Parametric analysis and multi response optimization of laser surface texturing of titanium super alloy

Shivakoti, Ishwer; Kalita, Kanak; Kibria, Golam; Sharma, Anurag; Pradhan, B. B.; Ghadai, Ranjan Kumar

doi:10.1007/s40430-021-03115-0

Cited by 5 publications

(3 citation statements)

References 22 publications

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“…Multi-criteria decision-making (MCDM) tools are extremely popular for obtaining compromise solutions [ 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 ]. GRA is much simpler than most MCDMs but performs a similar task, obtaining balanced solutions when multiple conflicting responses are present.…”

Section: Methodsmentioning

confidence: 99%

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Optimization of Process Variables in the Drilling of LM6/B4C Composites through Grey Relational Analysis

et al. 2022

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The objective of this investigational analysis was to study the influence of process variables on the response during the drilling of LM6/B4C composite materials. Stir casting was employed to produce the LM6/B4C composites. A Vertical Machining Center (VMC) with a dynamometer was used to drill the holes and to record the thrust force. An L27 orthogonal array was used to carry out the experimental work. A grey relational analysis (GRA) was employed to perform optimization in order to attain the lowest Thrust Force (TF), Surface Roughness (SR) and Burr Height (BH). For minimal responses, the optimum levels of the process variables viz. the feed rate (F), spindle speed (S), drill material (D) and reinforcing percentage (R) were determined. The process variables in the drilling of the LM6/B4C composites were indeed optimized, according to confirmational investigations. The predicted Grey Relational Grade was 0.846, whereas the experimental GRG was 0.865, with a 2.2% error—indicating that the optimization process was valid.

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Section: Methodsmentioning

confidence: 99%

“…Multi-criteria decision-making (MCDM) tools are extreme compromise solutions [31][32][33][34][35][36][37][38]. GRA is much simpler than most similar task, obtaining balanced solutions when multiple confli sent.…”

Section: Grey Relational Analysismentioning

confidence: 99%

Optimization of Process Variables in the Drilling of LM6/B4C Composites through Grey Relational Analysis

et al. 2022

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“…[4][5][6] This forces to use of advanced machining processes to machine SMAs. Various advanced machining processes, namely electrochemical machining (ECM), 7 laser beam machining (LBM), 8 abrasive water jet machining (AWJM), 9 electricdischarge machining (EDM), 10,11 and wire-electric discharge machining (WEDM), 12 might be used for machining of SMAs. Biomedical industries need complex shapes implants, and wire spark erosion machining (WSEM) is well known for producing complex shapes on a mass scale at a low cost.…”

Section: Introductionmentioning

confidence: 99%

Process Modelling and Optimization using ANN and RSM during WSEM of Ni_51.59Ti_48.41 shape memory alloy

Singh

Kumar

Misra

2022

Proceedings of the Institution of Mechanical Engineers, Part E:

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Shape memory Ti-Ni alloys have fascinated significantly in recent years since these types of material are intelligent, shape memory and functional materials. These materials find many applications in the engineering and medical fields. In the current study, ‘Ni-Ti’ shape memory alloy (SMA) has been processed by wire spark erosion machining (WSEM). This research article explores the effects of controllable machining variables such as spark on-time, spark off-time, wire feed and servo voltage on productivity, i.e., material removal rate (MRR) and surface integrity. To assess the impact of WSEM controllable factor, a statistical analysis is done. Mathematical modelling and ANOVA study have been conducted after performing 29 experiments based on Box-Behnken design (BBD) of response surface methodology (RSM) technique. Artificial neural network (ANN) modelling technique is used for further prediction of the response. The predicted values obtained from the ANN model shows excellent agreement with experimental outcomes. In this study, the correlation coefficient (R) value is 0.9919, which is very close to unity. It is clearly showing that the prediction accuracy of ANN model is high. Furthermore, scanning electron microscope (SEM) is utilized to assess the microstructure and composition of the WSEMed surfaces. After a critical study, it has been noticed that machined surfaces contain debris, cracks, craters and spherical droplets. In addition, energy dispersive spectroscopy (EDS) analysis has been performed to check the elemental composition on the machined surface.

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Comparative Analysis of Various MCDM Techniques for the Optimization of W-DLC Coatings for Tool Materials of High-Speed Steel and Cemented Carbide

Gangwar¹,

Mondal²,

Ghadai³

2023

Smart Innovation, Systems and Technologies

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Parametric analysis and multi response optimization of laser surface texturing of titanium super alloy

Cited by 5 publications

References 22 publications

Optimization of Process Variables in the Drilling of LM6/B4C Composites through Grey Relational Analysis

Optimization of Process Variables in the Drilling of LM6/B4C Composites through Grey Relational Analysis

Process Modelling and Optimization using ANN and RSM during WSEM of Ni_51.59Ti_48.41 shape memory alloy

Comparative Analysis of Various MCDM Techniques for the Optimization of W-DLC Coatings for Tool Materials of High-Speed Steel and Cemented Carbide

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Parametric analysis and multi response optimization of laser surface texturing of titanium super alloy

Cited by 5 publications

References 22 publications

Optimization of Process Variables in the Drilling of LM6/B4C Composites through Grey Relational Analysis

Optimization of Process Variables in the Drilling of LM6/B4C Composites through Grey Relational Analysis

Process Modelling and Optimization using ANN and RSM during WSEM of Ni51.59Ti48.41 shape memory alloy

Comparative Analysis of Various MCDM Techniques for the Optimization of W-DLC Coatings for Tool Materials of High-Speed Steel and Cemented Carbide

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Process Modelling and Optimization using ANN and RSM during WSEM of Ni_51.59Ti_48.41 shape memory alloy