Efficiency analysis of mechanical reducer equipment of material handling industry using Sunflower Optimization Algorithm and Material Generation Algorithm

Jena, Sourav; Jeet, Siddharth; Bagal, Dilip Kumar; Baliarsingh, Asini Kumar; Nayak, Dillip Ranjan; Barua, Abhishek

doi:10.1016/j.matpr.2021.08.005

Cited by 5 publications

(1 citation statement)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…where 𝜆 is the perpetual value which defines a "inertial" dislocation of the plants, 𝑃 𝑖 (‖𝑋 𝑖 − 𝑋 𝑖−1 ‖) is the possibility of crosspollination [29]. The max.…”

Section: Sunflower Optimizationmentioning

confidence: 99%

Parametric Appraisal of Electrochemical Machining of AISI 4140 Chromoly steel using Hybrid Taguchi - WASPAS - Sunflower optimization algorithm

Barua,

Mishra,

Das

et al. 2023

E3S Web Conf.

View full text Add to dashboard Cite

Electrochemical machining (ECM) is a significant technique for getting rid of metal that employs anodic dissolution to get complex contours and deep, precise holes, mostly in the components used in automotive or aerospace sectors. To achieve such high surface characteristics, the selection of factors is important. This work deals with the ECM of AISI 4140 Chromoly steel to investigate the surface roughness and material removal rate (MRR) on the machined specimen using a copper tool electrode. Factors like voltage, signal, and feed rate were optimized by hybrid optimization techniques. To acquire optimal factor configurations, the Taguchi-based WASPAS approach was utilised, accompanied by the Sunflower optimisation methodology. ANOVA was then used to determine the component that was the most impactful factor. A confirmation test is used to signify the outcomes of electrochemical machining. It was revealed that feed rate was among the most significantly relevant factors in affecting surface roughness and MRR. Also, all the optimization approaches provided similar predictions and agreed with the results fetched by the previous research.

show abstract

Section: Sunflower Optimizationmentioning

confidence: 99%