Ton), and pulse-off-time (Toff) and their effect were studied on output characteristics of material removal rate (MRR), and surface roughness (SR) for near-dry WEDM of nitinol SMA. ANOVA was carried out for MRR, and SR using statistical analysis to investigate the impact of design variables on response measures. ANOVA results depicted the significance of the developed quadratic model for both MRR and SR. Current, and Ton were found to be major contributors on the response value of MRR, and SR, respectively. A teaching–learning-based optimization (TLBO) algorithm was employed to find the optimal combination of process parameters. Single-response optimization has yielded a maximum MRR of 1.114 mm3/s at Ton of 95 µs, Toff of 9 µs, current of 6 A. Least SR was obtained at Ton of 35 µs, Toff of 27 µs, current of 2 A with a predicted value of 2.81 µm. Near-dry WEDM process yielded an 8.94% reduction in MRR in comparison with wet-WEDM, while the performance of SR has been substantially improved by 41.56%. As per the obtained results from SEM micrographs, low viscosity, reduced thermal energy at IEG, and improved flushing of eroded material for air-mist mixture during NDWEDM has provided better surface morphology over the wet-WEDM process in terms of reduction in surface defects and better surface quality of nitinol SMA. Thus, for obtaining the better surface quality with reduced surface defects, near-dry WEDM process is largely suitable.

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Section: Methodsmentioning

confidence: 99%

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

et al. 2022

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“…Another paper that focused on the optimization of TIG welding by achieving a conforming shape of weld seams is [25]. The process input parameters qualified in the paper were welding current, arc length, and electrode torch speed; the output variables investigated were melting depth, depth to width ratio, heat input, and heat affected zone width.…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of the Robotic Production Process for an Aircraft Jet Engine Casing

Uliasz¹,

Ornat²,

Burghardt

et al. 2022

Applied Sciences

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This paper investigated the design of a system to monitor the status of the robotic welding process of thin-walled components for an aircraft jet engine. Opportunities to measure and log processing parameters, such as welding speed, current, and voltage, on the existing production cell were taken. The acquired data were processed using elements of descriptive statistics. Obtained indicators were matched with physical inspection results of the weld’s quality. The adopted methodology was used to identify an essential parameter determining the presence of defined weld defects. The developed solution was implemented in the production cell.

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“…Multi-response optimization is needed in contemporary manufacturing. Various methods such as gray relational analysis (GRA) [ 11 , 15 , 18 ], heat transfer search (HTS) algorithm [ 2 , 19 ], teacher learning-based algorithm [ 20 ], particle swarm algorithm [ 21 ], genetic algorithm [ 22 ], artificial neural networks [ 23 ], etc., have been attempted to check their feasibility in finding the tread-off solution in terms of optimized process parameters.…”

Section: Introductionmentioning

confidence: 99%

Integration of Fuzzy AHP and Fuzzy TOPSIS Methods for Wire Electric Discharge Machining of Titanium (Ti6Al4V) Alloy Using RSM

et al. 2021

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Titanium and its alloys exhibit numerous uses in aerospace, automobile, biomedical and marine industries because of their enhanced mechanical properties. However, the machinability of titanium alloys can be cumbersome due to their lower density, high hardness, low thermal conductivity, and low elastic modulus. The wire electrical discharge machining (WEDM) process is an effective choice for machining titanium and its alloys due to its unique machining characteristics. The present work proposes multi-objective optimization of WEDM on Ti6Al4V alloy using a fuzzy integrated multi-criteria decision-making (MCDM) approach. The use of MCDM has become an active area of research due to its proven ability to solve complex problems. The novelty of the present work is to use integrated fuzzy analytic hierarchy process (AHP) and fuzzy technique for order preference by similarity to ideal situation (TOPSIS) to optimize the WEDM process. The experiments were systematically conducted adapting the face-centered central composite design approach of response surface methodology. Three independent factors—pulse-on time (Ton), pulse-off time (Toff), and current—were chosen, each having three levels to monitor the process response in terms of cutting speed (VC), material removal rate (MRR), and surface roughness (SR). To assess the relevance and significance of the models, an analysis of variance was carried out. The optimal process parameters after integrating fuzzy AHP coupled with fuzzy TOPSIS approach found were Ton = 40 µs, Toff = 15 µs, and current = 2A.

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Optimization of Activated Tungsten Inert Gas Welding Process Parameters Using Heat Transfer Search Algorithm: With Experimental Validation Using Case Studies

Cited by 36 publications

References 42 publications

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Parametric Optimization and Influence of Near-Dry WEDM Variables on Nitinol Shape Memory Alloy

Automatic Evaluation of the Robotic Production Process for an Aircraft Jet Engine Casing

Integration of Fuzzy AHP and Fuzzy TOPSIS Methods for Wire Electric Discharge Machining of Titanium (Ti6Al4V) Alloy Using RSM

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