A study on the μwire-EDM of Ni55.8Ti shape memory superalloy: an experimental investigation and a hybrid ANN/PSO approach for optimization

Akar, Samet; Seyedzavvar, Mirsadegh; Boğa, Cem

doi:10.1007/s40430-023-04100-5

Cited by 5 publications

(1 citation statement)

References 29 publications

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“…Since then, the concept of the ANN has been further developed and re ned, with the introduction of the back-propagation algorithm in 1986 [16] This algorithm has proven to be a powerful tool for minimizing error, and has been widely used in many elds, including pattern recognition and industrial processes. In recent years, many authors, including Coit et al [17], Haykin [18], and Al-Ahmari [19], Sable et al [20], Akar et al [21] have chosen to utilize ANN to solve a variety of problems. One reason is that the ANN can learn through examples, making it an effective tool for generalizing the network for unseen data.…”

Section: Introductionmentioning

confidence: 99%

An analysis to enhance the machining performance of micro-EDM for drilling of blind micro-hole using ANN

Pratap,

Upadhyay,

Sharma

et al. 2023

Int J Adv Manuf Technol

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The high demand for compact and multitasking devices in the market has been a driving force behind the growing interest in microfabrication techniques. These techniques have wide-ranging applications in many industries, including aerospace, automobile, electronics, and defense. Micro electrical discharge machining (µEDM) techniques have the unique ability to produce highly precise and intricate features on small components, which has further fueled the demand for such products. However, with the increasing demand for micro-featured products, there is a pressing need to enhance the process capability of µEDM process. This work aims to address this need by focusing on enhancing the performance of µEDM by varied process parameters and materials such as copper, brass, and tungsten carbide for the drilling of blind micro holes. Surface roughness (SR) and material removal rate (MRR) are the main performance factors taken into account in this investigation. Notably, the minimum SR was achieved on tungsten carbide, while the maximum MRR was achieved using copper electrodes. For SR and MRR, arti cial neural network (ANN) models have been constructed that predict with more than 90% accuracy. These ndings have signi cant implications for the future of microfabrication using µEDM.

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