Surface Preparation for Coating and Erosion MRR of SS 304 Using Silicon Carbide Abrasive Jet

Adak, Deb Kumar; Pal, Vivekananda; Das, Santanu; Ghara, Tina; Joardar, Hillol; Alrasheedi, Nashmi H.; Haldar, B.C.

doi:10.3390/lubricants11010010

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…The experiment employed an RSM-based experimental design to identify optimized input parameters and their corresponding values through RSM modeling and multi-objective optimization [33][34][35] using the desirability function. Furthermore, MOGA-ANN was used to predict responses.…”

Section: State-of-the-art Review Of Electrochemical Machining Inconel...mentioning

confidence: 99%

Multi-Response Optimization of Electrochemical Machining Parameters for Inconel 718 via RSM and MOGA-ANN

Saha,

Mondal,

Čep

et al. 2024

Machines

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Inconel 718’s exceptional strength and corrosion resistance make it a versatile superalloy widely adopted in diverse industries, attesting to its reliability. Electrochemical machining (ECM) further enhances its suitability for intricate part fabrication, ensuring complex shapes, dimensional accuracy, stress-free results, and minimal thermal damage. Thus, this research endeavors to conduct a novel investigation into the electrochemical machining (ECM) of the superalloy Inconel 718. The study focuses on unraveling the intricate influence of key input process parameters—namely, electrolytic concentration, tool feed rate, and voltage—on critical response variables such as surface roughness (SR), material removal rate (MRR), and radial overcut (RO) in the machining process. The powerful tool, response surface methodology (RSM), is used for understanding and optimizing complex systems by developing mathematical models that describe the relationships between input and response variables. Under a 95% confidence level, analysis of variance (ANOVA) suggests that electrolyte concentration, voltage, and tool feed rate are the most important factors influencing the response characteristics. Moreover, the incorporation of ANN modeling and the MOGA-ANN optimization algorithm introduces a novel and comprehensive approach to determining the optimal machining parameters. It considers multiple objectives simultaneously, considering the trade-offs between them, and provides a set of solutions that achieve the desired balance between MRR, SR, and RO. Confirmation experiments are carried out, and the absolute percentage errors between experimental and optimized values are assessed. The detailed surface topography and elemental mapping were performed using a scanning electron microscope (SEM). The nano/micro particles of Inconel 718 metal powder, obtained from ECM sludge/cakes, along with the released hydrogen byproducts, offer promising opportunities for recycling and various applications. These materials can be effectively utilized in powder metallurgy products, leading to enhanced cost efficiency.

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Section: State-of-the-art Review Of Electrochemical Machining Inconel...mentioning

confidence: 99%

Multi-Response Optimization of Electrochemical Machining Parameters for Inconel 718 via RSM and MOGA-ANN

Saha,

Mondal,

Čep

et al. 2024

Machines

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“…One of the main types of damage to machine components is wear including, among others, abrasive wear [4], adhesive wear [5], erosive wear [6], and fretting wear [7]. Therefore, the high-quality of machine part surfaces is usually needed.…”

Section: Introductionmentioning

confidence: 99%

Application of Taguchi Technique to Study Tribological Properties of Roller-Burnished 36CrNiMo4 Steel

2023

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The purpose of this work was to study the effect of roller burnishing process on tribological properties of 36CrNiMo4 steel. The experiments were performed under dry and starved lubrication contact conditions. The influence of selected parameters such as applied load, sliding speed, and sliding distance on the coefficient of friction and wear volume was investigated. The Taguchi technique was used to estimate the parameters significantly affecting the tribological properties. The levels of tested input factors were as follows: applied load—5, 10, and 15 N, sliding speed—0.24, 0.48, and 0.72 m/s, and sliding distance—160, 282, and 404 m. The results revealed that load was the most dominating factor that affects the wear volume and the coefficient of friction. The influence of other input factors was smaller. Regression analysis was also performed to predict tribological behavior. The results demonstrated good agreement between experimental and predicted results. The worn-out samples were analyzed using SEM to reveal the wear mechanism.

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Comparative performance investigation and sustainability evaluation between hot-AJM and AJM during machining of zirconia ceramic using Al2O3 abrasives

Samantaray,

Pradhan,

Dhupal

et al. 2024

J Braz. Soc. Mech. Sci. Eng.

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Surface Preparation for Coating and Erosion MRR of SS 304 Using Silicon Carbide Abrasive Jet

Cited by 5 publications

References 28 publications

Multi-Response Optimization of Electrochemical Machining Parameters for Inconel 718 via RSM and MOGA-ANN

Multi-Response Optimization of Electrochemical Machining Parameters for Inconel 718 via RSM and MOGA-ANN

Application of Taguchi Technique to Study Tribological Properties of Roller-Burnished 36CrNiMo4 Steel

Comparative performance investigation and sustainability evaluation between hot-AJM and AJM during machining of zirconia ceramic using Al2O3 abrasives

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