Performance of Textured Tool with MQL in Machining of Precipitation Hardened Stainless Steel

Palanisamy, D.; Devaraju, Anusuriya; Narasimhamu, K. L.; Manikandan, N.; Raju, Ramesh; Thejasree, P.; Arulkirubakaran, D.

doi:10.1007/978-981-19-0244-4_6

Cited by 20 publications

(1 citation statement)

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“…Predictive model development is a process utilized in manufacturing to predict the performance of a product or service. It helps the manufacturer make informed decisions and improve the efciency of their operations [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

Development of Hybrid Optimization Model Using Grey-ANFIS-Jaya Algorithm for CNC Drilling of Aluminium Alloy

Narasimhamu,

Natarajan,

Thejasree

et al. 2024

Journal of Engineering

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Aluminium alloys are gaining popularity in a diversity of engineering applications because of their extraordinary features such as strength, resistance to oxidation, and so on. AA5052 (Al-Mg series) is generally used in antirust uses, particularly in desalination related activities, due to its better resistance to corrosion in marine applications at temperature ranges up to 125°C, lower cost, better heat-carrying capacity, and nontoxicity of its corrosion components. Drilling is one of the most commonly adopted material removal processes that is adopted in numerous engineering uses. Taguchi’s technique is engaged to arrange and examine the tests, by treating the drilling diameter and speed and as independent process factors. The studies were carried out using an L27 orthogonal array. Material removal rate (MRR), surface roughness (SR), and form/orientation error are deemed as output characteristics. Taguchi’s analysis was engaged to discover the best process factors. ANOVA is used to examine the influence of process variables. Suitable application of artificial intelligence tools for making effective decision assists the manufacturer in accomplishing the benefits in numerous engineering domains. To obtain the maximum material removal and minimum roughness, circularity (circ), and perpendicularity errors (perp), the process variables have been optimized with the help of grey-ANFIS-amalgamated with Jaya algorithm. The multiperformance index was developed using grey theory. Statistical error analysis is used to estimate the performance of the established optimization model. Based on the investigative outcomes, the best-suited process variable combinations will be used to provide improved and enhanced multiperformance characteristics.

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Section: Introductionmentioning

confidence: 99%